Kamya Shah

A step-by-step migration guide from LiteLLM to Bifrost, the AI gateway with native semantic caching, dual-layer hit matching, and zero application code changes. Teams running LiteLLM in production often hit the same wall: response caching works in isolation, but wiring semantic caching into the proxy introduces Redis

Every LLM application runs into the same production wall: provider rate limits. A spike in traffic, a long context window, or a runaway agent loop trips a requests-per-minute or tokens-per-minute ceiling, and the API returns a 429 Too Many Requests error. Exponential backoff delays the problem

Smart LLM routing in production cuts cost and latency by matching each request to the right model. Learn how Bifrost routes at the gateway layer. Every production AI application eventually hits the same wall: the team picked one default model, costs keep climbing, and latency on simple requests is indistinguishable

Govern Claude Code usage across engineering teams with virtual keys, hierarchical budgets, and tool filtering. Bifrost is the best choice for enterprises running mission-critical AI workloads that require best-in-class performance, scalability, and reliability. Claude Code has become the default terminal coding agent in most engineering organizations, and