FastRouter.ai

Every supported model answers on the same OpenAI-compatible API, including Claude, Gemini, GPT, Grok, Veo and image models. FastRouter also accepts Anthropic Messages and Gemini Interactions formats, so a stack built on either one connects without a rewrite. Switching models is a string change in the request body.

Send fastrouter/auto and the router picks a model for that specific call. You can bias the choice toward cost, latency or throughput depending on what the feature needs. A support bot and a research assistant can share one integration and still land on different models.

When a provider returns an error or slows down, FastRouter retries and reroutes to a healthy provider serving the same model. Fallback lists add a second layer: you rank models in order, and traffic continues down the list when the first choice fails. Virtual model aliases let you rename a routing setup, so a later change does not touch application code.

Budgets attach to projects, keys and members. You can cap a single API key, give a contractor a read-only role, or hold a staging project to a fixed monthly limit. For AI cost control across an organization, this is the layer between an invoice surprise and a policy you can enforce. Alerts fire on spend spikes before the invoice arrives, which is the difference between a warning and an incident report.

Every request lands in a searchable log with latency, token count, cost and error status. Paid plans add distributed tracing and retention up to 120 days. Insights goes further by reading your own traffic and ranking changes, such as caching a repeated prompt or downgrading a model, with the sampled requests and the savings math attached.

The playground runs the same prompt against several models and shows latency, output and cost next to each other. Model council combines answers from more than one model to cross-check a result. Both are useful when a team argues about which model to ship with and nobody has data.