The best serverless GPU platform for you depends mostly on how quickly a cold container starts when the first request arrives, and whether you want to run your own code or call hosted models. This page lists ten serverless GPU platforms in no particular order, with what each one is built for.
A serverless GPU platform runs your model only when a request comes in, then scales back down when it's quiet. You don't manage servers, and you're not keeping a big machine switched on overnight for traffic that never arrives.
The ten platforms below are listed alphabetically and in no particular order. Every description comes from the provider's own website, read this week. If you're still picking a model, my guide to Hugging Face explains where most open models live.
Serverless GPU platforms at a glance
I checked the free plan column on each provider's own website, as of Oct 2026. Yes means the page I read names a free plan, free tier, free credits or open source software. Trial only means a free trial or starter credit. Not stated means the pages I read named neither, even where there's a button inviting you to start, so check the provider's page before you sign up. A free tier rarely covers the biggest GPUs.
| Tool | Type | Good for | Free plan |
|---|---|---|---|
| Baseten | Inference platform for production models | Dedicated inference for open and custom models | Not stated |
| Beam | Serverless GPU compute | Autoscaling endpoints from Python | Not stated |
| Cerebrium | Serverless GPU infrastructure | Real-time apps that need fast cold starts | Not stated |
| Cloudflare Workers AI | Serverless AI on a global network | Calling models from Cloudflare Workers | Yes |
| fal | Generative media platform | Image, video and audio model APIs | Not stated |
| Koyeb | Serverless platform for apps and inference | Apps and models on GPUs worldwide | Not stated |
| Modal | Serverless platform for AI | Python functions on fast-starting GPUs | Not stated |
| Novita AI | Model and agent cloud | Model APIs plus GPU instances | Not stated |
| Replicate | Run models with an API | Running public models with one call | Not stated |
| Runpod Serverless | On-demand GPU endpoints | Inference endpoints that scale with traffic | Not stated |
Ten serverless GPU platforms worth a look
Baseten
Best for: Product teams that want to serve open-source, custom and fine-tuned models on infrastructure built for fast inference.
Beam
Best for: Python developers who want to serve LLMs, ComfyUI workflows or image models behind autoscaling API endpoints, paying only while they run.
Cerebrium
Best for: Real-time AI products that need containers to launch in seconds, using memory and GPU snapshotting for fast restores.
Cloudflare Workers AI
Best for: Web developers who want to run AI models on serverless GPUs across Cloudflare's network without managing idle infrastructure.
fal
Best for: Creative apps that want hundreds of image, video, audio and 3D models through one unified API, plus serverless compute.
Koyeb
Best for: Builders who want to deploy APIs, inference and databases on GPUs, CPUs and accelerators with automatic scaling in many locations.
Modal
Best for: Engineers who want to run inference, training and sandboxes on GPUs that spin up in seconds from a multi-cloud pool.
Novita AI
Best for: Developers who want serverless model APIs for hundreds of open models, an agent sandbox and GPU instances on one platform.
Replicate
Best for: App makers who want to run and fine-tune public models or deploy custom ones with a single line of code.
Runpod Serverless
Best for: Startups that want real-time inference on low-latency GPUs whose endpoints react and scale instantly when traffic arrives.
Which platform fits the job
- Serving open and custom models in production: Baseten.
- Autoscaling endpoints written in Python: Beam.
- Calling models from web code at the edge: Cloudflare Workers AI.
- Image, video and audio models through one API: fal or Novita AI.
- Apps and inference in many locations: Koyeb.
- Inference endpoints that scale with traffic: Runpod Serverless.
Scale to zero has a hidden half
Google Cloud documentation for Cloud Run says services with a GPU can scale down to zero when not in use, and instances with an L4 or RTX PRO 6000 Blackwell GPU and drivers pre-installed start in approximately 5 seconds.
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Scaling to zero is the part everyone advertises. The start-up time is the part users feel. If your app is quiet for an hour, the next person to arrive waits for a container to start, load the model and answer. Five seconds is fine for a background job and painful in a chat window. Ask every platform how it handles cold starts, whether it can keep one warm instance running and how large models are loaded into memory.
Serverless functions, model APIs or endpoints?
Serverless functions run your own Python code on a GPU. You bring the model and the logic, and the platform handles scaling.
Model APIs give you ready-hosted public models. You send a request and get an image, transcript or answer back, with no container to build.
Dedicated endpoints sit in between. You deploy a chosen model, the platform keeps it running at a set size and you trade some flexibility for steadier response times.
What to test in your first week
- Time the first request after the app has been idle.
- Time a normal request when the model is already warm.
- Send a burst of requests and watch how fast it scales.
- Check how logs and errors show up when something fails.
- See how easily you can switch GPU type.
- Confirm where your requests and outputs are stored.
Do you make a serverless GPU platform? Tool makers can ask to be considered for a featured place on this list.
My plain advice on going serverless
Go serverless when traffic is spiky or unpredictable, and move to dedicated capacity once you have steady demand all day. That one rule saves most teams from paying for idle machines early on and from slow cold starts later.
Whatever you choose, keep your model weights and code portable, so you can move platforms without rewriting the product. My guide to Google's AI platform covers the managed route if you're already on Google Cloud.
Frequently asked questions
What is a serverless GPU?
It's GPU computing you use on demand: the platform starts a container when a request arrives and shuts it down when traffic stops.
What is a cold start?
A cold start is the delay when no container is running and the platform has to start one and load your model before answering.
Is serverless GPU good for chatbots?
It can be, as long as cold starts are short or you keep one instance warm, because users notice a slow first reply.
Can I run my own model on a serverless GPU platform?
Yes. Most platforms let you deploy custom code or models, and some also offer ready-hosted public models.
When should I move to dedicated GPUs?
When traffic is steady enough that a machine would be busy most of the day, dedicated capacity is usually simpler and more predictable.
Is there a free serverless GPU platform?
Cloudflare's Workers AI page names a free plan; most others I read didn't name one.
