The deciding difference is operational: LiteLLM can run on infrastructure you control, Hugging Face cannot. If that constraint is real for you, it settles the choice before anything else is considered.
One OpenAI-compatible endpoint routed across Groq, Together, Fireworks, Cerebras and Replicate — with the open-weight catalogue behind it.
Two modes: let Hugging Face route and bill, or bring your own provider key and use it purely as a client. Inference Endpoints is the sibling product when you want a dedicated scale-to-zero GPU instead.
Self-hosted proxy that speaks one API to 100+ providers, with keys and budgets.
Give every agent its own virtual key so you can attribute and cap spend per surface.
| Hugging Face | LiteLLM | |
|---|---|---|
| Licence | Proprietary | Open source |
| Pricing | freemium | open source |
| Self-hostable | No | Yes |
| Languages | TypeScript, Python | Python, TypeScript |
| Install | pip install huggingface_hub | pip install 'litellm[proxy]' |
| Keys required | HF_TOKEN | None |
The deciding difference is operational: LiteLLM can run on infrastructure you control, Hugging Face cannot. If that constraint is real for you, it settles the choice before anything else is considered. Hugging Face: One OpenAI-compatible endpoint routed across Groq, Together, Fireworks, Cerebras and Replicate — with the open-weight catalogue behind it. LiteLLM: Self-hosted proxy that speaks one API to 100+ providers, with keys and budgets.
Hugging Face is a hosted service only. LiteLLM can run on your own infrastructure.
Hugging Face is proprietary (freemium). LiteLLM is open source (open source).
Every page here answers to Accept: text/markdown and returns the same content at roughly a tenth the tokens. No separate site, no toggle — same URL.
curl -s -H "Accept: text/markdown" https://newagent.build/compare/huggingface-vs-litellm