Compare All AI Gateway & LLM Proxy Software 2026

An AI gateway (or LLM proxy) sits between your application and one or more model providers. It centralizes API keys, routes requests across models and vendors, enforces rate limits and budgets, caches responses, and logs every call for observability. It gives teams a single control plane for cost, reliability, and governance across all their LLM usage.

Open-source proxies like LiteLLM are free to self-host. Managed gateways usually offer a free developer tier and then charge by request volume, logged tokens, or seats, scaling to enterprise plans for SSO, SLAs, and advanced governance. The gateway fee is typically small relative to the underlying model token spend it helps you control.

A gateway adds failover between providers, semantic caching to cut duplicate token spend, unified logging and analytics, centralized key management, and per-team budget limits. These reduce cost and outages and make it far easier to swap or load-balance models without changing application code.

The main hidden cost is the added latency and infrastructure of self-hosting a proxy at scale, plus log storage for high request volumes. Managed tiers can meter on logged tokens, so verbose logging on a high-traffic app can raise the bill. Most teams find the savings from caching and provider failover outweigh these costs.