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llmlatency/llm-latency-tracker

LLM Latency Tracker Independent, continuously measured latency and availability for AI inference API providers, aggregated by day. Covers 46 providers across 4 regions (ap-tokyo, eu-hetzner, sa-east, us-central), built from 3,878,804 raw probes collected between 2026-07-23 and 2026-10-05. 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… See the full description on the dataset page: https://huggingface.co/datasets/llmlatency/llm-latency-tracker.

sourceHugging Facecc-by-4.0updated 21h agoView on Hugging Face
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LLM Latency Tracker

Independent, continuously measured latency and availability for AI inference API providers, aggregated by day. Covers 46 providers across 4 regions (ap-tokyo, eu-hetzner, sa-east, us-central), built from 3,878,804 raw probes collected between 2026-07-23 and 2026-10-05.

Live rankings and full methodology: [llmlatency.dev](https://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

filecontents
daily_aggregates.csvone row per date × provider × region × probe type, with p50/p95, sample count and success rate
rankings.jsonmachine-readable snapshot published live at llmlatency.dev

Usage

python
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:

bash
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>

bibtex
@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}
}

Links

  • —Live site and methodology: <https://llmlatency.dev>
  • —Machine-readable API: <https://llmlatency.dev/api/rankings.json>
  • —Daily snapshots in git: <https://github.com/mazamaka/llm-latency-tracker>
  • —Python client: <https://pypi.org/project/llmlatency/>
  • —Citable archive (DOI): <https://doi.org/10.5281/zenodo.21954788>

Licence: CC-BY-4.0. Snapshot generated 2026-10-05.