For LLM engineers
A silent provider update shows up first in the eval metrics you already track — judge scores sliding, task success dipping — and catching it early takes an alarm that stays valid under continuous checking. ValidAnytime watches every eval and quality metric with anytime-valid e-processes: one alarm per real regression, however often you look, instead of a dashboard that stays green until the churn email.
Free to start · no credit card
It's Tuesday. Your RAG pipeline starts returning answers that are subtly worse — format still valid, tone still fine, just wrong more often. No commit on your side, no error in the logs, latency normal. Your eval suite runs nightly and the average judge score wobbles the way it always wobbles, so nothing trips. Three days later a customer notices before you do. You spend the afternoon on the question with no owner: did they change the model, or is it just me?
Tracing and eval frameworks are indispensable for debugging — keep them. Their production alerting, though, is a threshold on a noisy score, and a threshold checked continuously is a false-alarm machine: check a fixed 5%-error test 5 times and the real false-positive rate is already ~23%; at 20 looks it passes 64%. Online evals don't fix it; every scheduled run is another roll of the dice.
Send any number you already compute — judge scores, task success, format validity, latency, cost — through one HTTP call or the thin Python SDK. No prompts, no completions, no traces leave your stack; you send the metric, nothing else.
Before anything goes live, the backtest gate replays your past eval runs and shows whether — and on which day — it would have caught your last regression. A config that false-alarms on your normal history never ships.
Live e-processes accumulate evidence point by point. When a score genuinely regresses you get one alarm with a certificate — the guarantee tag and the theorem behind it — the day the evidence is real, not a week of noise you learn to mute.
Every day we run anytime-valid change detection on the models you build on — Claude, GPT, Cursor, Gemini — from public signals, and publish a verdict with a stated false-alarm budget. When the community erupts with “did they nerf it?”, there's finally a referee. It's the exact engine you'd point at your own evals.
LLM judge score — slow regression
Synthetic, illustrative — runs in your browser, nothing uploaded.
From the seeded demo — synthetic stream, real engine. Load it in the live dashboard →
We are not a tracer and we don't run your evals. Keep LangSmith or Langfuse for debugging, datasets, and prompt management — they're genuinely better at it. ValidAnytime takes the scores those tools produce and wraps them in an alarm you can trust continuously. Any numeric metric, through the API — that's the honest integration story today; first-party connectors are on the roadmap.
Point ValidAnytime at one stream and it replays your own history — free, no credit card.