Datasets:

llmlatency
/

llm-latency-tracker

license: cc-by-4.0
language:
  - en
tags:
  - llm
  - latency
  - benchmark
  - inference
  - observability
  - api-monitoring
pretty_name: 'LLM Latency Tracker: latency and uptime for AI inference APIs'
size_categories:
  - 1K<n<10K
configs:
  - config_name: daily_aggregates
    data_files: daily_aggregates.csv
    default: true

LLM Latency Tracker

Independent, continuously measured latency and availability for AI inference API providers, aggregated by day. Covers 45 providers across 4 regions (ap-tokyo, eu-hetzner, sa-east, us-central), built from 1,347,917 raw probes collected between 2026-07-23 and 2026-08-18.

Live rankings and full methodology: llmlatency.dev

How the numbers are produced

Probes run every five minutes from separate network locations and are never routed through a gateway or an aggregator, so the numbers describe the providers themselves rather than a proxy in front of them.

Network probe — DNS → TCP → TLS → time to first byte (ttfb).
Inference probe — time to first token on a real completion request (ttft).

These are different quantities, an order of magnitude apart, and they are never mixed in one ranking. Percentiles are nearest-rank, identical to the ones shown on the site.

Files

file	contents
`daily_aggregates.csv`	one row per date × provider × region × probe type, with p50/p95, sample count and success rate
`rankings.json`	machine-readable snapshot published live at llmlatency.dev

Usage

from datasets import load_dataset

ds = load_dataset("llmlatency/llm-latency-tracker", split="train")
ds.filter(lambda r: r["region"] == "eu-hetzner" and r["probe_type"] == "network")

Or without this dataset at all — there is a client on PyPI:

pip install llmlatency
llmlatency fastest

Limitations, stated up front

Vantage points are cloud data centres, not consumer networks, so absolute values are lower than an end user would see — the comparison between providers is the meaningful part. Provider coverage changes over time as APIs appear and shut down.

Citation

Archived, citable version with a DOI: https://doi.org/10.5281/zenodo.21954788

@dataset{llmlatency_tracker,
  title  = {LLM Latency Tracker: measured latency and uptime for AI inference APIs},
  author = {llmlatency.dev},
  year   = {2026},
  doi    = {10.5281/zenodo.21954788},
  url    = {https://llmlatency.dev}
}