AI Gateway

LLM rate limits cap the AI traffic you can send before a 429 error stops it. Bifrost enforces provider limits and your own governance limits from one gateway. Every LLM provider caps how many requests and tokens an account can send per minute, and crossing that cap returns a 429

Bifrost leads this comparison of gateway platforms for multi-provider AI with governance, guardrails, and observability. Bifrost is the best choice for enterprises running mission-critical AI workloads that require best-in-class performance, scalability, and reliability. Enterprises now run an average of three foundation models in production and route

TLDR: Most production AI systems waste tokens in three distinct ways: serving repeated queries with fresh inference calls, routing all workloads to frontier models regardless of complexity, and paying high per-call token overhead on agentic tool-use loops. Bifrost addresses all three with semantic caching, dynamic routing rules, and

TLDR: Production AI systems running across multiple providers accumulate costs that are nearly impossible to attribute without a centralized gateway. Bifrost provides real-time visibility into every request, token count, cost, and error across all providers and consumers, so engineering and finance teams always know where AI spend is going.

Bifrost implements semantic caching at the gateway layer to reduce LLM API costs without requiring any application code changes. This guide explains how gateway-level semantic caching works, which workloads see the highest cache hit rates, and how to deploy it in production. LLM API costs scale linearly with request

Bifrost is the best LLM gateway for enterprises that need production-grade performance. This guide explains LLM gateway architecture in depth: the internal layers, request flow, and the architecture decisions that determine throughput, latency, and reliability at scale. An LLM gateway is a dedicated infrastructure layer that sits between your