RouterPlus

Teams building with multiple AI models today face a choice between many providers, each with its own API, pricing, and performance profile that may not match their actual workload. Ranking models against global benchmarks does not tell a team which model performs best on its own tasks, such as support triage, extraction, code review, or agents. Without a way to score candidate models against production traffic and a defined quality bar, teams risk deploying a model that looks good generically but underperforms on their specific use case. Switching providers or upgrading models also raises questions about data handling, existing contracts, and whether a new setup might perform worse than the current one.

RouterPlus provides a single API key that routes requests across a catalog of closed and open-weight models, with options to connect a team's own provider accounts (bring-your-own-key) or use prepaid marketplace credits. Routing decisions are based on a customer's own evals and production traffic scored against a quality bar, rather than global benchmarks, as illustrated by an example scoring several models against a 0.92 threshold and routing different task types to whichever models clear it. The Optimize feature compares candidate setups against a baseline on held-out eval cases and only marks a winner when it passes quality and cost checks, leaving the deployment choice to the customer and keeping earlier versions available. Existing OpenAI- or Anthropic-compatible clients can connect by changing the base URL and key, and the gateway does not store prompts or completions, only usage metadata, while provider-specific data policies still apply. Reserved capacity is available for teams anticipating shifts such as moving from closed to open source models, upgrading models, or adding a new provider.