Inferbase vs RouteLLM
RouteLLM is an open-source router you host and calibrate. Inferbase is the managed alternative, routing and serving through one API.
Run it, or call it
Both route between models. RouteLLM is a router you self-host; Inferbase is a managed API.
Open-source, you run it
Free and open-source. The server, keys, and calibration are yours to run.
Managed, routes and serves
Routes across a catalog and serves. Nothing to install or calibrate.
A framework, or a platform
Free and self-hosted, versus managed and kept current.
RouteLLM is respected open-source research from LMSYS, the group behind Chatbot Arena: free, permissively licensed, peer-reviewed, with public training data. It is a framework, not a service.
- Binary strong-vs-weak, by design. It routes between one strong and one weak model you configure, not across a catalog of models.
- You operate it. Install the library, configure provider keys, calibrate the cost-quality threshold, and run the server yourself.
- It reads as a 2024 artifact. The last commit to its main branch was August 2024, with no releases; keeping routers current as models change is on you.
If you want a free, modifiable, self-hosted router and the engineering to run it, RouteLLM is a strong choice. If you want routing and serving managed and kept current, that is where Inferbase is built to win.
Side by side
Where the two line up and where they diverge. RouteLLM is strong open-source research, and we treat it so.
| Inferbase | RouteLLM | |
|---|---|---|
| Type | Managed platform, hosted | Open-source framework, self-hosted |
| Routing scope | Best model per task across a curated catalog | Binary strong vs weak model, per query |
| Execution | Routes and serves the model | Decides, then forwards to endpoints you configure (via LiteLLM) |
| Setup | Point one OpenAI-compatible API, model="auto" | Install, configure provider keys, run your own server |
| Threshold tuning | Managed, you set an objective | Manual calibration step you own |
| Per-request audit | One record: decision, model, tokens, cost, latency | Build your own observability |
| Customization | Custom model pools you define from the catalog | Retrain routers on your own preference data |
| Cost | Free to start | Free, Apache-2.0, you pay your own infra and model bills |
| Maintenance | Maintained, managed | Research artifact; last commit Aug 2024, no releases |
Reflects the publicly available project as of June 2026. Check the RouteLLM repository for the latest.
Where each one fits
An open-source router you operate, or a managed platform that routes and serves. Pick by what you want to own.
RouteLLM is the better fit when
- You want a free, open-source, self-hosted router with no provider
- You want full control, on-prem, or routing to local models for privacy
- Your use case is a clean two-tier, strong versus weak, cost split
- You have the engineering to operate it, calibrate thresholds, and study or modify the method
Inferbase is the better fit when
- You want routing and serving managed, with nothing to install or operate
- You want per-task selection across many models, not a single strong-weak split
- You want managed objectives instead of calibrating a threshold yourself
- You want a per-request audit record and one bill, kept current as models change
Frequently asked questions
Straight answers on how Inferbase and RouteLLM differ, and when each one is the better choice.
A self-hosted LLM router is a library or server you install and run yourself. It decides between models on a policy you calibrate, usually a threshold between a strong and a weak model, and you supply the providers, the keys, the hosting and the upkeep as models change. A managed router such as Inferbase runs that decision as a service, over a catalog it maintains, and serves the request as well.
A router decides which model should answer; a gateway executes the request and governs it with keys, limits, budgets, logs and failover. Some routers only recommend, leaving you to call the provider; some gateways only proxy to the model you named. Inferbase runs the router inside the gateway, so the decision is served rather than recommended and its record sits beside the usage receipt.
That the same call which chooses the model also runs it. You send model="auto" to one OpenAI-compatible endpoint; the router classifies the request, picks the model that clears the bar for the task on your objective, the gateway serves it with two fallbacks ready, and the response comes back with its decision on the record. There is no second system to operate for the inference and no second bill for it.
When a workload mixes tasks and difficulties, which most production traffic does. We publish no savings percentage: one was withdrawn in September 2026 because the baseline price it was measured against is no longer one we can substantiate, and a ratio is only as good as its denominator. What routing does is send each request to the cheapest model that clears the quality bar for it, so the gain is the gap between what you pay now and what the right-sized model costs. A workload that is all one hard task gains less; the routing page shows the run and its caveats, and Routing Preview measures it on your own traffic.
No. RouteLLM is an open-source framework from LMSYS, the group behind Chatbot Arena. You pip install it and run it on your own infrastructure; there is no hosted API or company behind it. Inferbase is a managed platform: you call one OpenAI-compatible API and we route and serve.
No. RouteLLM is a binary router: per query it chooses between one strong and one weak model that you configure. You can change which two models the pair points at, but each decision is still strong-versus-weak. Inferbase selects the best model per task across a curated catalog, not a single two-tier split.
No. It decides which model to use and forwards the call to endpoints you configure, through LiteLLM, with your own provider keys; you can run its OpenAI-compatible server yourself. Inferbase routes and serves, so there is no infrastructure or keys for you to manage.
They come from a rigorous paper, but they were measured on a specific model pair (GPT-4 Turbo versus Mixtral-8x7B) and specific benchmarks (MT-Bench, MMLU, GSM8K) in 2024. Treat them as that result, not a universal guarantee; your savings depend on your own models and traffic.
It looks like a 2024 research artifact: the last commit to its main branch was August 2024 and there are no published releases. It is excellent reference work, but keeping routers current as frontier models change would be on you. Inferbase is maintained as a managed service.
On evidence, then on your objective. Each request is classified for its task family and complexity; the catalog is narrowed to the models that clear a quality bar for that task on benchmark evidence and that fit the request; the survivors are ranked on the objective you set (cost, balanced, quality or latency). A model with no measurement for the task is treated as unknown and does not qualify on guesswork.
Yes. Every routed request writes a decision record: the task the classifier saw, its complexity, the objective, how many models qualified, which won and how clearly, and each fallback attempt if any. When streaming it is the first event on the stream; afterwards it is retrievable by request id from the API and the dashboard.
One line. Point an existing OpenAI SDK at the Inferbase base URL and send model="auto"; chat completions, streaming, tool calls and structured output pass through unchanged, and Anthropic-shaped clients are served on the Messages endpoint. Name a model instead of auto and it is served as named, so an application can pin where it must and route where it can.
Free during beta. The published plan meters routing decisions at $1 per 1,000 with the first 5,000 each month free (a pinned model is never charged a routing fee), and managed inference at the host's token price plus a flat 6%. Connect your own provider keys and the tokens are billed by your provider instead. New accounts start with $5 of credit.
Put intelligence in the middle.
One gateway in front of every model, with your policies applied and every decision on record. Start with $5 of credit and 5,000 routing decisions a month, no card required.