# **GPU-Enabled Container Hosting Options for AI** **Workloads**

Large AI models (like LLMs) demand powerful GPUs with substantial VRAM. Below, we compare cloud


providers that let you run **containerized AI workloads** on GPUs (≥24 GB VRAM) **without managing full**
**VM instances** . These services focus on Docker/Kubernetes-based deployment or serverless containers,
sparing you the hassle of provisioning and maintaining GPU VMs. We highlight each provider’s GPU
offerings, pricing, deployment model, integration ease, and key pros/cons.

## **Comparison Overview**


To facilitate quick scanning, the table below summarizes the key features of each provider’s GPU container

services:























1


2


3


4


5


6


**Notes:** Pricing above is on-demand indicative rates as of 2025 and may vary by region. All providers support
Docker containers; GPU memory listed is per GPU.


Below we provide more detail on each provider and service, including deployment experience and

limitations.

## **Major Cloud Providers (Hyperscalers)**


**Amazon Web Services (AWS)**


**Relevant services:** _Amazon ECS_ (Elastic Container Service) and _Amazon EKS_ (Kubernetes) with GPU-backed

EC2 instances, and _Amazon SageMaker_ for managed ML deployments.


7


AWS lets you run Docker containers on GPU EC2 instances, but **lacks a serverless GPU container service**
as of late 2025. AWS Fargate (serverless ECS) **does not support GPUs** [2](https://spot.io/resources/aws-fargate/aws-fargate-explained-pros-cons-components-features/#:~:text=and%20the%20hard%20limit%20is,information%20in%20the%20pod%20spec), and similarly AWS App Runner
has **no GPU option** (these platforms are CPU-only) [26](https://rayn.group/understanding-aws-app-runner/#:~:text=AWS%20App%20Runner%20is%20a,only%20available%20in%20the) . This means you must provision or autoscale GPU
EC2 instances yourself (though the containers abstract the software environment).



[2](https://spot.io/resources/aws-fargate/aws-fargate-explained-pros-cons-components-features/#:~:text=and%20the%20hard%20limit%20is,information%20in%20the%20pod%20spec)



[26](https://rayn.group/understanding-aws-app-runner/#:~:text=AWS%20App%20Runner%20is%20a,only%20available%20in%20the)




- **ECS on EC2:** You can register EC2 instances with GPUs (e.g., P3, P4, G4dn, G5 instances) into an ECS

cluster. Task definitions allow requesting GPU resources which ECS will schedule onto those



instances [27](https://docs.aws.amazon.com/AmazonECS/latest/developerguide/ecs-gpu-specifying.html#:~:text=Specifying%20GPUs%20in%20an%20Amazon,The%20number%20of) . _Example:_ to meet ≥24 GB VRAM, you might use a G5 instance (NVIDIA A10G GPU with
24 GB) or P4d (8× A100 40 GB). You manage the cluster autoscaling or use EC2 Auto Scaling Groups
to handle instance provisioning. No need to manage containers manually – ECS pulls your Docker
image and runs it – but you **do manage the underlying VM fleet** (or use AWS Batch/EKS to manage
it for you).
**EKS (Kubernetes):** Similar to ECS, you can attach GPU node groups to an EKS cluster. Kubernetes
supports scheduling pods with GPU resource requests; the node group’s EC2 instances provide the
physical GPUs. There is _no_ Fargate GPU support for EKS either [2](https://spot.io/resources/aws-fargate/aws-fargate-explained-pros-cons-components-features/#:~:text=and%20the%20hard%20limit%20is,information%20in%20the%20pod%20spec), so GPU workloads on EKS also
require EC2-based nodes. If using EKS **Autopilot** (Google’s GKE-like managed mode), note that AWS
does not have an Autopilot equivalent – you still manage node groups in AWS.
**Amazon SageMaker:** SageMaker is an ML-focused service that can deploy models to HTTPS
endpoints. It supports GPU instance types (you specify instance type per endpoint, e.g.,



[27](https://docs.aws.amazon.com/AmazonECS/latest/developerguide/ecs-gpu-specifying.html#:~:text=Specifying%20GPUs%20in%20an%20Amazon,The%20number%20of)







[2](https://spot.io/resources/aws-fargate/aws-fargate-explained-pros-cons-components-features/#:~:text=and%20the%20hard%20limit%20is,information%20in%20the%20pod%20spec)







`ml.g5.xlarge` for an A10G 24 GB GPU, or `ml.p3.2xlarge` for a V100 16 GB, etc). SageMaker



essentially manages an EC2 instance behind the scenes to host your model. You can bring your own
Docker container with an inference server, so this meets the “containerized” requirement. The
advantage is you don’t directly manage the EC2 lifecycle – SageMaker handles provisioning and
scaling (you can set min/max instance count for auto-scaling). However, **SageMaker Serverless**
**Inference does** _**not**_ support GPU instances [28](https://repost.aws/questions/QUdIP4nsQeRF6JA1e6aw0W9g/how-can-i-run-sagemaker-serverless-inference-on-a-gpu-instance#:~:text=How%20can%20I%20run%20SageMaker,of%20the%20serverless%20endpoints), so you must use the standard (provisioned) mode
for GPU. This is more _PaaS-like_ (less DevOps overhead) than running raw ECS/EKS, though pricing is
essentially the EC2 cost plus a small surcharge.
**Instance/GPU options:** AWS offers a wide range of GPU instance families. For instance, G5 instances
(NVIDIA A10G 24 GB) are a cost-effective choice for 24 GB needs; P3/P4 instances offer V100 (16–
32 GB) or A100 (40 GB) GPUs for larger models. Newer H100 instances (e.g., P5) are emerging (80 GB
GPU) but in limited regions. Whatever the instance, it can be consumed via ECS tasks or EKS pods.
**Pricing:** On-demand prices on AWS are relatively high. As a point of reference, a single A100 40 GB in
AWS on-demand is ~$3.50–$4.00/hour [29](https://www.fluence.network/blog/best-cloud-gpu-providers-ai-2025/#:~:text=Google%20Cloud%20A100%2C%20H100%2C%20L4%2C,second%20billing%2C%20Secure%20and) . AWS often charges a premium for GPU instances
compared to specialized providers. You can reduce costs via spot instances or savings plans, but that
adds management complexity.
**Deployment and integration:** If you already use Docker, deploying to ECS or SageMaker is
straightforward (AWS CLI or console). _Ease of use varies:_ ECS on EC2 requires networking and IAM
setup, but once configured, deploying new versions is just updating task definitions. SageMaker
endpoints can be created with a few API calls or through the AWS SDK (with your container stored in
ECR). Integration with AWS CI/CD and other services (CloudWatch logging, IAM roles, etc.) is robust.
**Pros/Cons:** AWS’s strength is in flexibility and ecosystem. You get full control of environments, ability
to use any GPU model AWS offers, and tight security/control (VPC, IAM). On the downside, **AWS has**
**no “fully-managed” GPU container service** – you either manage the nodes or let SageMaker
manage one per model. This can be overkill for simple scenarios and less agile compared to true
serverless GPU platforms. Cost is another con; AWS rates are the highest in our list [29](https://www.fluence.network/blog/best-cloud-gpu-providers-ai-2025/#:~:text=Google%20Cloud%20A100%2C%20H100%2C%20L4%2C,second%20billing%2C%20Secure%20and), though
savings are possible with commitments or spot instances.



[28](https://repost.aws/questions/QUdIP4nsQeRF6JA1e6aw0W9g/how-can-i-run-sagemaker-serverless-inference-on-a-gpu-instance#:~:text=How%20can%20I%20run%20SageMaker,of%20the%20serverless%20endpoints)











[29](https://www.fluence.network/blog/best-cloud-gpu-providers-ai-2025/#:~:text=Google%20Cloud%20A100%2C%20H100%2C%20L4%2C,second%20billing%2C%20Secure%20and)











[29](https://www.fluence.network/blog/best-cloud-gpu-providers-ai-2025/#:~:text=Google%20Cloud%20A100%2C%20H100%2C%20L4%2C,second%20billing%2C%20Secure%20and)



8


**Bottom line:** If you need deep integration with AWS or custom GPU setups (multiple GPUs, specific instance
types), AWS provides the building blocks but not a single turnkey solution. You’ll be trading some
convenience for control. Many users wishing to _“just run a container on a big GPU for my LLM”_ might find AWS
requires more heavy lifting than newer platforms.


**Google Cloud Platform (GCP)**


**Relevant services:** _Cloud Run (fully managed)_, _Google Kubernetes Engine (GKE Autopilot)_, and _Vertex AI_ for
model serving.


Google Cloud has invested in **serverless GPUs** in 2025. The flagship offering here is **Cloud Run** with GPU

support:



**Cloud Run (Managed)** : Cloud Run is GCP’s serverless container platform (you deploy a Docker
image, and Google handles scaling it as requests come in). As of June 2025, Cloud Run supports
attaching **one NVIDIA L4 GPU (24 GB)** to each container instance [3](https://www.infoq.com/news/2024/12/azure-container-apps-gpu/#:~:text=Replicate%2C%20Baseten%2C%20Koyeb%20and%20Fal,time%20AI%20inferencing) . This is ideal for containerized
AI inference workloads. There’s **no cluster to manage** – you simply specify `--gpu=1` when







[3](https://www.infoq.com/news/2024/12/azure-container-apps-gpu/#:~:text=Replicate%2C%20Baseten%2C%20Koyeb%20and%20Fal,time%20AI%20inferencing)



deploying your service, and Cloud Run provisions an instance with an L4 GPU on demand [30](https://cloud.google.com/blog/products/serverless/cloud-run-gpus-are-now-generally-available#:~:text=AI%20inference%20for%20everyone) . It
automatically scales out to multiple instances under load (each with its own GPU) and scales down to
zero when idle [5](https://cloud.google.com/blog/products/serverless/cloud-run-gpus-are-now-generally-available#:~:text=Now%2C%20you%20can%20enjoy%20the,across%20both%20GPUs%20and%20CPUs), meaning you don’t pay for idle time. Cloud Run’s cold-start times for GPU
instances are impressively low (~5 seconds to spin up a GPU container, plus whatever your app
initialization needs) [31](https://cloud.google.com/blog/products/serverless/cloud-run-gpus-are-now-generally-available#:~:text=changer%20for%20sporadic%20or%20unpredictable,workloads) . Google optimized the platform to quickly allocate a GPU and have NVIDIA
drivers ready to go. Full support for streaming responses (useful for LLM token streaming) is built-in


[32](https://cloud.google.com/blog/products/serverless/cloud-run-gpus-are-now-generally-available#:~:text=,time%2C%20and%20running%20the%20inference) [33](https://cloud.google.com/blog/products/serverless/cloud-run-gpus-are-now-generally-available#:~:text=,users%20as%20they%20are%20generated) .


**Ease of deployment:** Deploying to Cloud Run is extremely simple. For example, to run [Ollama](https://ollama.com) (an
LLM runtime) on Cloud Run across multiple regions, you can use a one-liner gcloud command [6](https://cloud.google.com/blog/products/serverless/cloud-run-gpus-are-now-generally-available#:~:text=India%29%2C%20with%20more%20to%20come) .

Google Cloud’s blog demonstrated this exact use-case: _“gcloud run deploy my-llm --image ollama/_
_ollama --port 11434 --gpu 1 --regions us-central1, europe-west1, ...”_ [6](https://cloud.google.com/blog/products/serverless/cloud-run-gpus-are-now-generally-available#:~:text=India%29%2C%20with%20more%20to%20come) . Cloud Run will pull the
container image and start serving traffic on HTTPS with a scalable backend. No need to configure
load balancers or VM specifics – it’s all managed.
**Limitations:** Currently, Cloud Run allows at most **1 GPU per container** [4](https://docs.cloud.google.com/run/docs/configuring/services/gpu#:~:text=Documentation%20docs,current%20NVIDIA%20driver%20version%3A) (so you can’t, say, use 2
GPUs in one container for larger training jobs – Cloud Run is primarily for stateless services). The
GPU type is fixed to NVIDIA L4 for now. L4 is a powerful GPU optimized for inference (approximately
22.7 TFLOPs FP16, 24 GB VRAM), suitable for medium-large models. If you need A100 or H100 level
compute, or multi-GPU, you’d have to use GKE or Vertex AI instead. Also, there is a concurrency
consideration: typically one request will occupy the GPU, so you may want to set Cloud Run
concurrency to 1 for inference to avoid multiple requests contending for the single GPU.
**Pricing:** Google charges per second of GPU use. While official docs show price as $- (dependent on
region), a ballpark figure is around **$0.00015 per second** for L4 in us-central (roughly $0.54/hr) based
on Google’s price calculator. You also pay for CPU/memory of the instance (e.g., an 8 vCPU + 32 GB
RAM Cloud Run instance might be ~$0.40/hr when active, plus GPU cost on top). Importantly, when
instances scale to zero, you’re not billed for the GPU at all during idle periods [5](https://cloud.google.com/blog/products/serverless/cloud-run-gpus-are-now-generally-available#:~:text=Now%2C%20you%20can%20enjoy%20the,across%20both%20GPUs%20and%20CPUs) .
**GKE Autopilot:** If you require other GPU types (e.g., A100) or need to run batch jobs/training that
don’t fit the request/response model of Cloud Run, Google’s Kubernetes offering (GKE) has an
Autopilot mode that manages nodes for you. GKE Autopilot **does support GPUs** – as of 2025 it
supports T4 and A100 in preview, and likely A100/A2/B100 series in GA [34](https://docs.cloud.google.com/kubernetes-engine/docs/how-to/autopilot-gpus#:~:text=Request%20and%20deploy%20GPU%20workloads,B200%2C%20H200%2C%20H100%2C%20and%20A100) [35](https://cloud.google.com/blog/products/containers-kubernetes/run-gpu-workloads-on-gke-autopilot#:~:text=To%20enable%20such%20workloads%20on,you%20can%20run%20ML) . In Autopilot, you just
declare a pod with, say, `nvidia.com/gpu: 1` and the type (A100 or T4), and GKE will provision the



[30](https://cloud.google.com/blog/products/serverless/cloud-run-gpus-are-now-generally-available#:~:text=AI%20inference%20for%20everyone)



[5](https://cloud.google.com/blog/products/serverless/cloud-run-gpus-are-now-generally-available#:~:text=Now%2C%20you%20can%20enjoy%20the,across%20both%20GPUs%20and%20CPUs)



[31](https://cloud.google.com/blog/products/serverless/cloud-run-gpus-are-now-generally-available#:~:text=changer%20for%20sporadic%20or%20unpredictable,workloads)



[32](https://cloud.google.com/blog/products/serverless/cloud-run-gpus-are-now-generally-available#:~:text=,time%2C%20and%20running%20the%20inference) [33](https://cloud.google.com/blog/products/serverless/cloud-run-gpus-are-now-generally-available#:~:text=,users%20as%20they%20are%20generated)









[6](https://cloud.google.com/blog/products/serverless/cloud-run-gpus-are-now-generally-available#:~:text=India%29%2C%20with%20more%20to%20come)




- **Limitations:** [4](https://docs.cloud.google.com/run/docs/configuring/services/gpu#:~:text=Documentation%20docs,current%20NVIDIA%20driver%20version%3A)







[5](https://cloud.google.com/blog/products/serverless/cloud-run-gpus-are-now-generally-available#:~:text=Now%2C%20you%20can%20enjoy%20the,across%20both%20GPUs%20and%20CPUs)







[34](https://docs.cloud.google.com/kubernetes-engine/docs/how-to/autopilot-gpus#:~:text=Request%20and%20deploy%20GPU%20workloads,B200%2C%20H200%2C%20H100%2C%20and%20A100) [35](https://cloud.google.com/blog/products/containers-kubernetes/run-gpu-workloads-on-gke-autopilot#:~:text=To%20enable%20such%20workloads%20on,you%20can%20run%20ML)



9


node behind the scenes. This is a good compromise if you need more control (K8s features) but don’t
want to directly handle GCE VM management. The trade-off: Autopilot won’t scale to zero; you pay
for the node while it’s allocated (even if your pod is idle), and startup times are longer (minutes to

add a node).

**Vertex AI / AI Platform:** Google’s managed ML serving (Vertex Prediction) lets you deploy models
(including custom Docker images) to hosted endpoints with GPUs. It’s analogous to SageMaker. You
can choose instance types like n1-standard with T4 or A100, etc. Vertex handles autoscaling and
offers features like request batching. However, Vertex may require more setup (you need to upload
models or containers to their registry and use the Vertex UI or API). It’s very useful if you need
integration with model monitoring, experiments, etc., but if your use-case is just running a
containerized app, Cloud Run is usually simpler and now covers GPU needs for inference.
**Pros/Cons:** GCP’s main **pro** is the ease of Cloud Run with GPUs – it’s _truly serverless_ and developerfriendly. Integrating into CI/CD (Cloud Build or GitHub actions) is straightforward, and you get the


[36](https://cloud.google.com/blog/products/serverless/cloud-run-gpus-are-now-generally-available#:~:text=Multi)

benefits of Google’s fast global network and regional redundancy . Another pro is cost-efficiency
for spiky workloads: scale-to-zero and per-second billing mean you pay only for actual usage [5](https://cloud.google.com/blog/products/serverless/cloud-run-gpus-are-now-generally-available#:~:text=Now%2C%20you%20can%20enjoy%20the,across%20both%20GPUs%20and%20CPUs) . On
the **con** side, Cloud Run’s limitation to L4 GPU and one GPU per container might not cover the
absolute high-end needs (no 80 GB GPUs yet on Cloud Run). Also, Cloud Run has a maximum
memory of 32 GiB per instance currently – which could be a limitation if you need a huge CPU RAM
for preprocessing alongside the GPU.











[36](https://cloud.google.com/blog/products/serverless/cloud-run-gpus-are-now-generally-available#:~:text=Multi)



[5](https://cloud.google.com/blog/products/serverless/cloud-run-gpus-are-now-generally-available#:~:text=Now%2C%20you%20can%20enjoy%20the,across%20both%20GPUs%20and%20CPUs)



**Bottom line:** For serving LLMs or other AI models behind an API, **Cloud Run with L4 GPUs is one of the**


**most convenient options available** . It hits the ≥24 GB VRAM mark and avoids virtually all ops overhead. If
you outgrow the L4 or need multi-GPU, Google offers stepping stones (Autopilot, Vertex) but those revert to
more traditional management.


**Microsoft Azure**


**Relevant services:** _Azure Container Apps_ (with **Serverless GPU** feature), _Azure Kubernetes Service (AKS)_, and
_Azure Machine Learning_ endpoints.


Azure’s most exciting offering for this use-case is **Azure Container Apps with Serverless GPUs**, which
became generally available in March 2025 [37](https://techcommunity.microsoft.com/blog/appsonazureblog/announcing-ga-for-azure-container-apps-serverless-gpus/4394302#:~:text=Image%3A%20Icon%20for%20Microsoft%20rankMicrosoft) [8](https://techcommunity.microsoft.com/blog/appsonazureblog/announcing-ga-for-azure-container-apps-serverless-gpus/4394302#:~:text=Key%20benefits%20of%20serverless%20GPUs) . This service is Azure’s equivalent to Cloud Run or Cloud
Foundry, now augmented with GPU support:



**Azure Container Apps (ACA) – Serverless GPUs:** Container Apps is a fully managed container
runtime where you deploy a container image and Azure handles running it, scaling it, etc. With the
GPU feature enabled, Azure will schedule your container onto servers with NVIDIA GPUs. Supported
GPU types are **NVIDIA T4 (16 GB)** and **NVIDIA A100 (40 GB)** in the initial GA [8](https://techcommunity.microsoft.com/blog/appsonazureblog/announcing-ga-for-azure-container-apps-serverless-gpus/4394302#:~:text=Key%20benefits%20of%20serverless%20GPUs) . A100 40 GB meets
the 24 GB+ criterion (and offers ample compute for heavy AI tasks), whereas T4 16 GB might be
insufficient for larger LLMs (but fine for smaller models or other AI tasks).
**Usage model:** You can enable GPU for a Container App by simply ticking a box or adding a
parameter in the CLI when creating the app [38](https://techcommunity.microsoft.com/blog/appsonazureblog/announcing-ga-for-azure-container-apps-serverless-gpus/4394302#:~:text=From%20the%20portal%2C%20you%20can,Container%20App%C2%A0or%C2%A0your%20Container%20App%20Job) . The app can then scale out to multiple GPUpowered instances on demand. Notably, ACA supports **scale to zero** for GPU apps as well [9](https://techcommunity.microsoft.com/blog/appsonazureblog/announcing-ga-for-azure-container-apps-serverless-gpus/4394302#:~:text=%2A%20Scale,applications%20alongside%20your%20existing%20apps),
similar to Cloud Run – meaning if your container is idle (no incoming requests), it can deallocate the
GPU and stop billing. It also supports per-second billing for GPU usage [9](https://techcommunity.microsoft.com/blog/appsonazureblog/announcing-ga-for-azure-container-apps-serverless-gpus/4394302#:~:text=%2A%20Scale,applications%20alongside%20your%20existing%20apps) .
**Scenarios:** Azure positions this for real-time inference (e.g., custom model endpoints) and even
ephemeral GPU interactive sessions (like on-demand GPU notebooks) [39](https://techcommunity.microsoft.com/blog/appsonazureblog/announcing-ga-for-azure-container-apps-serverless-gpus/4394302#:~:text=Serverless%20GPUs%20accelerate%20the%20speed,which%20to%20build%20your%20applications) [40](https://techcommunity.microsoft.com/blog/appsonazureblog/announcing-ga-for-azure-container-apps-serverless-gpus/4394302#:~:text=NVIDIA%20T4) . Essentially it’s a







[8](https://techcommunity.microsoft.com/blog/appsonazureblog/announcing-ga-for-azure-container-apps-serverless-gpus/4394302#:~:text=Key%20benefits%20of%20serverless%20GPUs)







[38](https://techcommunity.microsoft.com/blog/appsonazureblog/announcing-ga-for-azure-container-apps-serverless-gpus/4394302#:~:text=From%20the%20portal%2C%20you%20can,Container%20App%C2%A0or%C2%A0your%20Container%20App%20Job)



[9](https://techcommunity.microsoft.com/blog/appsonazureblog/announcing-ga-for-azure-container-apps-serverless-gpus/4394302#:~:text=%2A%20Scale,applications%20alongside%20your%20existing%20apps)



[9](https://techcommunity.microsoft.com/blog/appsonazureblog/announcing-ga-for-azure-container-apps-serverless-gpus/4394302#:~:text=%2A%20Scale,applications%20alongside%20your%20existing%20apps)







[39](https://techcommunity.microsoft.com/blog/appsonazureblog/announcing-ga-for-azure-container-apps-serverless-gpus/4394302#:~:text=Serverless%20GPUs%20accelerate%20the%20speed,which%20to%20build%20your%20applications) [40](https://techcommunity.microsoft.com/blog/appsonazureblog/announcing-ga-for-azure-container-apps-serverless-gpus/4394302#:~:text=NVIDIA%20T4)



10


flexible middle-ground: instead of using Azure’s prebuilt “AI Service” APIs, you bring your own model

in a container and run it on serverless GPU infrastructure.
**Integration with dev workflow:** Container Apps integrates with GitHub Actions and Azure
Container Registry for CI/CD. You can, for example, deploy an app directly from a GitHub repo (Azure
will build the container) or push a built image. From a developer standpoint, once your Docker
image is ready (say an API that serves an LLM), deploying to ACA is straightforward. Azure provides a
web UI and CLI; plus features like revision management and custom domains for endpoints.
**Performance:** A point to note from Azure’s documentation – provisioning a container app with a
GPU can initially take several minutes (since behind the scenes Azure is allocating a VM with GPUs,
installing NVIDIA drivers, etc.) [12](https://learn.microsoft.com/en-us/azure/container-instances/container-instances-gpu#:~:text=Things%20to%20know) . They mention 8–10 minutes cold start in preview [12](https://learn.microsoft.com/en-us/azure/container-instances/container-instances-gpu#:~:text=Things%20to%20know) . However,
once running, scaling additional instances is faster. Also, Azure pre-installs CUDA drivers on the GPU
nodes, supporting CUDA up to 11.x out of the box [41](https://learn.microsoft.com/en-us/azure/container-instances/container-instances-gpu#:~:text=See%20pricing%20details) (so common base images like `nvidia/cuda`











[12](https://learn.microsoft.com/en-us/azure/container-instances/container-instances-gpu#:~:text=Things%20to%20know) [12](https://learn.microsoft.com/en-us/azure/container-instances/container-instances-gpu#:~:text=Things%20to%20know)



[41](https://learn.microsoft.com/en-us/azure/container-instances/container-instances-gpu#:~:text=See%20pricing%20details)



or `tensorflow:gpu` work without special configuration).



**Pricing:** Azure bills GPU container instances per second of use. In GA, enterprise customers get
default quotas for A100 and T4 GPUs [42](https://techcommunity.microsoft.com/blog/appsonazureblog/announcing-ga-for-azure-container-apps-serverless-gpus/4394302#:~:text=Quota%20changes%20for%20GA) . While the exact rates depend on region, Azure’s pricing
page indicates an **A100 (NC4as) around $0.000651 per second** (roughly $2.34/hr) and **T4 (NCas)**
**around $0.000540 per second** (~$1.94/hr) in West US 3, with discounts on 1-year or 3-year plans [10](https://azure.microsoft.com/en-gb/pricing/details/container-apps/#:~:text=~17,shown%20above%20are%20in) .
These prices are significantly lower than AWS on-demand for similar GPUs, likely because Azure’s
serverless GPU charges for _active use only_ (no charge when scaled to zero) and possibly intro pricing.
Do note you also incur CPU/memory costs for the container instances (a few cents per vCPU-hour)
plus a nominal request fee, similar to Azure Functions pricing model [43](https://azure.microsoft.com/en-gb/pricing/details/container-apps/#:~:text=Metre%20Pay%20as%20you%20go,year%20Savings%20Plan%20Price) [44](https://azure.microsoft.com/en-gb/pricing/details/container-apps/#:~:text=Container%20Apps%20are%20billed%20based,are%20included%20free%20each%20month) .
**Limitations:** As of GA, serverless GPU in ACA was in only **3 regions** (West US 3, Australia East,
Sweden Central) [11](https://techcommunity.microsoft.com/blog/appsonazureblog/announcing-ga-for-azure-container-apps-serverless-gpus/4394302#:~:text=With%20GA%2C%20we%20are%20introducing,for%20A100%20and%20T4%20GPUs), with more regions forthcoming. Also, currently Linux containers only, and
certain networking options (like VNet injection) might not support GPU apps yet (since initially in
preview they disallowed VNet with GPU) [45](https://learn.microsoft.com/en-us/azure/container-instances/container-instances-gpu#:~:text=Additional%20limitations%3A%20GPU%20resources%20can%27t,group%20into%20a%20virtual%20network) .
**AKS & Azure Batch:** If needed, Azure’s Kubernetes (AKS) can be used with GPU node pools (e.g.,
NDv4 VMs for A100s). Azure doesn’t have an “Autopilot” mode for AKS equivalent to GKE’s, but they
do offer features like virtual node integration (with ACI) – however, as noted, the previous ACI GPU
preview was retired in mid-2025 [46](https://learn.microsoft.com/en-us/azure/container-instances/container-instances-gpu#:~:text=Important) likely in favor of Container Apps GPU. For batch processing,
Azure Batch AI can allocate GPU VMs for container workloads, but that’s more for offline jobs rather
than serving.
**Azure ML Endpoints:** Similar to SageMaker/Vertex, Azure Machine Learning has “Managed Online

Endpoints” where you can deploy models on GPU compute clusters. It supports custom Docker
images as well (with ONNX or Triton etc.), and you can choose VM sizes like Standard_NC6 (1× Tesla
K80) or ND40 (A100). The service provides scaling, versioning, and monitoring. This is an option if
you are already using Azure ML for training and want a one-click deploy. Otherwise, Container Apps
is simpler for pure container workloads.
**Pros/Cons:** Pros: Azure’s serverless GPU offering brings **A100 GPUs (40 GB) to a truly PaaS**
**environment**, which is a big plus for memory-hungry models. The pay-per-use model could save
cost for intermittent workloads. It’s also integrated with NVIDIA’s NGC (they mention support for
NVIDIA’s NIM microservices for AI) [47](https://techcommunity.microsoft.com/blog/appsonazureblog/announcing-ga-for-azure-container-apps-serverless-gpus/4394302#:~:text=This%20GA%20release%20of%20Serverless,standard%20APIs) [48](https://techcommunity.microsoft.com/blog/appsonazureblog/announcing-ga-for-azure-container-apps-serverless-gpus/4394302#:~:text=Serverless%20GPUs%20now%20support%20NVIDIA,endpoints%20on%20Azure%20Container%20Apps), indicating tight collaboration with NVIDIA for optimized
deployment. Cons: It’s new and not as widely battle-tested as AWS or even Cloud Run; region
coverage and certain feature support might lag. Also, Azure’s developer experience historically can
be a bit complex (networking, resource groups, etc.), so there may be a learning curve. But if you’re
already in Azure ecosystem, it’s quite compelling.







[42](https://techcommunity.microsoft.com/blog/appsonazureblog/announcing-ga-for-azure-container-apps-serverless-gpus/4394302#:~:text=Quota%20changes%20for%20GA)



[10](https://azure.microsoft.com/en-gb/pricing/details/container-apps/#:~:text=~17,shown%20above%20are%20in)



[43](https://azure.microsoft.com/en-gb/pricing/details/container-apps/#:~:text=Metre%20Pay%20as%20you%20go,year%20Savings%20Plan%20Price) [44](https://azure.microsoft.com/en-gb/pricing/details/container-apps/#:~:text=Container%20Apps%20are%20billed%20based,are%20included%20free%20each%20month)







[11](https://techcommunity.microsoft.com/blog/appsonazureblog/announcing-ga-for-azure-container-apps-serverless-gpus/4394302#:~:text=With%20GA%2C%20we%20are%20introducing,for%20A100%20and%20T4%20GPUs)



[45](https://learn.microsoft.com/en-us/azure/container-instances/container-instances-gpu#:~:text=Additional%20limitations%3A%20GPU%20resources%20can%27t,group%20into%20a%20virtual%20network)







[46](https://learn.microsoft.com/en-us/azure/container-instances/container-instances-gpu#:~:text=Important)











[47](https://techcommunity.microsoft.com/blog/appsonazureblog/announcing-ga-for-azure-container-apps-serverless-gpus/4394302#:~:text=This%20GA%20release%20of%20Serverless,standard%20APIs) [48](https://techcommunity.microsoft.com/blog/appsonazureblog/announcing-ga-for-azure-container-apps-serverless-gpus/4394302#:~:text=Serverless%20GPUs%20now%20support%20NVIDIA,endpoints%20on%20Azure%20Container%20Apps)



**Bottom line:** Azure Container Apps with GPUs is a strong option if you want A100-level horsepower without
managing VMs. It is especially attractive for enterprise users who need that 40 GB VRAM and are okay with


11


Azure’s current region limitations. For smaller workloads (or global deployment needs), you might consider
Cloud Run (L4) vs ACA (A100) based on model size and cloud preference.

## **Specialized GPU Cloud Platforms**


Beyond the big three, several specialized cloud providers focus on GPU hosting for AI. These often offer
**better price-per-performance** and a more developer-centric experience, at the cost of having a smaller
ecosystem or fewer managed services. They generally support containerized workflows (some via
Kubernetes, others via simpler interfaces). Below are notable ones:


**CoreWeave**



**Overview:** CoreWeave is a specialized GPU cloud provider often described as an “AI **hyperscaler** ” itself [49](https://www.runpod.io/articles/comparison/runpod-vs-coreweave-which-cloud-gpu-platform-is-best-for-ai-image-generation#:~:text=CoreWeave%2C%20founded%20in%202017%2C%20started,NeoX%20model) .
It started from crypto-mining roots but now provides a cloud tailored for AI/ML, visual effects, and other
GPU-heavy tasks. CoreWeave’s cloud is **Kubernetes-native** – when you use their service, you are essentially
running on a managed Kubernetes cluster optimized for GPUs [50](https://dgtlinfra.com/coreweave-data-center-locations/#:~:text=CoreWeave%3A%20Data%20Center%20Regions%2C%20Locations%2C,compute%2C%20CPU%20compute%2C%20containers%2C) .



[49](https://www.runpod.io/articles/comparison/runpod-vs-coreweave-which-cloud-gpu-platform-is-best-for-ai-image-generation#:~:text=CoreWeave%2C%20founded%20in%202017%2C%20started,NeoX%20model)



[50](https://dgtlinfra.com/coreweave-data-center-locations/#:~:text=CoreWeave%3A%20Data%20Center%20Regions%2C%20Locations%2C,compute%2C%20CPU%20compute%2C%20containers%2C)



**GPU hardware:** CoreWeave offers a broad selection of high-end NVIDIA GPUs, including some of the
latest models not immediately available on other clouds. For example, they provide **NVIDIA A40**

**(48 GB)**, **RTX A6000 (48 GB)**, **A100 (40 GB and 80 GB variants)**, and even NVIDIA H100 (80 GB) GPUs


[13](https://www.runpod.io/articles/comparison/runpod-vs-coreweave-which-cloud-gpu-platform-is-best-for-ai-image-generation#:~:text=receiving%20outputs.%20,GPU%20variety%20is%20slightly%20narrower)

. They also have specialized offerings like NVIDIA L40/L40S (Ada Lovelace datacenter GPUs with
>40 GB VRAM) and announced support for upcoming Blackwell GPUs (B200, etc.) [1](https://www.fluence.network/blog/best-cloud-gpu-providers-ai-2025/#:~:text=CoreWeave%20A100%2C%20H100%2C%20B200%2C%20L40S,configured%20environments) . CoreWeave
focuses on data-center grade GPUs (unlike some others, they don’t typically offer consumer GeForce
cards, sticking to pro cards with ECC memory and high reliability).
**Services and deployment:** There are a few ways to use CoreWeave:
**Managed Kubernetes:** You can get access to a Kubernetes cluster (or namespace) in which you can
deploy your containerized workloads. CoreWeave manages the control plane for free [51](https://www.coreweave.com/pricing#:~:text=Free) and you
pay only for the worker nodes (GPUs) you consume. They provide a friendly UI and CLI ( `cwctl` ) to


launch deployments if you’re not a Kubernetes expert, but essentially it’s like having a Kubernetes
where you can just specify “I need an A100” and it will schedule a pod with that resource. This is
great for both long-running services and batch jobs. Advanced features like multi-GPU training with
NVLink, or distributed training with InfiniBand interconnect, are supported (their clusters are built
for HPC).
**On-demand instances:** They also offer more traditional on-demand VMs or containers through their
API. But the preferred method is via the K8s service because it handles a lot (scheduling, container
runtime, etc.) for you.
**Pricing:** CoreWeave is known for aggressive pricing. According to one 2025 comparison, an A100
GPU-hour on CoreWeave was about **$2.21/hr** (likely for 40 GB A100) [1](https://www.fluence.network/blog/best-cloud-gpu-providers-ai-2025/#:~:text=CoreWeave%20A100%2C%20H100%2C%20B200%2C%20L40S,configured%20environments), versus ~$4/hr on Azure and
~$3.90 on GCP for similar GPUs [29](https://www.fluence.network/blog/best-cloud-gpu-providers-ai-2025/#:~:text=Google%20Cloud%20A100%2C%20H100%2C%20L4%2C,second%20billing%2C%20Secure%20and) . They claim up to **80% cost savings** vs hyperscalers by
specializing in GPU workloads [52](https://www.runpod.io/articles/comparison/runpod-vs-coreweave-which-cloud-gpu-platform-is-best-for-ai-image-generation#:~:text=on%20a%20highly%20configurable%20platform,training%20to%20visual%20effects%20rendering) . For example, CoreWeave’s public pricing (as of Sep 2025) lists

**A100 40GB** at ~$1.70/hr and **A100 80GB** at ~$2.30/hr on-demand, and even lower for reserved usage

[53](https://lambda.ai/pricing#:~:text=Lambda%20AI%20GPU%20Cloud%20pricing%3A,85%2C%20A100%2C%20GH200%29%2C) . They bill by the minute (or even second granularity on their Kubernetes service). There are no

upfront costs. Egress is free which is nice if you’re pulling large docker images or models [54](https://www.coreweave.com/pricing#:~:text=Data%20transfer%20within%20CoreWeave) .

**Pros:**

**Performance & scale:** CoreWeave’s infrastructure is designed for heavy workloads. If you need a
cluster of 8 or 16 A100s, they can provide that (they even cite training a 20B model on their cluster)

[52](https://www.runpod.io/articles/comparison/runpod-vs-coreweave-which-cloud-gpu-platform-is-best-for-ai-image-generation#:~:text=on%20a%20highly%20configurable%20platform,training%20to%20visual%20effects%20rendering) . They also have new hardware early (H100s were available early on CoreWeave).







[13](https://www.runpod.io/articles/comparison/runpod-vs-coreweave-which-cloud-gpu-platform-is-best-for-ai-image-generation#:~:text=receiving%20outputs.%20,GPU%20variety%20is%20slightly%20narrower)



[1](https://www.fluence.network/blog/best-cloud-gpu-providers-ai-2025/#:~:text=CoreWeave%20A100%2C%20H100%2C%20B200%2C%20L40S,configured%20environments)


















[1](https://www.fluence.network/blog/best-cloud-gpu-providers-ai-2025/#:~:text=CoreWeave%20A100%2C%20H100%2C%20B200%2C%20L40S,configured%20environments)







[53](https://lambda.ai/pricing#:~:text=Lambda%20AI%20GPU%20Cloud%20pricing%3A,85%2C%20A100%2C%20GH200%29%2C)



[54](https://www.coreweave.com/pricing#:~:text=Data%20transfer%20within%20CoreWeave)








[52](https://www.runpod.io/articles/comparison/runpod-vs-coreweave-which-cloud-gpu-platform-is-best-for-ai-image-generation#:~:text=on%20a%20highly%20configurable%20platform,training%20to%20visual%20effects%20rendering)



12


**Kubernetes flexibility:** You can run anything containerized, not just ML inference. If you want to run
a custom training job, a distributed Ray cluster, or even GPU-accelerated databases, you have full
control. It’s basically cloud with a GPU-optimized K8s as the interface.
**No-frills integration:** You’re not locked into some opinionated ML workflow; you use standard tools
(Docker, kubectl, etc.). They also provide support for _Slurm on Kubernetes_ for HPC users [55](https://www.coreweave.com/pricing#:~:text=) .
**Enterprise features:** CoreWeave offers things like private networking, volume storage, etc. It’s
aiming to be an alternative for businesses that might otherwise build their own GPU cluster.

**Cons:**

**K8s knowledge needed:** While they try to simplify things, a basic understanding of Kubernetes or
container orchestration is helpful to make the most of CoreWeave. Less savvy users might be
overwhelmed compared to a simple “deploy via web form” approach of others.
**Overhead of scheduling:** Spinning up a new pod on a free GPU node is quick (seconds), but if a new
node must be allocated, there might be a bit more latency than say Cloud Run’s super-fast scaling. A
comparison noted that CoreWeave (with its flexible scheduling) had _“a bit more overhead in spinning_
_up nodes or scheduling containers”_ for very bursty tasks [15](https://www.runpod.io/articles/comparison/runpod-vs-coreweave-which-cloud-gpu-platform-is-best-for-ai-image-generation#:~:text=Additionally%2C%20deployment%20speed%20is%20a,a%20slight%20edge%20in%20agility), whereas a platform like RunPod (tailored
to instant start) might be faster for one-off runs.
**Fewer high-level services:** Don’t expect things like managed data labeling or built-in model
monitoring – this is infrastructure-focused. You may need to handle more parts of the stack (which
could be a pro for some).
**Use case fit:** If you are comfortable with cloud infrastructure and need to run large jobs or serve
models cost-efficiently at scale, CoreWeave is excellent. For example, an AI startup could use
CoreWeave to deploy their model in a Kubernetes Deployment, set up HPA for scaling, and pay much
less than on AWS. You have fine-grained control over resources (exact CPU/Memory with the GPU in
each pod, etc.).











[55](https://www.coreweave.com/pricing#:~:text=)
















[15](https://www.runpod.io/articles/comparison/runpod-vs-coreweave-which-cloud-gpu-platform-is-best-for-ai-image-generation#:~:text=Additionally%2C%20deployment%20speed%20is%20a,a%20slight%20edge%20in%20agility)











**Bottom line:** CoreWeave delivers **GPU-as-a-service with the power of Kubernetes** . It’s like having your
own cloud GPU cluster without managing the hardware. The learning curve is higher than fully serverless
platforms, but the reward is high performance and cost savings, especially for consistent or large-scale


workloads (including those requiring ≥24 GB VRAM per GPU, which they handle with ease via A40/A100/

H100).


**RunPod**


**Overview:** RunPod is a fast-growing GPU cloud platform (launched in 2022) that focuses on **ease of use,**
**low cost, and flexibility** for AI developers [56](https://www.runpod.io/articles/comparison/runpod-vs-coreweave-which-cloud-gpu-platform-is-best-for-ai-image-generation#:~:text=Runpod%20is%20a%20newer%20entrant,complexity%20of%20traditional%20cloud%20setups) . It provides on-demand access to GPUs through a simple web
UI or API, abstracting away VMs in favor of a “pod” concept. In many ways, RunPod feels like a serverless
container service specifically for GPUs, with an emphasis on quick startups and interactive use.



**GPU hardware:** RunPod supports a _huge_ catalog of GPU models – in fact, one of its differentiators is
offering **consumer-grade GPUs** in addition to data-center GPUs [16](https://www.runpod.io/articles/comparison/runpod-vs-coreweave-which-cloud-gpu-platform-is-best-for-ai-image-generation#:~:text=,at%20default%20Stable%20Diffusion%20resolution) . For example:
You can spin up pods with an **NVIDIA RTX 3090 or 4090 (each 24 GB VRAM)**, which are popular for
tasks like Stable Diffusion due to their high performance/price ratio [16](https://www.runpod.io/articles/comparison/runpod-vs-coreweave-which-cloud-gpu-platform-is-best-for-ai-image-generation#:~:text=,at%20default%20Stable%20Diffusion%20resolution) . These are often cheaper per
hour than equivalent datacenter GPUs, albeit with slightly lower reliability.
They also offer data center GPUs including **A100 40/80 GB**, **H100 80 GB**, **RTX A6000 48 GB**, etc.
Essentially, from mid-range (RTX 3080s, etc.) to high-end (A100/H100), 30+ GPU types are available


[16](https://www.runpod.io/articles/comparison/runpod-vs-coreweave-which-cloud-gpu-platform-is-best-for-ai-image-generation#:~:text=,at%20default%20Stable%20Diffusion%20resolution)

. This breadth means you can choose a GPU that fits your exact VRAM and budget needs (e.g.,
use a 24 GB 3090 for a 13B model, or jump to an 80 GB A100 for a 70B model).







[16](https://www.runpod.io/articles/comparison/runpod-vs-coreweave-which-cloud-gpu-platform-is-best-for-ai-image-generation#:~:text=,at%20default%20Stable%20Diffusion%20resolution)







[16](https://www.runpod.io/articles/comparison/runpod-vs-coreweave-which-cloud-gpu-platform-is-best-for-ai-image-generation#:~:text=,at%20default%20Stable%20Diffusion%20resolution)







[16](https://www.runpod.io/articles/comparison/runpod-vs-coreweave-which-cloud-gpu-platform-is-best-for-ai-image-generation#:~:text=,at%20default%20Stable%20Diffusion%20resolution)



13


**Deployment model:** RunPod uses the term **“pods”** which indeed are containerized instances. When
you launch, you select:
A GPU type (and quantity, though most often 1 GPU per pod; multi-GPU might be possible in some

regions),
A container environment (you can choose from predefined Docker images – for example, an image
with PyTorch and CUDA, Jupyter, etc. – or supply your own image from Docker Hub),
and optional startup commands (like to launch Jupyter or SSH server). RunPod then provisions the
container on a suitable host with that GPU. You get a web terminal, Jupyter notebook access, or you
can expose ports to internet (for an API server). You have **root access** inside the container, so you
can install packages, download models, etc. It’s a lot like being on your own VM, but it’s actually
container-isolated – this gives very fast launch and teardown. Importantly, RunPod supports an API
for automation. For example, you can programmatically spin up a pod, run inference or training,
then spin it down – integrating into a pipeline.
**Serverless GPU and scale:** RunPod emphasizes _no idle cost_ . You can shut down pods when not in
use. They charge per-second, so even short experiments are cheap [57](https://www.runpod.io/articles/comparison/runpod-vs-coreweave-which-cloud-gpu-platform-is-best-for-ai-image-generation#:~:text=can%20spin%20up%20isolated%20GPU,complexity%20of%20traditional%20cloud%20setups) [58](https://www.runpod.io/articles/comparison/runpod-vs-coreweave-which-cloud-gpu-platform-is-best-for-ai-image-generation#:~:text=,on%20Runpod%E2%80%99s%20standard%20pricing%2C%20and) . They recently introduced
“Serverless Jobs” – a feature where you run a container to completion on a GPU and pay just for that
job’s duration (useful for batch inference or periodic jobs, without keeping a pod alive).
**Pricing:** RunPod’s pricing is one of the lowest:
For instance, an **A100 80GB** was quoted at **$1.19/hr** on their _Secure Cloud_ (their professionally
managed data centers) [58](https://www.runpod.io/articles/comparison/runpod-vs-coreweave-which-cloud-gpu-platform-is-best-for-ai-image-generation#:~:text=,on%20Runpod%E2%80%99s%20standard%20pricing%2C%20and) – compare that to ~$3/hr on AWS or ~$2.30/hr on CoreWeave. Even the
latest **H100 80GB** was ~$2.79/hr on RunPod [58](https://www.runpod.io/articles/comparison/runpod-vs-coreweave-which-cloud-gpu-platform-is-best-for-ai-image-generation#:~:text=,on%20Runpod%E2%80%99s%20standard%20pricing%2C%20and), which is extremely competitive.
RTX 4090 24 GB can be as low as ~$0.90/hr (varies by region/provider).
They have two tiers: **Secure Cloud** (data center, guaranteed uptime) and **Community Cloud**
(cheaper, using capacity from community providers – somewhat like a marketplace). Community
instances can be much cheaper (e.g., a 3090 might be $0.20–0.30/hr), but they can be preempted or
less consistent. Secure Cloud is still way cheaper than hyperscalers for comparable GPUs.
Billing is per second, so you can run something for 10 minutes and pay only for that fraction (great
for testing).

**Pros:**

**Ease of use:** As their comparison states, RunPod is _“developer-friendly – quick to start, pay-as-you-_
_go”_ [59](https://www.runpod.io/articles/comparison/runpod-vs-coreweave-which-cloud-gpu-platform-is-best-for-ai-image-generation#:~:text=In%20summary%2C%20Runpod%20positions%20itself,aspects%20for%20AI%20image%20generation) . Launching a pod is nearly instant (they use a technology called FlashBoot to cache images
and get containers up in seconds) [60](https://www.runpod.io/articles/comparison/runpod-vs-coreweave-which-cloud-gpu-platform-is-best-for-ai-image-generation#:~:text=performance%20with%20high,flexibility%20on%20the%20lower%20end) . The web UI is simple and geared toward ML (with quick
launch buttons for Jupyter, SSH, VSCode).
**Flexibility:** Because you have root in a container, you can do anything. It’s not limited to only serving
an endpoint or only training – you could run a web UI for Stable Diffusion, or a API server for an LLM,
or just use it as a remote workstation. They even support workflows like connecting pods to a
distributed training job.
**Scalability:** RunPod can scale out in parallel pods as well (though with a quota per account initially).
Some users use it to spin 5–10 pods to handle workloads in parallel, akin to a poor man’s cluster.
**No management overhead:** You don’t worry about drivers, CUDA versions (their base images take
care of that), or container orchestrators – it’s very plug-and-play.

**Cons:**

**Not a full platform:** It’s more like IaaS with a nice wrapper. You won’t get things like automated
scaling based on request rate (you’d have to script that via their API). Also no built-in load balancer –
but you can expose a service and handle traffic yourself.
**Session persistence:** Each pod has ephemeral storage (some local NVMe). You can attach a
persistent volume, but it’s not as seamless as a fully managed solution. If you terminate a pod, you























[57](https://www.runpod.io/articles/comparison/runpod-vs-coreweave-which-cloud-gpu-platform-is-best-for-ai-image-generation#:~:text=can%20spin%20up%20isolated%20GPU,complexity%20of%20traditional%20cloud%20setups) [58](https://www.runpod.io/articles/comparison/runpod-vs-coreweave-which-cloud-gpu-platform-is-best-for-ai-image-generation#:~:text=,on%20Runpod%E2%80%99s%20standard%20pricing%2C%20and)








[58](https://www.runpod.io/articles/comparison/runpod-vs-coreweave-which-cloud-gpu-platform-is-best-for-ai-image-generation#:~:text=,on%20Runpod%E2%80%99s%20standard%20pricing%2C%20and)



[58](https://www.runpod.io/articles/comparison/runpod-vs-coreweave-which-cloud-gpu-platform-is-best-for-ai-image-generation#:~:text=,on%20Runpod%E2%80%99s%20standard%20pricing%2C%20and)

















[59](https://www.runpod.io/articles/comparison/runpod-vs-coreweave-which-cloud-gpu-platform-is-best-for-ai-image-generation#:~:text=In%20summary%2C%20Runpod%20positions%20itself,aspects%20for%20AI%20image%20generation)



[60](https://www.runpod.io/articles/comparison/runpod-vs-coreweave-which-cloud-gpu-platform-is-best-for-ai-image-generation#:~:text=performance%20with%20high,flexibility%20on%20the%20lower%20end)
























14


need to save important data externally (they do have integrations to save model weights to cloud

storage, etc.).
**Community cloud variability:** If you use the cheapest community GPUs, you might encounter slight
reliability issues (like an instance occasionally rebooting). The Secure Cloud, however, is stable.
**Limited enterprise features:** Features like IAM roles, VPC integration into your corporate network,
etc., are not as mature as big providers. RunPod is mainly targeted at individual developers and
small teams, though they are expanding enterprise offerings.
**Use cases:** RunPod is excellent for **quick experiments, demo deployments, and bursty**
**workloads** . For example, if you have an occasional need to run an LLM inference for a user query,
you could keep a pod off and spin it up on demand via API, serve the request, then spin down –
paying maybe a few cents. It’s also popular in the AI community for running things like Stable
Diffusion or fine-tuning models without needing your own GPU hardware. Some even use it as a
backend for web apps (keeping a pod running as an API server, which still can be cheaper than

mainstream clouds).















**Bottom line:** RunPod offers **containerized GPUs “on tap”** with superb cost efficiency and ease. It’s like
having a cloud GPU workstation or microservice that you can start or stop at will in seconds. It fulfills the
requirement of containerized deployment (you provide a Docker image or use theirs) and provides plenty of
>=24 GB VRAM options (4090s, A5000s, A6000s, A100s, etc.). If ultra-low ops overhead and cost is your
priority – and you can work around the lighter feature set – RunPod is a top choice.


**Lambda Labs (Lambda Cloud)**


**Overview:** Lambda Labs is known for building specialized deep learning hardware (the Lambda
workstations and servers). They also run **Lambda Cloud**, an GPU cloud service primarily targeted at
researchers and startups. It’s essentially an alternative to AWS/GCP for renting GPU machines, with some
ML-friendly touches (like pre-configured environments).



**GPU hardware:** Lambda Cloud provides high-end GPUs like **A100 80GB**, **A100 40GB**, **H100 80GB**, and
even the NVIDIA **GH200 (Grace Hopper)** in early access [1](https://www.fluence.network/blog/best-cloud-gpu-providers-ai-2025/#:~:text=CoreWeave%20A100%2C%20H100%2C%20B200%2C%20L40S,configured%20environments) . They also have older cards (like V100,
1080 Ti etc. in limited supply) and sometimes new consumer GPUs through a marketplace. Their

focus is on the latest NVIDIA accelerators for AI. For our ≥24 GB criteria, Lambda certainly qualifies –
A100 and H100 are mainstays, and they’ve offered RTX 4090 (24GB) to some customers as well.

**Service model:** Lambda Cloud works a bit more like a traditional IaaS:

You **launch instances** (VMs) of a given type via their web dashboard or API. For example, a single
GPU A100 instance (which comes with a certain number of vCPUs and RAM) or multi-GPU server (like

8× A100).

The instances come with a fresh OS (Ubuntu) or you can use their DL Image (which has CUDA
drivers, PyTorch, etc., pre-installed). So you can SSH in and use it like any server.
They do have a web-based SSH and JupyterLab interface, making it easy to get started without
complex setup.
They recently added the ability to create Kubernetes clusters on top of Lambda Cloud resources for
those who want to orchestrate multiple instances.
**Containers:** While Lambda Cloud itself doesn’t abstract away the VM, you _can_ run Docker on the
VMs. Many users simply use conda or pip on the VM itself. If you prefer containerization, you could
deploy a Docker container (Lambda even has some guides to run Docker with nvidia-docker support
on their instances). They do not (yet) have a serverless container service like Cloud Run – it’s more

raw access.







[1](https://www.fluence.network/blog/best-cloud-gpu-providers-ai-2025/#:~:text=CoreWeave%20A100%2C%20H100%2C%20B200%2C%20L40S,configured%20environments)
























15


**Pricing:** Lambda’s pricing is quite good relative to AWS:
As noted in a comparison, **A100 80GB ~ $2.49/hr** and **H100 80GB ~$2.49/hr** as well (possibly
promotional) [1](https://www.fluence.network/blog/best-cloud-gpu-providers-ai-2025/#:~:text=CoreWeave%20A100%2C%20H100%2C%20B200%2C%20L40S,configured%20environments) . Another source indicates A100-80GB from ~$1.57/hr with certain discounts [19](https://northflank.com/blog/cheapest-cloud-gpu-providers#:~:text=7%20cheapest%20cloud%20GPU%20providers,savings%20compared) .
They have different plans – on-demand vs reserved, etc.
They often have volume discounts and even “spot” like offerings (though not as formal as AWS spot).
For multi-GPU, an 8×A100 server was listed around $11.60/hr (which is about $1.45/GPU/hr),

showing economies of scale.
No charge when instances are off, obviously, but you have to manually shut down or terminate to
stop billing.

**Pros:**

**Simple and developer-centric:** You sign up with GitHub, get credits for trial, and the interface is
straightforward. Many in the ML community find Lambda Cloud easy – “upload an SSH key, launch

instance, done” [61](https://www.fluence.network/blog/best-cloud-gpu-providers-ai-2025/#:~:text=Developers%20consistently%20praise%20platforms%20like,pain%20points%20in%20hyperscaler%20environments) .
**Pre-configured environments:** Lambda’s DL stack images save time. They also ensure driver
compatibility. The ease is slightly less than fully managed container services, but easier than vanilla
AWS in some ways (no need to individually manage Nvidia driver installs if you use their image).
**Cost and availability:** They try to keep stock of popular GPUs and are transparent about availability.
There have been times of shortage (especially for H100s), but they often communicate this. They can
be cheaper than hyperscalers and also **don’t charge for things like data egress** up to generous
limits, which is nice (moving model checkpoints around won’t incur big fees).
**Support for teams:** They offer team account management, which is useful for collaborations
(multiple users can share billing, etc.).

**Cons:**
**No autoscaling/on-demand scaling:** If you want to handle variable traffic, Lambda won’t scale a
deployment in/out automatically – you’d have to script it. It’s more static; you launch what you need
and keep it running. This can lead to idle costs if not managed.
**Manual work for services:** To run a persistent service (like an API for an LLM), you’ll have to set up
something on the VM (maybe Docker + your service, plus something like an nginx for endpoint). This
is less PaaS-y than Cloud Run or ACA, obviously.
**Capacity spikes:** Lambda Labs has sometimes had capacity constraints – their popularity soared in
2023–2024 and users reported _“often out of capacity”_ for certain GPU types [20](https://www.fluence.network/blog/best-cloud-gpu-providers-ai-2025/#:~:text=2,frustration) . They’re scaling up,
but it’s something to consider if you need guaranteed availability (they do have a reserved instance

program to ensure you have what you need).
**Fewer regions:** Lambda Cloud is primarily in North America (one east, one west data center) and
maybe one in Europe. It’s not as globally distributed, which could be a factor for latency or data

sovereignty.
**Use cases:** Lambda Cloud is great if you want a development environment or a training server with a
big GPU that you control entirely. It’s also used for hosted inference of models that perhaps run 24/7
(some companies deploy their models on Lambda instances behind their own API gateway).
Essentially, it’s a middle ground: not fully DIY (hardware/colo) but not high-level PaaS either –
something akin to renting a powerful GPU server by the hour. Given our focus on _containerized_
workloads, you would use Lambda if you don’t mind treating the VM as cattle and running Docker or
your app directly on it.








[1](https://www.fluence.network/blog/best-cloud-gpu-providers-ai-2025/#:~:text=CoreWeave%20A100%2C%20H100%2C%20B200%2C%20L40S,configured%20environments) [19](https://northflank.com/blog/cheapest-cloud-gpu-providers#:~:text=7%20cheapest%20cloud%20GPU%20providers,savings%20compared)

















[61](https://www.fluence.network/blog/best-cloud-gpu-providers-ai-2025/#:~:text=Developers%20consistently%20praise%20platforms%20like,pain%20points%20in%20hyperscaler%20environments)




























[20](https://www.fluence.network/blog/best-cloud-gpu-providers-ai-2025/#:~:text=2,frustration)











**Bottom line:** Lambda Labs provides **powerful GPUs on VMs with ML-friendly setup** at lower cost than the
big clouds. It doesn’t eliminate server management, but for many ML engineers, a Linux box with root
access is actually comfortable. If your workflow is not event-driven but rather long-running jobs or services,


16


and you can tolerate a bit of manual DevOps, Lambda is a solid, cost-effective choice. Ensure you
orchestrate shutting down machines when not in use to avoid extra costs.


**Paperspace Gradient (DigitalOcean)**


**Overview:** Paperspace Gradient is a cloud ML platform that was acquired by DigitalOcean. It aims to
provide an end-to-end solution from development (notebooks) to deployment (APIs) with minimal ops. For
our purposes, **Gradient’s Deployments** feature allows containerized model serving on GPU without
managing the underlying VM.



**GPU hardware:** Gradient offers a range of GPU instance types:
Notably, it has **NVIDIA A5000 (24 GB)**, **A6000 (48 GB)** as common choices that balance cost and

memory [22](https://www.paperspace.com/pricing#:~:text=A5000) [23](https://www.paperspace.com/pricing#:~:text=A6000) .

It also has A100 80GB and 40GB in its “Dedicated” tier (often labeled as A100-80G) [24](https://www.paperspace.com/pricing#:~:text=A100), and even

H100 via request (especially after DigitalOcean’s acquisition, new hardware is being added).
Older GPUs are available too: e.g., Quadro P6000 (24 GB) [62](https://www.paperspace.com/pricing#:~:text=P6000), Tesla V100 (16 or 32GB), etc., which
might be used for lower-cost options.
The table from Paperspace shows e.g. **P6000 (24GB) at $1.10/hr** and **A6000 (48GB) at $1.89/hr** ondemand [21](https://www.paperspace.com/pricing#:~:text=P6000) [23](https://www.paperspace.com/pricing#:~:text=A6000), which are quite attractive prices. A100-80GB was priced ~$1.15/hr with some
commitment (possibly a monthly rate) [24](https://www.paperspace.com/pricing#:~:text=A100), which is very low – perhaps that is a discounted rate or
reserved price.

**Platform services:** Gradient is more than raw GPUs:

**Notebooks:** You can spin up Jupyter notebooks on a chosen GPU with one click. This is interactive

dev and includes free GPU tiers (with slower GPUs).
**Workflows (Jobs):** You can run one-off container jobs (for training or batch inference) on a schedule
or triggered by events. This is akin to a pipeline runner.
**Deployments (Endpoints):** This is the key for our scenario: you can deploy a web service (Docker
container) to serve predictions. For example, containerize an API that loads your model and listens
on port 80 – Gradient will deploy it on a GPU instance and give you a stable endpoint. It manages
scaling (you can specify autoscaling rules or keep a fixed replica count) and can integrate with REST
or GraphQL frontends.

**Kubernetes under the hood:** Gradient is built with Kubernetes as the backbone [25](https://www.paperspace.com/gradient/enterprise#:~:text=The%20world%E2%80%99s%20most%20advanced%20AI,orchestration%20fabric), so these

features (notebooks, jobs, deployments) are abstractions on top of a K8s cluster. Users don’t see this
unless they use advanced features or the enterprise offering which allows hybrid (on-prem + cloud)
deployments. For most, it’s just click-and-go.

**Ease of use:**

The UI is user-friendly with project spaces, etc. For a deployment, you typically select a container
from their registry or provide an image URL, set environment variables, choose the instance type
(GPU size), and click deploy. The service handles pulling the image, scheduling on a node with that
GPU, and starting it.
Logging and monitoring are integrated (you can see logs, utilization, etc. in their console).
It supports versioning and canary updates (you can deploy a new version of the container and
gradually route traffic).
Integration with Git is possible: you can link a GitHub repo, and it can build the Docker image and
deploy on push (CI/CD).
**Pricing:**








[22](https://www.paperspace.com/pricing#:~:text=A5000) [23](https://www.paperspace.com/pricing#:~:text=A6000)




- [24](https://www.paperspace.com/pricing#:~:text=A100)




- [62](https://www.paperspace.com/pricing#:~:text=P6000)







[21](https://www.paperspace.com/pricing#:~:text=P6000) [23](https://www.paperspace.com/pricing#:~:text=A6000)



[24](https://www.paperspace.com/pricing#:~:text=A100)

















- **Kubernetes under the hood:** Gradient is built with Kubernetes as the backbone [25](https://www.paperspace.com/gradient/enterprise#:~:text=The%20world%E2%80%99s%20most%20advanced%20AI,orchestration%20fabric)





















17


Gradient has a mix of **hourly billing or monthly plans** . You can pay as you go for hours on a given
instance type (as shown in the pricing list [22](https://www.paperspace.com/pricing#:~:text=A5000) ).
They also have a subscription model where you pay a flat monthly fee for some capacity (like the Pro
plan gives some hours included, etc.).
Compared to others, Gradient’s on-demand rates for a given GPU are in the same ballpark as
CoreWeave/RunPod. For instance, $1.38/hr for A5000 24GB [22](https://www.paperspace.com/pricing#:~:text=A5000) is similar to RunPod’s 3090 price
(3090 is slightly cheaper but A5000 is a workstation-class card).
Because it’s integrated with DigitalOcean, billing is unified, and you might see some DO-style dev
credits or discounts for longer uses.

**Pros:**

**Full ML Ops pipeline:** If you need not just hosting but also a place to iterate on models, Gradient
provides that in one platform. You can go from a notebook (perhaps fine-tuning a model) to a
deployment in one environment. This is convenient for data scientists who want to avoid jumping
between many tools.
**Abstraction of infra:** You really don’t manage any VM or container runtime – it’s all handled. This
meets the “no full VM management” criterion perfectly. It’s as simple as deploying to Heroku, but for

GPUs.

**Team collaboration:** There are features for sharing notebooks, deploying within a team space, etc.
**Scalability:** Deployments can be scaled out to multiple instances if needed (though you’ll pay for
each, of course). It also supports auto-scaling based on metrics or schedule.
**Integration with popular frameworks:** They provide base container images (for PyTorch,
TensorFlow, etc.) and examples, which lowers the barrier if you’re containerizing your model server.

**Cons:**

**Less generalized:** Gradient is very much aimed at ML tasks. If you wanted to use it to host, say, a
generic GPU-accelerated microservice that isn’t ML (maybe a graphics app), it’s possible but not the
typical use. You might find some opinionation towards AI use cases (for example, metrics might
assume things like throughput of predictions, etc.).
**Cost for persistent use:** While prices are good, if you keep a GPU deployment running 24/7, it could
approach the cost of just renting the GPU on Lambda or CoreWeave. The added value is the
management and UI – but it’s something to consider. For large-scale production serving, costs might
add up unless you fully utilize the included features.
**Limits on customization:** In a fully managed setup, you might run into some limits (for instance,
certain networking constraints, or if you needed a very custom driver/kernel tweak, you can’t do
that). Usually not an issue, but worth noting for edge cases.
**Enterprise features still maturing:** Post-acquisition, integration with DigitalOcean’s cloud is
ongoing. For example, DO’s networking (VPC) and Gradient integration is evolving. If you need
things like bring-your-own-key encryption, etc., check whether Gradient supports it.
**Use case:** Gradient is excellent for a small team that wants to prototype and deploy an ML model
quickly. You could train a model on Gradient Notebooks (or elsewhere) and then deploy it via
Gradient in a contained environment. It’s also good for hackathons or demos – you can share a live
endpoint without exposing your local machine. In the context of LLMs, if you have a fine-tuned
model that you want to serve behind an API, you can just give Gradient your Docker container (with
something like FastAPI or HF Transformers serving) and it will handle the rest. This is very much “AI

PaaS”.







[22](https://www.paperspace.com/pricing#:~:text=A5000)











[22](https://www.paperspace.com/pricing#:~:text=A5000)














































**Bottom line:** Paperspace Gradient offers a **container-native, serverless-like experience for GPU model**
**hosting**, aligned with modern ML workflows. It hides the VM and Kubernetes details effectively [25](https://www.paperspace.com/gradient/enterprise#:~:text=The%20world%E2%80%99s%20most%20advanced%20AI,orchestration%20fabric) . With


18


>=24GB GPUs like A5000/A6000 readily available and pricing competitive, it’s a strong choice to consider,
especially if you value a unified environment for development and deployment.


**Other Notable Platforms (Koyeb, Replicate, Baseten, etc.)**


In addition to the above, a few emerging platforms cater to containerized AI inference without full VM

management:



**Koyeb:** A serverless platform akin to Cloud Run, which recently introduced support for GPU-powered
endpoints. Users have reported success deploying models on Koyeb’s “Functions” with GPU –
essentially you can get a dedicated GPU endpoint in seconds [63](https://www.reddit.com/r/deeplearning/comments/1hc29vn/hosting_a_serverlessgpu_endpoint/#:~:text=Hosting%20a%20Serverless,com) . Koyeb supports Docker images
and will schedule them on GPU instances (likely using A100s under the hood). Pricing is usagebased. The benefit of Koyeb is simplicity and a developer-friendly YAML/CLI to define services. It’s a
newer player, but if you want a multi-cloud serverless feel with GPU (and perhaps not tied to a big
cloud provider), it’s worth a look.







[63](https://www.reddit.com/r/deeplearning/comments/1hc29vn/hosting_a_serverlessgpu_endpoint/#:~:text=Hosting%20a%20Serverless,com)




- **Replicate:** Replicate.com is a hosted inference service primarily for ML models. Instead of managing

containers directly, you write a small handler (or use their pre-built model repos) and Replicate runs
it on GPUs and exposes a web API for inference calls. It’s not exactly bring-any-container (they have a
specific interface for model prediction), but under the hood they use GPUs (often A100s). Pricing is
per second of inference and they abstract all infra. This is great for quickly exposing a model via REST
API (especially image or text generative models) without worrying about deployment at all. The
limitation is it’s only for inference (not arbitrary long-running services) and you pay per run rather
than for a persistent container.




- **Baseten:** Baseten is an MLOps platform where you can deploy models (they containerize it or you

provide a Docker) and build simple web apps on top. They offer GPU hosting for models as well. It’s
more of an integrated solution (with a UI builder for creating demo interfaces, etc.). Baseten
manages scaling and infrastructure; you just focus on your model logic. Like Gradient, it’s focused on
ML applications.




- **Modal:** Modal is a serverless cloud for running code (kind of like a supercharged AWS Lambda) and

recently introduced GPU support for their functions. You can define a container image with your
code, and Modal will spin up GPU instances on demand to run it. It’s very developer-friendly (you can
trigger via API or schedule jobs) and it handles the provisioning. If your use case is sporadic GPU
tasks (like generating images or performing a heavy computation occasionally), Modal’s on-demand
approach could save cost and complexity.




- **Hugging Face Inference Endpoints:** For completeness, if your workload is specifically serving a

Transformer/LLM model, Hugging Face offers managed inference endpoints. You select a model (or
bring your own) and choose a hardware size (they do have GPU options like T4 or A100). They then
serve the model behind an API for you. This is highly specialized (just for ML inference with the
Transformers or Diffusers libraries), but it’s zero-devops. Pricing, however, can be higher since it’s a

niche service.


19


Each of these has its niche, but all share the goal: **let developers deploy AI workloads to GPUs without**
**wrangling servers** . The best choice depends on your priorities – be it raw performance per dollar, ease of
use, integrated ML tools, or fine-grained control:



If you want **the absolute simplest path** to host an LLM API for occasional use, a fully-managed
solution like Cloud Run, Azure Container Apps, or a third-party serverless (Koyeb/Modal) is ideal.
If you need **heavy lifting power at lower cost** and don’t mind a bit of setup, CoreWeave or Lambda
(with Kubernetes or your own orchestration) might be better.
If you prefer an **ML-specialized platform** that handles everything from notebooks to monitoring,
Gradient or Baseten fit well.
And if you are cost-sensitive and somewhat hands-on, **RunPod** and similar marketplaces provide
tremendous value with just a slight trade-off in automation.


















## **Conclusion**

There are now many options to run containerized AI workloads on GPUs without maintaining your own
servers. The traditional cloud providers (AWS, GCP, Azure) have begun offering serverless or managed
container services for GPUs – with Google and Azure taking the lead in ease-of-use with Cloud Run and
Container Apps. Meanwhile, specialized GPU clouds (CoreWeave, RunPod, Lambda, etc.) fill the gap by
providing more affordable or flexible access to GPU hardware, often appealing to researchers and startups.


When choosing among these: - **Match GPU specs to your model needs:** Ensure the service offers >=24 GB
GPUs since larger LLMs will need that. All options listed do (through various GPU models). - **Consider usage**
**pattern:** For always-on services with steady traffic, a dedicated instance (Lambda Labs or a reserved
CoreWeave pod) might be cost-effective. For spiky or experimental usage, serverless scaling (Cloud Run,
ACA, RunPod’s per-second pods) will save money. - **Assess integration effort:** If you want plug-and-play
with minimal coding beyond your model, platforms like Gradient or Replicate shine. If you have a DevOps
team or need custom networking, a Kubernetes-based solution might be preferable.


In summary, the ecosystem for containerized AI on GPUs is rich in 2025. Whether you choose a
hyperscaler’s managed service or a niche GPU cloud, you can avoid the pain of managing GPU VMs and
instead focus on your AI models. Each provider above offers a pathway to deploy LLMs (e.g. via Ollama or

similar tools) in containers – pick the one that best aligns with your technical comfort and workload profile,
and you’ll be able to serve up your AI models efficiently and scalably.


**Sources:**



AWS Fargate GPU limitation [2](https://spot.io/resources/aws-fargate/aws-fargate-explained-pros-cons-components-features/#:~:text=and%20the%20hard%20limit%20is,information%20in%20the%20pod%20spec) ; ECS GPU support docs [27](https://docs.aws.amazon.com/AmazonECS/latest/developerguide/ecs-gpu-specifying.html#:~:text=Specifying%20GPUs%20in%20an%20Amazon,The%20number%20of) .
Cloud Run GPUs GA – Google Blog [5](https://cloud.google.com/blog/products/serverless/cloud-run-gpus-are-now-generally-available#:~:text=Now%2C%20you%20can%20enjoy%20the,across%20both%20GPUs%20and%20CPUs) [7](https://cloud.google.com/blog/products/serverless/cloud-run-gpus-are-now-generally-available#:~:text=To%20support%20global%20applications%2C%20Cloud,with%20more%20to%20come) [6](https://cloud.google.com/blog/products/serverless/cloud-run-gpus-are-now-generally-available#:~:text=India%29%2C%20with%20more%20to%20come) ; InfoQ note on L4 support [3](https://www.infoq.com/news/2024/12/azure-container-apps-gpu/#:~:text=Replicate%2C%20Baseten%2C%20Koyeb%20and%20Fal,time%20AI%20inferencing) .
Azure Container Apps GPUs – MS Blog [8](https://techcommunity.microsoft.com/blog/appsonazureblog/announcing-ga-for-azure-container-apps-serverless-gpus/4394302#:~:text=Key%20benefits%20of%20serverless%20GPUs) [9](https://techcommunity.microsoft.com/blog/appsonazureblog/announcing-ga-for-azure-container-apps-serverless-gpus/4394302#:~:text=%2A%20Scale,applications%20alongside%20your%20existing%20apps) and MS Learn docs [12](https://learn.microsoft.com/en-us/azure/container-instances/container-instances-gpu#:~:text=Things%20to%20know) .
Pricing comparisons (Fluence network) [29](https://www.fluence.network/blog/best-cloud-gpu-providers-ai-2025/#:~:text=Google%20Cloud%20A100%2C%20H100%2C%20L4%2C,second%20billing%2C%20Secure%20and) [1](https://www.fluence.network/blog/best-cloud-gpu-providers-ai-2025/#:~:text=CoreWeave%20A100%2C%20H100%2C%20B200%2C%20L40S,configured%20environments) and provider pricing pages [22](https://www.paperspace.com/pricing#:~:text=A5000) [62](https://www.paperspace.com/pricing#:~:text=P6000) .
CoreWeave vs RunPod analysis [16](https://www.runpod.io/articles/comparison/runpod-vs-coreweave-which-cloud-gpu-platform-is-best-for-ai-image-generation#:~:text=,at%20default%20Stable%20Diffusion%20resolution) [15](https://www.runpod.io/articles/comparison/runpod-vs-coreweave-which-cloud-gpu-platform-is-best-for-ai-image-generation#:~:text=Additionally%2C%20deployment%20speed%20is%20a,a%20slight%20edge%20in%20agility) .
RunPod platform description and pricing [18](https://www.runpod.io/articles/comparison/runpod-vs-coreweave-which-cloud-gpu-platform-is-best-for-ai-image-generation#:~:text=regions%2C%20leveraging%20both%20its%20Secure,complexity%20of%20traditional%20cloud%20setups) [58](https://www.runpod.io/articles/comparison/runpod-vs-coreweave-which-cloud-gpu-platform-is-best-for-ai-image-generation#:~:text=,on%20Runpod%E2%80%99s%20standard%20pricing%2C%20and) .

Lambda Cloud info – Fluence and GetDeploying comparisons [1](https://www.fluence.network/blog/best-cloud-gpu-providers-ai-2025/#:~:text=CoreWeave%20A100%2C%20H100%2C%20B200%2C%20L40S,configured%20environments) .
Paperspace Gradient documentation [25](https://www.paperspace.com/gradient/enterprise#:~:text=The%20world%E2%80%99s%20most%20advanced%20AI,orchestration%20fabric) and pricing page [23](https://www.paperspace.com/pricing#:~:text=A6000) .
Reddit discussion of Koyeb GPU endpoint [63](https://www.reddit.com/r/deeplearning/comments/1hc29vn/hosting_a_serverlessgpu_endpoint/#:~:text=Hosting%20a%20Serverless,com) .




- [2](https://spot.io/resources/aws-fargate/aws-fargate-explained-pros-cons-components-features/#:~:text=and%20the%20hard%20limit%20is,information%20in%20the%20pod%20spec) [27](https://docs.aws.amazon.com/AmazonECS/latest/developerguide/ecs-gpu-specifying.html#:~:text=Specifying%20GPUs%20in%20an%20Amazon,The%20number%20of)




- [5](https://cloud.google.com/blog/products/serverless/cloud-run-gpus-are-now-generally-available#:~:text=Now%2C%20you%20can%20enjoy%20the,across%20both%20GPUs%20and%20CPUs) [7](https://cloud.google.com/blog/products/serverless/cloud-run-gpus-are-now-generally-available#:~:text=To%20support%20global%20applications%2C%20Cloud,with%20more%20to%20come) [6](https://cloud.google.com/blog/products/serverless/cloud-run-gpus-are-now-generally-available#:~:text=India%29%2C%20with%20more%20to%20come) [3](https://www.infoq.com/news/2024/12/azure-container-apps-gpu/#:~:text=Replicate%2C%20Baseten%2C%20Koyeb%20and%20Fal,time%20AI%20inferencing)




- [8](https://techcommunity.microsoft.com/blog/appsonazureblog/announcing-ga-for-azure-container-apps-serverless-gpus/4394302#:~:text=Key%20benefits%20of%20serverless%20GPUs) [9](https://techcommunity.microsoft.com/blog/appsonazureblog/announcing-ga-for-azure-container-apps-serverless-gpus/4394302#:~:text=%2A%20Scale,applications%20alongside%20your%20existing%20apps) and MS Learn docs [12](https://learn.microsoft.com/en-us/azure/container-instances/container-instances-gpu#:~:text=Things%20to%20know)




- [29](https://www.fluence.network/blog/best-cloud-gpu-providers-ai-2025/#:~:text=Google%20Cloud%20A100%2C%20H100%2C%20L4%2C,second%20billing%2C%20Secure%20and) [1](https://www.fluence.network/blog/best-cloud-gpu-providers-ai-2025/#:~:text=CoreWeave%20A100%2C%20H100%2C%20B200%2C%20L40S,configured%20environments) [22](https://www.paperspace.com/pricing#:~:text=A5000) [62](https://www.paperspace.com/pricing#:~:text=P6000)




- [16](https://www.runpod.io/articles/comparison/runpod-vs-coreweave-which-cloud-gpu-platform-is-best-for-ai-image-generation#:~:text=,at%20default%20Stable%20Diffusion%20resolution) [15](https://www.runpod.io/articles/comparison/runpod-vs-coreweave-which-cloud-gpu-platform-is-best-for-ai-image-generation#:~:text=Additionally%2C%20deployment%20speed%20is%20a,a%20slight%20edge%20in%20agility)




- [18](https://www.runpod.io/articles/comparison/runpod-vs-coreweave-which-cloud-gpu-platform-is-best-for-ai-image-generation#:~:text=regions%2C%20leveraging%20both%20its%20Secure,complexity%20of%20traditional%20cloud%20setups) [58](https://www.runpod.io/articles/comparison/runpod-vs-coreweave-which-cloud-gpu-platform-is-best-for-ai-image-generation#:~:text=,on%20Runpod%E2%80%99s%20standard%20pricing%2C%20and)




- [1](https://www.fluence.network/blog/best-cloud-gpu-providers-ai-2025/#:~:text=CoreWeave%20A100%2C%20H100%2C%20B200%2C%20L40S,configured%20environments)




- [25](https://www.paperspace.com/gradient/enterprise#:~:text=The%20world%E2%80%99s%20most%20advanced%20AI,orchestration%20fabric) [23](https://www.paperspace.com/pricing#:~:text=A6000)




- [63](https://www.reddit.com/r/deeplearning/comments/1hc29vn/hosting_a_serverlessgpu_endpoint/#:~:text=Hosting%20a%20Serverless,com)



20


[1](https://www.fluence.network/blog/best-cloud-gpu-providers-ai-2025/#:~:text=CoreWeave%20A100%2C%20H100%2C%20B200%2C%20L40S,configured%20environments) [20](https://www.fluence.network/blog/best-cloud-gpu-providers-ai-2025/#:~:text=2,frustration) [29](https://www.fluence.network/blog/best-cloud-gpu-providers-ai-2025/#:~:text=Google%20Cloud%20A100%2C%20H100%2C%20L4%2C,second%20billing%2C%20Secure%20and) [61](https://www.fluence.network/blog/best-cloud-gpu-providers-ai-2025/#:~:text=Developers%20consistently%20praise%20platforms%20like,pain%20points%20in%20hyperscaler%20environments)



Best Cloud GPU Providers for AI: How to Choose (2025) - Fluence



[https://www.fluence.network/blog/best-cloud-gpu-providers-ai-2025/](https://www.fluence.network/blog/best-cloud-gpu-providers-ai-2025/)



[2](https://spot.io/resources/aws-fargate/aws-fargate-explained-pros-cons-components-features/#:~:text=and%20the%20hard%20limit%20is,information%20in%20the%20pod%20spec)



AWS Fargate Explained: Pros and Cons, Components & Key Features | Spot.io



[https://spot.io/resources/aws-fargate/aws-fargate-explained-pros-cons-components-features/](https://spot.io/resources/aws-fargate/aws-fargate-explained-pros-cons-components-features/)



[3](https://www.infoq.com/news/2024/12/azure-container-apps-gpu/#:~:text=Replicate%2C%20Baseten%2C%20Koyeb%20and%20Fal,time%20AI%20inferencing)



Microsoft Introduces Serverless GPUs on Azure Container Apps in Public Preview - InfoQ



[https://www.infoq.com/news/2024/12/azure-container-apps-gpu/](https://www.infoq.com/news/2024/12/azure-container-apps-gpu/)



[4](https://docs.cloud.google.com/run/docs/configuring/services/gpu#:~:text=Documentation%20docs,current%20NVIDIA%20driver%20version%3A)



GPU support for services | Cloud Run - Google Cloud Documentation



[https://docs.cloud.google.com/run/docs/configuring/services/gpu](https://docs.cloud.google.com/run/docs/configuring/services/gpu)



[5](https://cloud.google.com/blog/products/serverless/cloud-run-gpus-are-now-generally-available#:~:text=Now%2C%20you%20can%20enjoy%20the,across%20both%20GPUs%20and%20CPUs) [6](https://cloud.google.com/blog/products/serverless/cloud-run-gpus-are-now-generally-available#:~:text=India%29%2C%20with%20more%20to%20come) [7](https://cloud.google.com/blog/products/serverless/cloud-run-gpus-are-now-generally-available#:~:text=To%20support%20global%20applications%2C%20Cloud,with%20more%20to%20come) [30](https://cloud.google.com/blog/products/serverless/cloud-run-gpus-are-now-generally-available#:~:text=AI%20inference%20for%20everyone) [31](https://cloud.google.com/blog/products/serverless/cloud-run-gpus-are-now-generally-available#:~:text=changer%20for%20sporadic%20or%20unpredictable,workloads) [32](https://cloud.google.com/blog/products/serverless/cloud-run-gpus-are-now-generally-available#:~:text=,time%2C%20and%20running%20the%20inference) [33](https://cloud.google.com/blog/products/serverless/cloud-run-gpus-are-now-generally-available#:~:text=,users%20as%20they%20are%20generated) [36](https://cloud.google.com/blog/products/serverless/cloud-run-gpus-are-now-generally-available#:~:text=Multi)



Cloud Run GPUs are now generally available | Google Cloud Blog



[https://cloud.google.com/blog/products/serverless/cloud-run-gpus-are-now-generally-available](https://cloud.google.com/blog/products/serverless/cloud-run-gpus-are-now-generally-available)


[8](https://techcommunity.microsoft.com/blog/appsonazureblog/announcing-ga-for-azure-container-apps-serverless-gpus/4394302#:~:text=Key%20benefits%20of%20serverless%20GPUs) [9](https://techcommunity.microsoft.com/blog/appsonazureblog/announcing-ga-for-azure-container-apps-serverless-gpus/4394302#:~:text=%2A%20Scale,applications%20alongside%20your%20existing%20apps) [11](https://techcommunity.microsoft.com/blog/appsonazureblog/announcing-ga-for-azure-container-apps-serverless-gpus/4394302#:~:text=With%20GA%2C%20we%20are%20introducing,for%20A100%20and%20T4%20GPUs) [37](https://techcommunity.microsoft.com/blog/appsonazureblog/announcing-ga-for-azure-container-apps-serverless-gpus/4394302#:~:text=Image%3A%20Icon%20for%20Microsoft%20rankMicrosoft) [38](https://techcommunity.microsoft.com/blog/appsonazureblog/announcing-ga-for-azure-container-apps-serverless-gpus/4394302#:~:text=From%20the%20portal%2C%20you%20can,Container%20App%C2%A0or%C2%A0your%20Container%20App%20Job) [39](https://techcommunity.microsoft.com/blog/appsonazureblog/announcing-ga-for-azure-container-apps-serverless-gpus/4394302#:~:text=Serverless%20GPUs%20accelerate%20the%20speed,which%20to%20build%20your%20applications) [40](https://techcommunity.microsoft.com/blog/appsonazureblog/announcing-ga-for-azure-container-apps-serverless-gpus/4394302#:~:text=NVIDIA%20T4) [42](https://techcommunity.microsoft.com/blog/appsonazureblog/announcing-ga-for-azure-container-apps-serverless-gpus/4394302#:~:text=Quota%20changes%20for%20GA) [47](https://techcommunity.microsoft.com/blog/appsonazureblog/announcing-ga-for-azure-container-apps-serverless-gpus/4394302#:~:text=This%20GA%20release%20of%20Serverless,standard%20APIs) [48](https://techcommunity.microsoft.com/blog/appsonazureblog/announcing-ga-for-azure-container-apps-serverless-gpus/4394302#:~:text=Serverless%20GPUs%20now%20support%20NVIDIA,endpoints%20on%20Azure%20Container%20Apps) Announcing GA for Azure Container Apps Serverless GPUs | Microsoft

Community Hub


[https://techcommunity.microsoft.com/blog/appsonazureblog/announcing-ga-for-azure-container-apps-serverless-gpus/4394302](https://techcommunity.microsoft.com/blog/appsonazureblog/announcing-ga-for-azure-container-apps-serverless-gpus/4394302)



[10](https://azure.microsoft.com/en-gb/pricing/details/container-apps/#:~:text=~17,shown%20above%20are%20in) [43](https://azure.microsoft.com/en-gb/pricing/details/container-apps/#:~:text=Metre%20Pay%20as%20you%20go,year%20Savings%20Plan%20Price) [44](https://azure.microsoft.com/en-gb/pricing/details/container-apps/#:~:text=Container%20Apps%20are%20billed%20based,are%20included%20free%20each%20month)



Azure Container Apps - Pricing



[https://azure.microsoft.com/en-gb/pricing/details/container-apps/](https://azure.microsoft.com/en-gb/pricing/details/container-apps/)



[12](https://learn.microsoft.com/en-us/azure/container-instances/container-instances-gpu#:~:text=Things%20to%20know) [41](https://learn.microsoft.com/en-us/azure/container-instances/container-instances-gpu#:~:text=See%20pricing%20details) [45](https://learn.microsoft.com/en-us/azure/container-instances/container-instances-gpu#:~:text=Additional%20limitations%3A%20GPU%20resources%20can%27t,group%20into%20a%20virtual%20network) [46](https://learn.microsoft.com/en-us/azure/container-instances/container-instances-gpu#:~:text=Important)



Deploy GPU-enabled container instance - Azure Container Instances | Microsoft Learn



[https://learn.microsoft.com/en-us/azure/container-instances/container-instances-gpu](https://learn.microsoft.com/en-us/azure/container-instances/container-instances-gpu)


[13](https://www.runpod.io/articles/comparison/runpod-vs-coreweave-which-cloud-gpu-platform-is-best-for-ai-image-generation#:~:text=receiving%20outputs.%20,GPU%20variety%20is%20slightly%20narrower) [15](https://www.runpod.io/articles/comparison/runpod-vs-coreweave-which-cloud-gpu-platform-is-best-for-ai-image-generation#:~:text=Additionally%2C%20deployment%20speed%20is%20a,a%20slight%20edge%20in%20agility) [16](https://www.runpod.io/articles/comparison/runpod-vs-coreweave-which-cloud-gpu-platform-is-best-for-ai-image-generation#:~:text=,at%20default%20Stable%20Diffusion%20resolution) [17](https://www.runpod.io/articles/comparison/runpod-vs-coreweave-which-cloud-gpu-platform-is-best-for-ai-image-generation#:~:text=,on%20Runpod%E2%80%99s%20standard%20pricing%2C%20and) [18](https://www.runpod.io/articles/comparison/runpod-vs-coreweave-which-cloud-gpu-platform-is-best-for-ai-image-generation#:~:text=regions%2C%20leveraging%20both%20its%20Secure,complexity%20of%20traditional%20cloud%20setups) [49](https://www.runpod.io/articles/comparison/runpod-vs-coreweave-which-cloud-gpu-platform-is-best-for-ai-image-generation#:~:text=CoreWeave%2C%20founded%20in%202017%2C%20started,NeoX%20model) [52](https://www.runpod.io/articles/comparison/runpod-vs-coreweave-which-cloud-gpu-platform-is-best-for-ai-image-generation#:~:text=on%20a%20highly%20configurable%20platform,training%20to%20visual%20effects%20rendering) [56](https://www.runpod.io/articles/comparison/runpod-vs-coreweave-which-cloud-gpu-platform-is-best-for-ai-image-generation#:~:text=Runpod%20is%20a%20newer%20entrant,complexity%20of%20traditional%20cloud%20setups) [57](https://www.runpod.io/articles/comparison/runpod-vs-coreweave-which-cloud-gpu-platform-is-best-for-ai-image-generation#:~:text=can%20spin%20up%20isolated%20GPU,complexity%20of%20traditional%20cloud%20setups) [58](https://www.runpod.io/articles/comparison/runpod-vs-coreweave-which-cloud-gpu-platform-is-best-for-ai-image-generation#:~:text=,on%20Runpod%E2%80%99s%20standard%20pricing%2C%20and) [59](https://www.runpod.io/articles/comparison/runpod-vs-coreweave-which-cloud-gpu-platform-is-best-for-ai-image-generation#:~:text=In%20summary%2C%20Runpod%20positions%20itself,aspects%20for%20AI%20image%20generation) [60](https://www.runpod.io/articles/comparison/runpod-vs-coreweave-which-cloud-gpu-platform-is-best-for-ai-image-generation#:~:text=performance%20with%20high,flexibility%20on%20the%20lower%20end) Runpod vs. CoreWeave: Which Cloud GPU Platform Is Best for AI


Image Generation?


[https://www.runpod.io/articles/comparison/runpod-vs-coreweave-which-cloud-gpu-platform-is-best-for-ai-image-generation](https://www.runpod.io/articles/comparison/runpod-vs-coreweave-which-cloud-gpu-platform-is-best-for-ai-image-generation)



[14](https://www.coreweave.com/pricing#:~:text=NVIDIA%20HGX%20H100) [51](https://www.coreweave.com/pricing#:~:text=Free) [54](https://www.coreweave.com/pricing#:~:text=Data%20transfer%20within%20CoreWeave) [55](https://www.coreweave.com/pricing#:~:text=)



GPU Cloud Pricing | CoreWeave



[https://www.coreweave.com/pricing](https://www.coreweave.com/pricing)



[19](https://northflank.com/blog/cheapest-cloud-gpu-providers#:~:text=7%20cheapest%20cloud%20GPU%20providers,savings%20compared)



7 cheapest cloud GPU providers in 2025 | Blog - Northflank



[https://northflank.com/blog/cheapest-cloud-gpu-providers](https://northflank.com/blog/cheapest-cloud-gpu-providers)



[21](https://www.paperspace.com/pricing#:~:text=P6000) [22](https://www.paperspace.com/pricing#:~:text=A5000) [23](https://www.paperspace.com/pricing#:~:text=A6000) [24](https://www.paperspace.com/pricing#:~:text=A100) [62](https://www.paperspace.com/pricing#:~:text=P6000)



Pricing | DigitalOcean



[https://www.paperspace.com/pricing](https://www.paperspace.com/pricing)



[25](https://www.paperspace.com/gradient/enterprise#:~:text=The%20world%E2%80%99s%20most%20advanced%20AI,orchestration%20fabric)



Enterprise GPU & MLops Platform | Paperspace



[https://www.paperspace.com/gradient/enterprise](https://www.paperspace.com/gradient/enterprise)



[26](https://rayn.group/understanding-aws-app-runner/#:~:text=AWS%20App%20Runner%20is%20a,only%20available%20in%20the)



Understanding AWS App Runner - Rayn Group



[https://rayn.group/understanding-aws-app-runner/](https://rayn.group/understanding-aws-app-runner/)



[27](https://docs.aws.amazon.com/AmazonECS/latest/developerguide/ecs-gpu-specifying.html#:~:text=Specifying%20GPUs%20in%20an%20Amazon,The%20number%20of)



Specifying GPUs in an Amazon ECS task definition



[https://docs.aws.amazon.com/AmazonECS/latest/developerguide/ecs-gpu-specifying.html](https://docs.aws.amazon.com/AmazonECS/latest/developerguide/ecs-gpu-specifying.html)



[28](https://repost.aws/questions/QUdIP4nsQeRF6JA1e6aw0W9g/how-can-i-run-sagemaker-serverless-inference-on-a-gpu-instance#:~:text=How%20can%20I%20run%20SageMaker,of%20the%20serverless%20endpoints)



How can I run SageMaker Serverless Inference on a GPU instance?



[https://repost.aws/questions/QUdIP4nsQeRF6JA1e6aw0W9g/how-can-i-run-sagemaker-serverless-inference-on-a-gpu-instance](https://repost.aws/questions/QUdIP4nsQeRF6JA1e6aw0W9g/how-can-i-run-sagemaker-serverless-inference-on-a-gpu-instance)



[34](https://docs.cloud.google.com/kubernetes-engine/docs/how-to/autopilot-gpus#:~:text=Request%20and%20deploy%20GPU%20workloads,B200%2C%20H200%2C%20H100%2C%20and%20A100)



Deploy GPU workloads in Autopilot | GKE AI/ML



[https://docs.cloud.google.com/kubernetes-engine/docs/how-to/autopilot-gpus](https://docs.cloud.google.com/kubernetes-engine/docs/how-to/autopilot-gpus)



[35](https://cloud.google.com/blog/products/containers-kubernetes/run-gpu-workloads-on-gke-autopilot#:~:text=To%20enable%20such%20workloads%20on,you%20can%20run%20ML)



Run GPU workloads on GKE Autopilot | Google Cloud Blog



[https://cloud.google.com/blog/products/containers-kubernetes/run-gpu-workloads-on-gke-autopilot](https://cloud.google.com/blog/products/containers-kubernetes/run-gpu-workloads-on-gke-autopilot)


21


[50](https://dgtlinfra.com/coreweave-data-center-locations/#:~:text=CoreWeave%3A%20Data%20Center%20Regions%2C%20Locations%2C,compute%2C%20CPU%20compute%2C%20containers%2C)



CoreWeave: Data Center Regions, Locations, and GPU Cloud



[https://dgtlinfra.com/coreweave-data-center-locations/](https://dgtlinfra.com/coreweave-data-center-locations/)



[53](https://lambda.ai/pricing#:~:text=Lambda%20AI%20GPU%20Cloud%20pricing%3A,85%2C%20A100%2C%20GH200%29%2C)



AI Cloud Pricing | Lambda



[https://lambda.ai/pricing](https://lambda.ai/pricing)



[63](https://www.reddit.com/r/deeplearning/comments/1hc29vn/hosting_a_serverlessgpu_endpoint/#:~:text=Hosting%20a%20Serverless,com)



Hosting a Serverless-GPU Endpoint : r/deeplearning - Reddit



[https://www.reddit.com/r/deeplearning/comments/1hc29vn/hosting_a_serverlessgpu_endpoint/](https://www.reddit.com/r/deeplearning/comments/1hc29vn/hosting_a_serverlessgpu_endpoint/)


22


