AI Gateway

TL;DR * An enterprise LLM gateway centralizes routing, budgets, caching, failover, and usage attribution for every service that calls a model provider. * Cost control at the gateway comes mainly from caching and model routing; budgets and per-key model limits bound the bill, and attribution is what makes those decisions

TL;DR * LLM gateway security enforces prompt injection defense, PII protection, access control, and audit evidence once at the gateway, so every application and provider inherits the same policies. * Prompt injection is LLM01 and sensitive information disclosure is LLM02 in the OWASP Top 10 for LLM Applications 2025. * Bifrost guardrails

This ranked list compares Bifrost, OpenRouter, Kong, Cloudflare, and Amazon Bedrock on routing depth, virtual keys, budgets, RBAC, clustering, and in-VPC deployment.

A hands-on comparison of the five model routing tools engineers evaluate in 2026, ranked on routing mechanics: CEL routing rules, model aliasing, weighted key and provider distribution, fallback chains, and adaptive load balancing.

Claude Code talks to Anthropic models by default, and a gateway changes that by intercepting ANTHROPIC_BASE_URL. This guide compares five gateways, covers three routing patterns, and documents the defaults Claude Code changes behind a gateway.

TL;DR * Reliable AI in production depends less on model quality than on what happens when a provider call fails, because 429 rate limits and 5xx errors arrive on a routine basis. * Retries and fallbacks solve different problems: retries handle transient errors inside one provider, fallbacks switch providers once the