Route AI requests across hosted models, open-source inference, and private endpoints with automatic fallback, cost-aware routing, usage controls, and observability.
Any model · Any provider · Any endpoint · One router
AI teams are no longer using one model from one provider. They're testing new models, managing multiple APIs, handling rate limits, comparing costs, routing around outages, and serving different workloads to different endpoints.
Without a routing layer, that complexity becomes application code.
Pipe gives teams one place to connect providers, route requests, monitor usage, control spend, and keep applications online when providers are slow, expensive, rate-limited, or unavailable.
Use one API across major model providers, open-source inference platforms, dedicated deployments, and custom endpoints.
Route requests based on cost, latency, quality, uptime, context length, region, provider availability, or internal business rules.
Keep applications online when a provider is down, degraded, rate-limited, or returning errors.
Set budgets, usage limits, model access rules, team-level policies, and routing preferences.
Track latency, cost, token usage, errors, fallbacks, provider performance, and routing decisions.
Route to private clusters, self-hosted models, dedicated endpoints, third-party inference APIs, or Pipe-managed providers.
Pipe AI Router is not just a catalog of models. It's infrastructure for production AI teams that need control over inference behavior, provider selection, reliability, and cost.
Marketplaces help you access models. Proxies help teams build their own gateway. Pipe gives teams a managed inference router connected to a global AI Cloud — operated, observed, and supported.
Route across providers without hardcoding provider-specific logic into your application.
Use fallback and policy routing to keep agents responsive and reliable.
Centralize provider access, usage visibility, team policies, and spend controls.
Route traffic to internal endpoints, dedicated clusters, or self-hosted models.
Send each request to the provider that best matches the workload, price, latency, and quality requirements.
Start with one provider or connect them all. Pipe gives your team the routing layer to scale AI applications with more control and less infrastructure work.