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ValidAnytime

A guaranteed false-alarm budget across your whole fleet, valid no matter how often you look.

Made by Compiled Intelligence — a frontier AI lab working on quantitative finance from first principles; ValidAnytime is the monitoring we built for our own model fleets, productized.

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  • For LLM engineers
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  • LLM eval regression
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Every alarm ships with its guarantee_tag and theorem_ref.

All comparisons

ValidAnytime vs Evidently

ML monitoring

Evidently gives you a broad, open library of data-quality and drift tests plus tidy reports — great for exploration and CI checks — while ValidAnytime is the live, anytime-valid alarm on the metrics those tests produce. Where Evidently leaves you on your own is alerting: run its tests on a schedule and the more often you look, the more they false-alarm. ValidAnytime fills exactly that gap, with fleet-wide FDR control.

Capability comparison between ValidAnytime and Evidently.
CapabilityValidAnytimeEvidently
Valid under continuous monitoring (unlimited peeking)
YesAnytime-valid by construction — Ville's inequality bounds the false-alarm rate at every look at once.
NoFixed thresholds and fixed-n tests inflate false alarms the more often you check.
Fleet-wide false-alarm control (online FDR)
YesA false-discovery budget shared across every stream, not per-alert luck.
NoAlerts are configured per-metric; no global bound on false discoveries.
Per-alarm statistical certificate
YesEvery alarm ships a guarantee tag and a theorem reference — you can audit why it fired.
NoAn alert tells you a line was crossed, not what its error guarantee is.
Prove it on your own history before committing (backtest gate)
YesReplay your past data: a config only ships if it stays quiet on normal history and fires on a real regression.
PartialYou can chart history, but there is no gate that validates a detector's error behaviour before it goes live.
Breadth of built-in tests & metrics
PartialWe focus on trustworthy change-detection, not a broad test catalog.
YesLarge library of data-quality, drift, and quality presets.
Reports & visual exploration
PartialFocused monitor reports rather than a general reporting toolkit.
YesPolished HTML reports and dashboards out of the box.
LLM & tabular coverage in one tool
PartialAny numeric stream — LLM-judge scores, latency, quality — through one detector.
YesPresets spanning tabular, NLP, and LLM evaluation.

Where Evidently is genuinely stronger

We are not trying to be a dashboard, a tracer, or a platform. If you need these, reach for the right tool — often alongside ValidAnytime.

  • Much broader catalog of ready-made tests and metric presets.
  • Excellent visual reports for exploration and CI.
  • Strong open-source adoption and documentation.

A comparison table is claims; behavior is measurable. The honest drift-detector benchmark replays every detector we ship — including the classical control-chart rules most monitoring stacks alert with — against labeled synthetic breaks, and the detector guides explain each rule, where it wins, and where it lies.

Don’t take our word for it — prove it on your data.

Replay your own history through the backtest gate and see whether — and at which point — ValidAnytime would have caught your regression. Free, in minutes.

Prove it on your dataTry the detector in your browser

Comparison based on public documentation as of July 2026; corrections welcome — email hello@validanytime.com. Source: Evidently docs