Routes AI model requests across providers, centralizing failover and cost control.
Product memo
AI teams use routing.run to manage multiple AI model providers through a unified endpoint. It abstracts away provider volatility in model quality, pricing, and uptime, allowing teams to change providers or fallback orders without code changes. This positions it as a critical infrastructure layer for production AI, ensuring stability and cost predictability.
For who
AI teams and production engineers
Solves what
Centralized AI model routing, failover, and cost control
- Single API endpoint
- Provider failover
- Privacy-first inference
In their own words
Model routing for production
One endpoint for multiple providers, fallback chains, and live routing control. Typically used by production teams of absolute necessity.
Commercial cues
Model
usage based
Free tier
Yes
Trial
No
Pricing Strategy
- • A free tier lets teams test the API shape and validate route.
- • Request-based pricing provides predictable costs, avoiding token-based volatility.
Operator context
Operating setup
Founded
May 2026
Platform
API
Audience
Developers
Social footprint
Tech stack
Builder Strategy
- Strategy Type
- Niche Specialist
- Stage
- Pre Revenue
- Effort
- Solo Buildable
About Routing Expand
Routing.run provides AI teams and production engineers with a centralized product for managing AI model requests. It offers a single endpoint to route traffic across multiple providers, handling failover and cost control.
This approach abstracts away the inherent volatility in model quality, pricing, and uptime from individual providers. Teams can modify providers or fallback orders without altering their code, making it a crucial infrastructure layer for maintaining stability and predictability in production AI environments.
The service also includes observability and privacy-first inference, supporting open-source infrastructure for greater control.