Pick Modal if you live in Python and want your infrastructure defined in code, especially for sandboxes and big parallel jobs. Pick RunPod if you already package work as containers, or you want a plain GPU machine you can rent alongside serverless endpoints. For straightforward serverless inference from a Docker image, the container-first option is the more direct fit.
Where does your machine get described: in Python or in a Dockerfile?
That one habit tells you most of what you need to know. Some teams think in functions. They want to write a bit of Python, mark it as needing a GPU and have it run in the cloud as if it were on their laptop. Other teams think in images. They've already got a container that works, and they want somewhere to put it that scales up when requests arrive and down when they stop.
Both of these platforms will run AI inference, fine-tuning and heavy compute jobs. The difference is the front door you walk through, and once you're inside, it shapes almost everything else.
What each one gives you
The code-first platform
Here you write Python functions with decorators and run them from the command line. The docs say there's zero configuration, and the products are named around jobs: Inference, Sandboxes, Training and Functions. The vendor says it rebuilt the infrastructure for AI, from the container runtime and storage to a capacity pool spread across several clouds. It suits Python developers who want cloud compute that feels like local development.
The container-first GPU cloud
The other side is a GPU cloud with four parts: on-demand Pods, Serverless endpoints, multi-node Clusters and a Hub of open-source models and templates. You deploy any containerised model as an autoscaling GPU endpoint, and the vendor claims sub-200ms FlashBoot cold starts with scale to zero. It's built for teams who want to bring their own container and choose how it runs.
Renting a machine versus calling a function
This is the bit that catches people out. The container-first cloud lets you rent a plain GPU Pod, an on-demand machine in one of 31 global regions, as well as running serverless endpoints. The code-first platform's docs only describe serverless execution, where it hosts everything and you never get a box of your own.
If you want a GPU machine of your own to keep running while you experiment, only one of these offers that. If the idea of managing a machine sounds like a chore, the serverless-only model is a relief rather than a limit. Both are also moving quickly: one has just launched a training API called Spindle, and the other's banner says Batch Inference is now generally available on Serverless. Check what's live before you plan around a feature.
Where the two split, line by line
| Area | Modal | RunPod |
|---|---|---|
| Workflow | Python functions with decorators, run from the command line; docs say zero configuration | Bring your container and deploy it as an autoscaling GPU endpoint |
| Products | Inference, Sandboxes, Training and Functions | Pods, Serverless, Clusters and a Hub |
| Always-on machines | Serverless execution only | On-demand GPU Pods across 31 regions, alongside Serverless |
| Sandboxes for agents | A headline product for coding agents and untrusted code | No sandbox product in the main navigation |
| Ready-made models | Featured model endpoints on the home page | A Hub of open-source models and templates |
| Latest change | New training API called Spindle | Batch Inference now generally available on Serverless |
Sandboxes are the quiet differentiator
If you're building a coding agent, or anything that runs code you don't fully trust, look hard at this row. The code-first platform makes Sandboxes a headline product and has written about scaling to a million concurrent sandboxes. The GPU cloud doesn't show a sandbox product in its main navigation. That doesn't mean you can't isolate work in a container, but it isn't a packaged product there.
For ready-made models, both have something. One lists featured model endpoints on its home page. The other has a Hub that deploys open-source models and templates in a few clicks. If you mainly want a popular open model running fast, either path works.
Advice by workload
For parallel Python jobs, batch processing written as functions, or agent sandboxes, start on the code-first platform. For a Docker image you want behind an autoscaling endpoint, or for a GPU machine you can rent and keep running, start on the container-first cloud. If your team splits both ways, it's fine to use each for what it's good at rather than forcing one to do everything. There's a wider list of other GPU clouds to compare if neither shape fits.
Five other GPU and inference platforms
Replicate
Best for: Ready-made models through one API, with public models for images, speech, music and video, plus fine-tuning and custom deployments.
Fireworks AI
Best for: Open-model inference and tuning, with a platform to run, ensemble and train open models; the vendor says 10,000+ companies trust it.
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Together AI
Best for: Teams wanting serverless, batch and dedicated inference plus raw GPU clusters, fine-tuning and sandboxes in one place.
Beam
Best for: Agent workloads and sandboxes on bare-metal servers turned into VMs, task queues, functions and endpoints.
Lambda
Best for: Larger GPU clusters, with instances, superclusters and 1-Click Clusters; its banner says it has a new address at lambda.com.
Do you make GPU cloud or AI inference software? Tool makers can ask to be considered for a featured place on this page.
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Frequently asked questions
Can I rent a plain GPU machine on Modal?
Its docs only describe serverless execution, where it hosts everything for you. If you want an on-demand GPU machine you control, the container-first cloud offers Pods for that.
Do I need Docker for either platform?
On the container-first cloud, you bring your container to deploy an endpoint. On the code-first platform, you write Python functions and the docs say there's zero configuration.
Which is better for AI coding agents?
The code-first platform has Sandboxes as a headline product for coding agents and untrusted code. The GPU cloud doesn't show a sandbox product in its main navigation.
Is there a free plan on either?
Neither site states a free plan.
Can I deploy an open-source model quickly?
Yes, on both. One lists featured model endpoints on its home page and the other has a Hub of open-source models and templates.
