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LLM Latency Tracker

Official registry AI and agents

Measured latency, time to first token and uptime for ~45 AI inference APIs, by region.

Source code Website Documentation

What can you do with LLM Latency Tracker?

  • 🌍 Regional measurements — compare provider response times and availability from distributed probe nodes.
  • ⏱️ Two probe types — network time to first byte (TTFB) and optional inference time to first token (TTFT).
  • 📈 Open dataset — daily rankings, historical snapshots and regional p50 / p95 statistics.
  • 🤖 Agent access — JSON API, MCP, Markdown pages and an llms.txt index.
  • 🗓️ Model lifecycle — a deprecation calendar with links to provider announcements.

Install

Add this to your client's MCP configuration (Claude Desktop: claude_desktop_config.json; Cursor: .cursor/mcp.json; VS Code and others use the same "mcpServers" shape).

Remote (streamable-http)
Claude Code 
claude mcp add --transport http llm-latency-tracker 'https://llmlatency.dev/mcp'
JSON configuration (Claude Desktop, Cursor, VS Code) 
{
    "mcpServers": {
        "llm-latency-tracker": {
            "type": "http",
            "url": "https://llmlatency.dev/mcp"
        }
    }
}

Documentation

From the project's README on GitHub, refreshed weekly. Links open on GitHub.

Contents
  1. 📊 LLM Latency Tracker
  2. ⚡ What it does
  3. 🔧 Built for measurement
  4. 🚀 Quick start
  5. 🧪 Contributing

📊 LLM Latency Tracker

Measure AI API latency and uptime across regions, and publish the results as open data.

🌐 Live rankings · JSON API · MCP endpoint

⚡ What it does

  • 🌍 Regional measurements — compare provider response times and availability from distributed probe nodes.
  • ⏱️ Two probe types — network time to first byte (TTFB) and optional inference time to first token (TTFT).
  • 📈 Open dataset — daily rankings, historical snapshots and regional p50 / p95 statistics.
  • 🤖 Agent access — JSON API, MCP, Markdown pages and an llms.txt index.
  • 🗓️ Model lifecycle — a deprecation calendar with links to provider announcements.

🔧 Built for measurement

  • Python standard-library core for edge probes; provider keys are needed for inference probes.
  • SQLite storage and regional aggregation, with remote nodes shipping measurements to a central node.
  • Static publishing for the website and JSON datasets.
  • CI checks for tests and linting; published snapshots include their measurement date.

Reading the results: edge TTFB measures network/API responsiveness, not model generation speed. Compare it separately from inference TTFT; results depend on region, probe type and time window.

🚀 Quick start

Use the public data without installing anything:

curl https://llmlatency.dev/api/rankings.json

Or run edge probes locally with Python 3.12+:

git clone https://github.com/mazamaka/llm-latency-tracker.git
cd llm-latency-tracker
REGION=local python3 run.py
python3 aggregate.py --region local

For a local stdio MCP server over the published API (stdlib only, no keys):

python3 mcp_server.py

Tools: get_ai_api_latency (provider leaderboard per region), get_provider_latency (one provider across regions), get_model_deprecations (model shutdown calendar), get_coding_agent_health (Claude Code / Codex speed and accuracy drift). All read-only.

Inference probes, API access & site generation → · Deployment →

🧪 Contributing

Add a provider, bring a new probe region, or improve the measurement code. See CONTRIBUTING.md for setup and checks.

Python · SQLite · JSON API · MCP · Cloudflare Pages

Code: MIT · Data: CC-BY-4.0, with attribution to llmlatency.dev.

📈 Latest daily snapshot

Daily snapshot — 2026-10-26

Measured latency across 46 AI inference providers in 4 regions. Method: distributed edge (DNS→TCP→TLS→TTFB) + inference (TTFT) probes, last 24h. License: CC-BY-4.0.

RegionFastest provider (p50)p50p95Uptime
Asia (Tokyo)fireworks24 ms99 ms100%
Europe (Germany)openrouter98 ms201 ms100%
South America (São Paulo)writer56 ms85 ms100%
US (Central)fireworks28 ms77 ms100%

Snapshot generated 2026-10-26T07:24:17Z — this table is regenerated daily.

Frequently asked questions

What is LLM Latency Tracker?

Measured latency, time to first token and uptime for ~45 AI inference APIs, by region.

How do I install LLM Latency Tracker?

It runs as a remote server. In Claude Code: claude mcp add --transport http llm-latency-tracker 'https://llmlatency.dev/mcp'. For other clients, paste the JSON configuration from this page into their MCP settings.

Does LLM Latency Tracker need an API key?

Its registry entry lists no required key or secret. Some features may still need an account with the service it connects to.

Is LLM Latency Tracker open source?

Yes. Its code is on GitHub under the MIT license.

Who maintains LLM Latency Tracker?

It is published by mazamaka. AI.tn lists it but does not maintain or endorse it.

From the official MCP Registry. Listing is not an endorsement: review a server's code and permissions before giving it access to your data.