Know which AI change should ship.
Run evals and online experiments on prompts, models, and agents. Winnow tells you when the evidence is strong enough, protects the rollout, and turns production failures into the next test.
No login · same statistical engine · method & assumptions shown
Ship candidate B to 25%
Quality improved with no safety regression. Cost is lower; latency stays within the release budget.
winnow.init("dft_your_key", auto_instrument=["anthropic"]) arm = winnow.get_variant("models", user_id="u-1") # then winnow.log(...) your metric
Method in the open. Data you keep.
Every result shows its test and its assumptions, exports through open standards, and runs on an SDK you can read.
Shown, not asserted
Every verdict carries its test, its assumptions, and a could_be_wrong_if. We show the statistics, not just a green light.
Statistics that passed review
Statistical methods carry a documented sign-off before they back any public claim.
You own the data
Point any OTLP gateway at Winnow with one static key. Cancel anytime and keep everything.
Two-line install
MIT-licensed SDK. Self-host the platform or run it on our cloud. Star it on GitHub →
Any OTLP gateway to Winnow, one static key. You own what you learn.
# one endpoint, one header export OTEL_EXPORTER_OTLP_ENDPOINT=https://ingest.justwinnow.com export OTEL_EXPORTER_OTLP_HEADERS="x-winnow-key=wk_…"
One loop, from evaluation to production and back.
A code change is deterministic: tests pass, you ship. A prompt, a model swap, or an agent tweak behaves differently — the same input can pass today and drift tomorrow, and "looks good in the PR" tells you nothing about production.
Winnow puts the loop back: evaluate with real statistics, ship behind guardrails, watch quality in production, and feed failures back into the evals — so an AI change moves through your release flow with the confidence a code change used to. Then Optimize proposes the next change to test, and the loop repeats.
Measure offline. Prove online. Learn in production.
The same statistical engine runs your offline evals and your live experiments, then guards the rollout and closes the loop.
Evaluate offline
Turn quality, safety, and cost into repeatable evals. Offline evals and online experiments share one engine, so a passing eval means the same thing in production.
Prove online
Compare prompts, models, and agents with evidence that stays valid while the experiment runs. mSPRT, CUPED, and SRM detection under the hood.
Learn in production
Guardrails and kill-switches protect the rollout; a failing production cluster becomes the next golden eval — human-gated, so coverage compounds.
The agent proposes the next change — always reviewable. Observer · Supervised · Autonomous, each proposal carrying a rationale, a safety screen, and a could_be_wrong_if.
It's better? How sure are you?
Every winner Winnow declares comes with the math a careful reviewer would demand — an assignment-unit result at a planned look, confidence intervals, SRM detection, and anytime-valid intervals you can peek at safely. And it tells you when it might be wrong: every result carries a could_be_wrong_if. We don't ask you to trust the AI; we show you the statistics.
Where Winnow fits.It's strongest when you can define a metric and route real traffic. It isn't a labeling workforce or a data warehouse — bring a metric, connect your gateway, and Winnow does the deciding.
| Feature | Winnow | Braintrust | Statsig | Datadog |
|---|---|---|---|---|
| A/B testing | yes | no | yes | yes |
| LLM evaluation | yes | yes | no | partial |
| Autonomous optimization | yes | no | no | no |
| Feature flags | yes | no | yes | no |
| LLM-agnostic | yes | partial | no | partial |
| Self-hosted | yes | no | no | no |
Start with one change. Scale when the workflow proves itself.
Full plan comparison lives on Pricing — the homepage just shows where you start.
Teams start at $349/mo flat with 10 seats included. No plan cards here — see the full matrix for limits, overage, and controls.