Experimentation & feature flags · LaunchDarkly

LaunchDarkly

Feature-flag management platform that also runs experiments on flagged rollouts, reading event data it collects itself rather than the warehouse.

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LaunchDarkly is first a feature-management platform: flags gate code paths for progressive rollouts, kill switches, and targeted releases across server, client, and mobile SDKs. Its experimentation module analyzes the same flag-driven traffic splits, offering both a frequentist path (fixed-horizon or sequential analysis) and a Bayesian path (posterior mean and credible intervals), letting teams pick per-experiment. This distinguishes it from warehouse-native experimentation platforms such as Eppo, Statsig's warehouse mode, or GrowthBook: LaunchDarkly's experiment data comes from events its own SDKs report, not from querying a customer's existing warehouse tables, so there is no data-modeling step required before testing, but metrics live in LaunchDarkly rather than alongside a company's other warehouse-based analytics. It is cloud-hosted (with self-managed relay proxy options) and billed primarily on seats and monthly active users measured through flags.

At a glance

Vendor LaunchDarkly
Pricing model Subscription
Free tier Yes
Deployment Cloud
Open source No
Best for Engineering teams that want feature-flag-driven releases with built-in experiment analysis on the same flags.

Pricing

Free Developer plan with 100K experimentation MAU/month; paid Foundation plan bills per service connection and per 1K client-side MAU; Enterprise is custom.

Plan Price Notes
Developer $0/month unlimited seats, 100K experimentation MAU/month, 14 days data retention
Foundation $10 per service connection/month + $8.33 per 1K client-side MAU/month billed yearly, pay-as-you-go available, 30 days data retention
Enterprise custom 100+ days retention, advanced RBAC, SCIM

Prices read from the vendor's own page on September 21, 2026. Vendors change prices; check the source before you budget.

Features

  • Feature flags across server, client, and mobile SDKs
  • Progressive rollouts, targeting rules, and kill switches
  • Experimentation with a choice of frequentist or Bayesian analysis
  • Sequential analysis for continuous monitoring
  • Guardian add-on for automatic rollback on regressions
  • Audit logs and RBAC for release governance

Integrations

Profile last reviewed September 21, 2026

Head to head

Alternatives

Harness Feature Management & Experimentation (formerly Split) Feature-flag platform with built-in statistical experimentation, acquired by Harness and sold as an Enterprise-plan module (Harness FME). Unleash Open-source feature-flag platform with rollout-based experimentation, moved from Apache-2.0 to AGPLv3 licensing in mid-2026. Flagsmith Open-source (BSD-3-Clause) feature-flag and remote-config platform with basic A/B testing on flagged traffic, self-hostable or cloud.

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Terms to know

A/B testing A controlled experiment that compares two variants to measure which one produces a better outcome. Statistical significance A threshold judgment that an observed effect is unlikely to be due to chance alone, given the data and test used. Confidence interval A range calculated from sample data that, under repeated sampling, would contain the true value a stated percentage of the time. Role-based access control (RBAC) Granting data or system permissions based on a user's assigned role rather than configuring access for each person individually. Sequential testing An analysis method that lets experiments be checked continuously without inflating the false-positive rate from repeated peeking.

Related guides

How to choose an experimentation and feature-flag platform Pick by where your experiment data comes from — flag telemetry or your own warehouse — and which statistical engine your team trusts, not feature count. How to set up an experimentation program From your first A/B test to a durable program — the plumbing, the statistics rules, the review ritual, and the tools that support each stage.