There was no independent, measured view of AI-API latency by region — so I built one

An engineer built LLM Latency Tracker, an independent, provider-neutral tool that measures AI API latency and uptime from four global regions. The open-source project covers ~45 providers and is designed to be consumed by both humans and AI agents, with a JSON API and MCP tool support.

If you build anything on top of hosted AI APIs, latency isn't a detail you get to ignore — it's a feature. A sluggish time-to-first-token is the difference between an assistant that feels alive and one that feels broken. Yet when I went looking for an honest answer to a simple question — how fast is provider X from where my users actually are? — I couldn't find one. The numbers people quote tend to come from a single machine in a single region usually somewhere in the US , from vendor-reported status pages, or from a benchmark that got run once and never refreshed. There was no independent, measured , regional view. So I built one: LLM Latency Tracker , a provider-neutral tracker of latency and uptime for AI inference APIs. The core idea is boring on purpose: actually measure, don't scrape. A small Python prober — standard library only, no API key needed for the edge probes — opens real connections to each provider's endpoint and times every phase of the handshake: DNS resolution → TCP connect → TLS negotiation → time-to-first-byte TTFB . That's the edge view: how long the network path itself takes before a single byte comes back. Separately, where possible, it measures inference time-to-first-token TTFT — the thing your users actually feel, i.e. how long after you hit "send" the model starts streaming. Those two numbers answer different questions, and keeping them apart matters. Edge latency is about the network and the front door; TTFT is about the model and the queue behind it. The probes run from four regions — Europe Germany , US Central, Asia Tokyo , and South America São Paulo — because "fast" is meaningless without "from where." Results land in a SQLite time-series; a static-site generator turns that into the pages you see, hosted on Cloudflare Pages, and the whole thing is self-updating on a schedule. There's no always-on backend to rot or page me at 3am. It currently covers ~45 providers — the usual Western labs OpenAI, Anthropic, Google, Mistral, Groq, Together, Fireworks, Cerebras, OpenRouter, Perplexity, xAI, DeepSeek… plus Chinese ones GLM/Zhipu, Kimi/Moonshot, Qwen, MiniMax that rarely show up in Western benchmarks. All of it is free and open under CC-BY-4.0 , with a JSON API and an OpenAPI spec. Here's what makes this more than another dashboard. I built it to be read by machines , not just humans. The way people ask questions is shifting from search boxes to answer engines and agents. If your data only exists as HTML wrapped in JavaScript, an agent can't reliably consume or cite it — which means your measurements never reach the place where someone actually asks "which provider has the lowest TTFT in Europe right now?" So the site speaks several agent dialects: /mcp , exposing a get ai api latency tool. An agent can call it directly and get structured latency data back — no scraping, no guessing. /.well-known/mcp/server-card.json , plus llms.txt and llms-full.txt text/markdown and you get tidy Markdown instead of a DOM you have to reverse-engineer.The nice side effect is that building for agents forces you to build clean for everyone. The same discipline that makes a page legible to an LLM — structured, fast, no junk — is what earned a Lighthouse 100 and Cloudflare's "Agent-Native" Level 5 rating on their "Is Your Site Agent-Ready?" check. I won't pretend that means much on its own; it's a checklist, not a trophy. But it's a decent proxy for "this data is actually reachable." There's also a small companion: a model deprecation & migration calendar at /deprecations , hand-verified from official provider docs, so you can spot which models are being sunset before they break your app. A tracker that oversells itself is worse than none, so — plainly: It's a baseline and a starting point, not gospel. It's free, open-source, and the data is CC-BY-4.0 — use it, chart it, cite it, build on it. If a provider or region you care about is missing, open an issue or a PR — that's exactly the kind of contribution that makes this more useful for everyone. And if you're building agents, point one at the MCP endpoint and tell me what breaks.