Guide
Nivana vs LiteLLM
LiteLLM gives you one OpenAI-style interface to many providers, on your own infrastructure, with the model chosen by you. Nivana chooses the model for every request and runs the whole thing for you.
The short version
- Choose LiteLLM if you want an open-source proxy you host yourself, your own provider keys and contracts, and full control over which model serves which call, with load balancing and fallbacks you configure.
- Choose Nivana if you want the best measured model for each request without choosing or maintaining a model list, nothing to deploy, and a response that says which model answered and why.
How they differ
| Nivana | LiteLLM | |
|---|---|---|
| What it is | A hosted router and chat app | An open-source Python SDK and proxy server you run |
| Picks the model | Always, per request, from measured quality, live price and speed | You do, per call or per model group in config |
| Keeping up with new models | Measured weekly; the pool changes when a model wins | You add and test them yourself |
| Fallbacks | The next two models in the ranking for that request | Rules you write: retries, fallbacks, load balancing across deployments |
| Provider accounts | None needed: one Nivana key | Your own key for every provider |
| Data retention | Zero data retention requested on every call | Whatever your provider contracts say; you control the logs |
| Pricing | Pay as you go: the routed model’s live price plus a 50% fee | Free to self-host; you pay providers directly (an enterprise offering exists) |
Switching
Nivana speaks the OpenAI API, so switching is a base-URL change: point your OpenAI SDK at https://nivana.one/v1, keep the code, and the model string is accepted and ignored because Nivana routes. See migrating and the quickstart. You start with 2,000 free credits, enough to compare answers on your own prompts first.
What LiteLLM does better
Control and ownership. If you must run inside your own network, use your own negotiated provider contracts, serve self-hosted models, or pin exact models for compliance, a proxy you run is the right tool. Nivana is hosted, keeps a small pool it measures, and never lets you pin a model. Some teams use both: LiteLLM in front of their stack, with Nivana as one OpenAI-compatible upstream for the requests where they want the router to choose.
Or let the router pick, per request
Nivana sends every message to the model that scores best for it, sets how hard it thinks, and tells you which one answered. 2,000 free credits to start, no card.