Open-source AI gateways compared against managed platforms: options, self-hosting requirements, ownership trade-offs, the open-source versus managed matrix, and which teams fit which path.
Read more →AI gateway architecture explained through the request lifecycle: application, authentication, normalization, policy, model selection, routing, reliability, response, usage, and error handling.
Read more →Best AI gateways compared for 2026: providers, unified APIs, routing, fallbacks, cost visibility, and a selection framework to choose the right gateway for your stack.
Read more →AI gateway vs API gateway: learn how the two layers differ, where they overlap, when a traditional API gateway is enough, and when to add an AI gateway.
Read more →An AI gateway is a single API layer between your application and the AI models it calls. Learn what AI and LLM gateways do, why multi-model applications need one, and how routers, proxies, and API gateways differ.
Read more →A practical guide to open source LLM routers: what they are, why teams self-host them, the routing methods they support, what deployment and maintenance cost, and when open source beats managed routing.
Read more →A practical guide to choosing an LLM router: the evaluation criteria that matter, how routing flexibility and OpenAI compatibility compare, how to account for pricing and migration, and how to pilot a shortlist.
Read more →A practical guide to LLM fallback and failover routing: what triggers a fallback, how to design primary and secondary model chains, where retries backfire, and a production readiness checklist.
Read more →A practical guide to LLM routing strategies: what cost, latency, availability, task, budget, and rule based routing each solve, how static and dynamic routing differ, and how to combine signals into one decision.
Read more →A practical guide to LLM routers: what they are, how LLM routing works, the signals that drive model selection, the strategies at a high level, and when a router belongs in your stack.
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