Every AI request has a different best model.Inferbase finds it automatically.
Measured on 100 prompts: $11.25/1M on Claude Opus 4.8 to $2.87/1M routed, 62 points from switching models and 13 from routing. See the run
Works with every major model provider.
Every request is
evaluated independently.
The cheapest model isn't always the right one. The smartest model isn't always necessary. Inferbase finds the best option for every request in milliseconds.
Try in the PlaygroundSend a message to start
- requestmodel = "auto"any OpenAI-compatible client
- analyzeTask + complexityread from the prompt itself
- filter64 → 38 modelsavailable, capable, context fits
- scoreQuality · cost · latencyevidence per task family
- serveOne model streamsdecision disclosed inline
Every routing decision
is explainable.
Know exactly why each request went to the model it did, before the first token arrives, and pull any past decision from the audit trail afterward.
Trust matters.
taskWhat kind of work the prompt is: code, analysis, extraction, chat.
complexityHow demanding this specific prompt is, measured from the text itself.
decisivenessHow clear-cut the winning model was over the runner-up.
modelThe exact model, variant, and provider that served the request.
Most AI applications
waste inference spend.
Static model selection leaves money on the table. Inferbase continuously routes each request to the cheapest model that still clears the quality bar for the task.
Run a routing preview90 of 100 moved to a cheaper model at projected equal-or-better quality. The rest stayed put.
Switching once off the baseline captures 62 of those points on its own; per-prompt routing adds the other 13.
A real run: 100 mixed-difficulty prompts against a Claude Opus 4.8 baseline, 2026-07-06. Your own prompts and baseline will give a different number. How this was measured
Everything needed to
run production AI.
The routing layer, plus the operations you need around it.
Intelligent Routing
Optimize every request automatically.
Instant Failover
Stay online when providers don't.
Unified API
One endpoint. Every provider we serve.
Cost Intelligence
Track exactly where every token goes.
Traffic Analytics
Understand how your applications actually use AI.
Continuous Optimization
Your routing improves as new models become available.
Why Inferbase
More than another AI gateway.
Optimize
Lower costs automatically.
Observe
Understand every request.
Adapt
New models. New pricing. New providers. Without rewrites.
Scale
From prototypes to billions of tokens.
How Inferbase compares
Routing brains tell you which model; aggregators resell routing; frameworks make you host it. Inferbase routes and serves, end to end.
| Inferbase | OpenRouter | LiteLLM | Portkey | NotDiamond | RouteLLM | |
|---|---|---|---|---|---|---|
| Picks the best model per request | First-party | Via NotDiamond add-on | Beta tiers you map by hand | No, rules you define | Yes | Strong vs weak only |
| Routes and serves in one API | Yes | Yes | Proxies via your providers | Proxies via your providers | No, you run it | No, self-hosted |
| Per-request decision audit | Yes | No | Logs and cost tracking | Deep logs and traces | Recommend-side | Build your own |
| Nothing to self-host or calibrate | Yes | Yes | No | Hosted, rules are yours | Yes | No |
| Model breadth | Curated catalog of open models | Hundreds of models | 100+ providers, your keys | 1,600+ models, your keys | Your chosen pool | Two models |
A unified API where routing is a NotDiamond-powered add-on.
Read the comparisonRecommends a model per prompt; you run the inference.
Read the comparisonA self-hosted, strong-vs-weak router you operate.
Read the comparisonFrom the blog.
Benchmarks, cost analysis, and the thinking behind how we build.
LLM Sampling Parameters Explained: Temperature, Top-p, and Friends
What temperature, top_p, top_k, penalties, and seeds actually do to LLM output, which defaults to trust, and which knobs deserve adjustment for each task.
Fine-Tuning vs RAG vs Prompt Engineering: Which One Fixes Your Problem
The three ways to adapt an LLM solve different failure modes. A decision framework for choosing between prompting, retrieval, and fine-tuning, with costs.
What Is an LLM API? How Applications Talk to Language Models
How LLM APIs work: the request and response anatomy, tokens as the billing unit, streaming, API keys, and why one wire format became the standard.
Your AI stack shouldn't stand still.
Every month new models become cheaper, faster, and more capable. Inferbase ensures your application automatically benefits without changing a single API call.