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 module dependencies, embedding

AI gateway rate limiting eliminates 429 errors by pooling provider keys, enforcing internal quotas, and failing over across providers before limits hit. 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

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. Full Bifrost playbook for platform teams. Claude Code has become the default terminal coding agent in most engineering organizations, and the bill is following. Anthropic's own enterprise data puts the average at $13

Cut OpenAI and Anthropic API bills 40 to 70 percent with semantic caching. Learn how Bifrost's gateway-level cache captures redundant traffic at scale. OpenAI and Anthropic bills are growing faster than traffic for most teams shipping LLM features. Claude Opus 4.7 costs $5 per million input tokens