Confidence by Spotify is a warehouse-native platform for feature management, A/B testing, progressive rollouts, session replay, and product analytics.
Confidence by Spotify is a warehouse-native experimentation and feature management platform for product teams. It supports feature flags, A/B tests, sequential testing, progressive rollouts, warehouse-defined metrics, session replay, and analytics in a single workflow.
The product is built around connecting product changes to measured outcomes: teams can release features gradually, monitor guardrails, analyze experiment results, and investigate user sessions when results need explanation. It can run on Confidence Cloud or on a warehouse managed by the organization, with support listed for BigQuery, Snowflake, Redshift, and Databricks.
Run A/B tests with sequential testing, automatic guardrails, and CUPED variance reduction enabled by default. The platform also publishes guidance on sample size, multiple metrics, multiple testing, ratio metrics, and experiment review.
Ramp releases from an initial percentage toward full rollout while monitoring guardrail metrics, with a one-click rollback path when regressions appear.
Create typed feature flags with warehouse-powered targeting and cleanup prompts for flags that have completed their rollout. Confidence supports unlimited feature flags on the Free plan.
Capture and replay real user sessions with experiment variants and flags overlaid, helping teams investigate behavior behind a metric change. Capture and replay are free; AI analysis is metered.
Ask questions in plain English and receive charts and explanations grounded in experiments, defined metrics, and warehouse data. The Confidence Agent can inspect sessions and experiments and suggest next steps.
Use Confidence Cloud or connect an organization-managed warehouse. Listed warehouse options include BigQuery, Snowflake, Redshift, and Databricks, while OpenFeature provides a standards-based feature-flag integration path.
Define an experiment, estimate and monitor its runtime, expose users to variants, and evaluate outcomes with sequential testing, guardrails, and variance reduction.
Release a feature gradually, watch health and guardrail metrics during the ramp, and revert the rollout when the release produces a regression.
Use typed flags and warehouse-powered targeting to separate deployment from release, then identify flags ready for cleanup after the rollout is complete.
When an experiment metric changes unexpectedly, inspect recordings with variants and flags overlaid to connect aggregate results with individual user behavior.
Let product and data teams ask questions against experiments, metrics, and warehouse data in plain language, then use charts, causes, session evidence, or suggested next steps to continue analysis.
Confidence is an experimentation and feature management platform for product teams. It combines feature flags, A/B testing, sequential testing, warehouse-native metric analysis, rollouts, session replay, and analytics in one product.
Yes. The Free plan includes unlimited feature flags and seats, Google SSO, EU or US hosting, and 75,000 monthly credits. It does not require a credit card. Feature flags and unmonitored rollouts do not consume credits.
Confidence can use Confidence Cloud, a managed BigQuery-based data layer, or a warehouse configured and managed by your organization. Supported warehouse-native options listed on the site are Snowflake, BigQuery, Redshift, and Databricks; paid plans can connect their own warehouse without a Confidence warehouse fee.
Credits are shared across metered capabilities, including measured experiment exposures, standard event ingestion, AI session-recording analysis, and AI agents. Capture and replay are free, while AI recording analysis is metered by active minute. Feature flags and unmonitored rollouts do not consume credits.
The product supports OpenFeature, a CNCF standard. Applications can use the OpenFeature API with Confidence as the provider, and the site describes migration kits for rewriting competitor SDK code to the standard.
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