Pick Replicate if you want to call existing image, video, speech or language models through an API, and maybe push one custom model of your own. Pick Modal if your team writes its own Python pipelines, training jobs or agent sandboxes and wants control over containers, scaling and security settings. One is a model menu, the other is programmable infrastructure.
Picture two product teams. The first wants to add image generation to an app by Friday, and nobody on the team wants to look after a GPU. The second has its own model code, a training job that runs overnight and an AI agent that needs somewhere safe to run untrusted code. Both teams will end up comparing these two platforms, and they'll reach opposite answers. Which team looks more like yours?
The honest advice, before the detail
If you're the first team, choose Replicate. You pick a model from its public library and call it from Node, Python or plain HTTP, and its docs cover pushing a custom model when you need one. If you're the second team, choose Modal. You write Python, run it on cloud GPUs and define functions, images, volumes, scheduled jobs and clusters in code. There's no single winner here, only a better match for how your engineers like to work.
Where they overlap, and where they part ways
Here's what makes this pairing confusing. Both will happily serve AI inference on GPUs without you renting a machine full time, so on a quick skim they look interchangeable. They're not. One is closest to buying an API for models someone else has already published. The other is closest to renting programmable infrastructure where you bring your own code.
That difference shows up everywhere. The model API's docs talk about predictions, deployments, webhooks and organisations. The infrastructure platform's site talks about a rebuilt container runtime, storage and what it calls a multi-cloud capacity pool. It also builds Sandboxes for coding agents and untrusted code, and there's nothing that matches that on the model API side. If you're building agents, that one product line may decide the whole thing.
Feature by feature
| What you're weighing | Replicate | Modal |
|---|---|---|
| Starting point | Pick a model from the library, call it from Node, Python or HTTP | Write your own Python and run it on cloud GPUs with modal run |
| Model catalog | Public gallery of official and community models, with run counts | Featured model endpoints and a supported models list, but infrastructure leads |
| Custom code | Deploy a custom model, push your own via publishing guides, GitHub Actions and CI/CD | Functions, images, volumes, scheduled jobs, web endpoints and clusters defined in code |
| Product surface | Predictions, deployments, webhooks and organisations | Inference, Sandboxes for agents and untrusted code, Training and multi-node clusters |
| Training | A guide to fine-tuning an image model | Training section with multi-node training and a Tinker-compatible API in beta |
| Security and data | Docs cover data retention, safety checking and API tokens | Docs cover data residency, audit logs and customer-supplied encryption keys |
Three themes that matter in practice
How much code you'll write
With the model API, the vendor says you can run and fine-tune models, or deploy custom ones, with one line of code. That's the pitch: very little plumbing between you and a result. The infrastructure platform expects more of you. You'll define your environment, storage and endpoints in Python, which is more work up front but gives you far more control later on.
Training and fine-tuning
If training is a small part of your plans, the model API's guide to fine-tuning an image model may be enough. If you're training regularly, the infrastructure side goes much further, with multi-node training and a Tinker-compatible API that's still in beta. Beta means you should expect some change, so don't hang a production deadline on it.
Security questions from your customers
This is the theme people forget until a big customer sends a questionnaire. The model API's docs cover data retention, safety checking and API tokens. The infrastructure platform's docs cover data residency, audit logs and encryption keys you supply yourself. If you sell to larger companies with strict data rules, that second list will come up in sales calls, so check it against what your buyers ask for.
Still working out which kind of GPU setup you need? Our guide to the best serverless GPU platforms compares more options that skip the full-time machine, and the list of cloud GPU providers covers renting the hardware directly.
Three more platforms on the shortlist
Together AI
Best for: Teams running open-source models at scale, with inference, post-training and compute in one AI cloud.
RunPod
Best for: Developers who want rentable GPUs, with on-demand Pods across 31 global regions plus serverless endpoints and open-source templates.
fal
Best for: Developers building generative media features, with model APIs and serverless compute aimed at image, video and audio.
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Questions to put to your engineers before signing up
Five questions will tell you which camp you're in. Ask them out loud in your next planning meeting, and the answer usually falls out of the conversation.
- Are we calling a model someone else published, or running code we wrote ourselves?
- Will we train or fine-tune more than once in a while?
- Do we need a safe place for an AI agent to run untrusted code?
- Who on the team is happy looking after containers, storage and scaling settings?
- Will our customers ask about data residency, audit logs or their own encryption keys?
Mostly the first half of each question? Start with the model API. Mostly the second half? The infrastructure platform is the better fit.
Do you make an AI inference or GPU platform? Tool makers can ask to be considered for a featured place on this page.
Want your tool on this list? Featured spots and reviews here are paid partnerships. Here's how to partner with me.
Frequently asked questions
Is Replicate easier to start with than Modal?
For calling ready-made models, yes. You pick a model and call it from Node, Python or HTTP. The other platform expects you to write and run your own Python code.
Can I run my own model on Replicate?
Yes. Its docs cover deploying a custom model and pushing your own through publishing guides, GitHub Actions and CI/CD.
Does Modal have a model library?
It lists featured model endpoints and supported models, but its main pitch is infrastructure for your own AI code rather than a public gallery.
Which one suits AI agents?
The infrastructure platform builds Sandboxes for agents and untrusted code. There's no matching product on the model API side.
Which has more security controls in its docs?
One covers data retention, safety checking and API tokens. The other covers data residency, audit logs and customer-supplied encryption keys.
