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Adaptive load balancing shifts LLM traffic across providers and API keys based on live error rates and latency. This guide compares six gateways, including Bifrost, Kong AI Gateway, LiteLLM, and OpenRouter, on the load-balancing mechanics that decide uptime.

Enterprises running several LLMs need a routing layer that decides which model and provider serve each request. This guide compares six model routing tools across AI gateways, cloud-native routers, and open-source learned routers.

A guide to choosing an AI gateway for Claude Code: six evaluation criteria, per-developer budgets, the configuration, and the defaults Claude Code changes when ANTHROPIC_BASE_URL points at a gateway.

TL;DR * Evaluate an LLM gateway for enterprise scalability on eight criteria: performance overhead, failover, horizontal scaling, governance, security and compliance, deployment flexibility, observability, and extensibility. * Benchmark added overhead, success rate, and tail latency at your target requests per second on the instance size you plan to run, not average

RBAC for LLM apps runs on two planes: who can administer the gateway, and what each application, team, or agent may call through it. This guide compares six AI gateways on both, including agent tool permissions.

Gateway caching runs in two modes: direct hash matching with no embeddings, and semantic matching by meaning. This guide compares five gateways, explains what each mode costs, and shows which workloads see real savings.