Measuring AI Gateway Failover: 30 Days of Production Data

<p>How it works:</p> <p>Request arrives at an OpenAI-compatible endpoint<br /> Classifier detects task type + complexity<br /> Adaptive router picks the highest-scoring model for

<p>Your agent hits a rate limit. The fallback logic kicks in, picks an alternative model. Everything should be fine.</p> <p>Except the request still goes to the original model.

<h1> AI API Gateway </h1> <p>Stop hard-coding provider-specific API calls throughout your codebase. This gateway gives you a single unified interface to OpenAI, Anthropic, Google,

<p>You've heard of DevOps. You've heard of AIOps. You've probably heard of MLOps.</p> <p>But there's a category that doesn't exist yet — and it's the one the AI industry needs

<p>You've built the demo. It works. The LLM responds, the tools fire, the output looks great.</p> <p>Then you push it to production — and everything breaks.</p> <p>API calls fail

<p>In May 2026, a widely-discussed essay on Hacker News argued that <em>"the bottleneck was never the code"</em> — AI code generation has solved the coding bottleneck, but the

<p>57% of organizations estimate their data isn't AI-ready. General-purpose LLMs handle broad tasks well but hallucinate on specialized queries, miss domain jargon, and can't

<p>Vertex AI has been Google Cloud's AI development platform since 2021. On April 22, 2026, at Google Cloud Next in Las Vegas, Google retired it — not with a deprecation notice,