Experimentation
Turn any feature flag, config, or AI config into an experiment instantly. Measure the impact of UI changes, parameters, and AI behavior, all with the same statistical rigor.
Measure impact at the feature level, trust the statistics behind it, and run experiments wherever your data lives.
Attribute metric changes to the exact flag, config, or AI config a user was exposed to.
Rigorous statistical methods make sure you act on valid results, not noise.
Run experiments in the cloud or your warehouse, governed and privacy-first, with the same statistical engine.
Experiment Results · checkout_flow_v2
Experiment on flags, configs, and AI behavior. Turn any feature flag, config, or AI config into an experiment. Test UI changes, operational parameters, prompts, model selection, and temperature, without a separate tool or redeploying.
Patented exposure-level attribution. Metric changes are attributed to the specific flag, config, or AI config the user was exposed to. Standard analytics tools attribute to pages or sessions, not to the individual change that caused the result.
Guardrail metrics on every change. Define metrics that must not degrade. Get alerted automatically if a secondary metric moves in the wrong direction.
Sequential testing. Monitor results continuously and reach valid conclusions faster without waiting for a fixed end date.
SRM detection. Automatically detect sample ratio mismatch before it corrupts your results.
Dimensional analysis by any attribute. Slice results by country, device, cohort, or any custom attribute. Surface patterns that aggregate results miss.
Unified metrics library shared across teams. Define metrics once, reuse them across every experiment. No fragmented definitions, no reconciliation across tools.
Warehouse-native experimentation. Query your data where it lives. Snowflake, Redshift, and BigQuery supported. No ETL, no PII export, no vendor lock-in on your metrics.
Define metrics from your own data. Use warehouse tables that already follow your governance rules. No vendor templates. Results computed inside your infrastructure, refresh on a schedule or on demand.
Inspect the SQL behind every result. Full transparency into how results are calculated. No black box, no vendor opacity.
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increase in release frequency
versions of the app testing simultaneously in production
“Harness makes it possible to really understand how our users respond to changes we make. Now it's easy to know the best change for our users and how we can help them satisfy their creative journey.”
— Jean Steiner, Ph.D., VP of Data Science, Skillshare
Cloud Experimentation lets you turn any feature flag into a controlled experiment instantly. You define metrics, Harness handles the assignment and analysis, and you get statistically rigorous results without standing up separate A/B testing infrastructure.
Harness supports both sequential testing and fixed-horizon testing. Sequential testing lets you reach decisions as soon as significance is detected, which is ideal for rapid iteration. Fixed-horizon testing applies stricter statistical rigor for high-stakes decisions like revenue and conversion experiments.
Harness's patented attribution engine automatically connects metric changes to the exact feature flag that caused them. It tracks metrics in real time across every rollout and integrates with analytics providers like Segment, mParticle, Sentry, and Google Analytics so you never have to manually correlate results.
Yes. Any active feature flag can be converted into an experiment with a few clicks. You select your target metric, define the experiment window, and Harness begins tracking assignment and metric data automatically.
Harness surfaces statistical significance in the results dashboard as data accumulates. With sequential testing, you receive a decision as soon as the threshold is crossed. With fixed-horizon testing, results are surfaced at the end of the predetermined sample period with full confidence intervals.
Enable every product and engineering team to run experiments without bottlenecks.