Pick Hugging Face if you want to find, share and fine-tune open models, datasets and demo apps, or you need team controls such as SSO and audit logs. Pick Replicate if you mainly want to call image, video, audio or text models from your own app with very little setup. Plenty of teams end up using both, one for discovery and one for shipping.
Here's a quick way in. Picture the moment your product needs a model. Are you still working out which model, or have you already chosen and just need it running behind a button? If you're still exploring, you'll spend most of your time in a catalogue. If you've chosen, you'll spend it writing a few lines of code that call an API. These two services started at opposite ends of that journey, and they've both been moving towards the middle.
A library with a server room, or a single API?
The old shorthand was simple: one is where models live, the other is where you run them. That's no longer the whole story. The hub has grown a hosted-inference side of its own. Inference Providers gives you 45,000+ models through one API, and dedicated Inference Endpoints let you run a model on its own machine. That overlaps directly with what the API service does.
The API service has stretched too. Its docs include guides for running models from Cloudflare Workers, which shows it sits comfortably inside serverless stacks. So the fair split today isn't storage against compute. It's catalogue and community on one side, and a clean call-a-model API with Official models on the other.
For the rest of this page I'll call them the hub and the API service, since that's still the clearest way to tell them apart.
Hub against API, point by point
| Area | Hugging Face | Replicate |
|---|---|---|
| Core job | Community hub for hosting and collaborating on models, datasets and Spaces apps | API for running models in your app, with a Playground to compare them |
| Running models | Inference Providers (45,000+ models via one API) and dedicated Inference Endpoints | Run any listed model through its API; push your own model or publish with GitHub Actions |
| Open source | Maintains the Transformers and Diffusers libraries and a large open model library | Docs include an open source section and client libraries; the product itself is an API service |
| Catalogue | 2M+ models, much of it community uploads, with trending lists | Home page features Official models from OpenAI, Black Forest Labs, Google and ByteDance, with run counts |
| Team features | Team and Enterprise with SSO, audit logs, resource groups and private datasets | Organizations, API tokens and prepaid credit billing in its docs |
| Beyond inference | Datasets, Spaces apps, leaderboards, Storage Buckets and learning resources | Predictions, deployments, webhooks and streaming output |
Finding a model you can rely on
The hub's catalogue is enormous. Its site says you can browse more than two million models, 500,000+ datasets and over a million applications. Much of that is community uploads, which is both the strength and the snag: you'll find almost anything, but you'll also need to judge quality yourself. Trending lists and leaderboards help you narrow it down.
The API service takes a curated route on its home page. It features Official models from names such as OpenAI, Black Forest Labs, Google and ByteDance, and shows how many times each has been run. That's a handy signal if you'd rather pick from a shorter, well-known list than dig through a library. It's also why it's popular for image and video features, where a handful of big models do most of the work.
Shipping it inside your product
This is where the API service feels at home. Its pitch is running, fine-tuning and deploying models with one line of code, from Node, Python or plain HTTP. The docs cover predictions, deployments, webhooks and streaming output, which are the pieces you need when a model sits behind a feature your customers use. If you've built your own model, you can push it, or publish it using GitHub Actions.
The hub's answer is Inference Providers for quick access to a wide range of models and Inference Endpoints when you want a dedicated deployment. It's a capable route, and it means you don't have to leave the place where you found the model. If you want to compare more services built purely for this job, our guide to AI inference platforms lines them up.
Teams, logins and governance
If you're rolling this out across a company, the hub says more about controls. Its Team and Enterprise options list SSO, audit logs, resource groups and private datasets. That's the kind of list a security or IT lead will ask about before signing anything.
The API service's docs cover Organizations, API tokens and prepaid credit billing. That's enough for a product team sharing access, but it doesn't describe the same depth of governance. If those controls are on your checklist, they're worth raising with both sales teams. For tooling around the whole model lifecycle, our list of MLOps tools goes further.
Using both at once
You don't have to choose only one. A common pattern is to explore and fine-tune on the hub, where the open libraries and datasets live, then call a finished model through whichever API your app already uses. Because the hub now offers hosted inference too, some teams will keep everything in one place. Others will like the API service's shorter list and simple calls. Neither is wrong.
Four more places to run AI models
Modal
Best for: Serverless AI infrastructure for engineers deploying their own code, with GPUs it says spin up in seconds for inference, training and sandboxes.
Together AI
Best for: An AI cloud for running open models in production, combining serverless and dedicated inference with GPU clusters and post-training.
Want AI doing the heavy lifting in your marketing?
I build the systems that handle the boring 80 percent, so you get your week back. Done properly, with the human kept in.
fal
Best for: Developers building generative media features, with 1,500+ model APIs for image, video, audio and 3D through one unified API, by its own account.
OpenRouter
Best for: One API that routes to 600+ models across 80+ providers, with custom data policies over which providers see your prompts.
If you're mainly weighing up options to the hub, our page on other model hubs and hosting services covers more of them.
The verdict for developers and ML teams
Choose the hub if your work is finding, sharing and fine-tuning open models, datasets and demo apps, or if you need SSO, audit logs and private datasets for a wider team. Choose Replicate if you mainly want to call image, video, audio or text models from your own app with as little setup as possible, using Official models you'll recognise. Many teams will use both, and that's a sensible place to land rather than a failure to decide.
Do you make AI model hosting or inference tools? 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
Can Hugging Face run models through an API like Replicate?
Yes. The hub now offers Inference Providers, which gives access to 45,000+ models through one API, and dedicated Inference Endpoints. That overlaps directly with the API service.
Which is easier to add to an app?
The API service is built for that job, with one-line calls from Node, Python or HTTP, plus webhooks and streaming output. The hub's inference options work too, but its wider focus is the model library and community.
Can I deploy my own model on either?
Yes. The API service lets you push your own model or publish it with GitHub Actions. The hub lets you host models and run them on dedicated Inference Endpoints.
Which has better team and security controls?
The hub lists SSO, audit logs, resource groups and private datasets in its Team and Enterprise options. The API service's docs cover Organizations and API tokens.
Should I use both?
Many teams do: the hub for discovering and fine-tuning open models and datasets, and an API service for calling finished models from production code.
