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Use a feature flag to control who sees a change and when; use an A/B test to learn which alternative performs better against a defined outcome. A rollout can reduce release risk without comparing alternatives. An experiment requires deliberate assignment and measurement. Many teams use both: gate access, test eligible users across variants, then progressively release the chosen version.

Feature flags vs. A/B testing: the core difference

A feature flag is a runtime control for enabling or disabling a code path for selected users or environments. It separates deploying code from exposing the feature: teams can preview a change internally, target an audience, increase exposure gradually, or switch the feature off without making another code deployment. Statsig calls these controls “feature gates”; its documentation describes targeting and toggling capabilities in its own platform (Statsig feature flag documentation).

An A/B test is a controlled comparison. It assigns people or other eligible units to different versions, measures a chosen outcome, and evaluates whether the observed difference supports a decision. The question is not simply whether a version can be enabled, but whether the evidence favors one alternative on the outcome that matters. See Optimizely’s A/B test overview.

The distinction is purpose, not a strict boundary between product capabilities. Some platforms build experiments on top of flags. A flag can control eligibility or exposure while an experiment handles variant assignment and measurement. The details—such as rule types, metrics, and statistical options—depend on the platform.

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When should you use a feature flag?

Choose a flag or rollout when the immediate task is managing delivery and exposure, rather than deciding which of several alternatives wins.

  • Internal preview or dogfooding: let employees or a named group try a feature before a broader launch.
  • Targeted release: expose a feature to a beta audience, selected accounts, or a particular region.
  • Gradual rollout: expand exposure in stages while watching for problems.
  • Fast disablement: turn off a troublesome feature without waiting for a code redeployment, provided the flag is implemented and evaluated in a way that supports that control.
  • One known change with monitoring: observe operational impact, such as errors or latency, while releasing a single implementation. This is a rollout with metrics, not automatically an A/B test.

A rollout can be useful even when there is no competing version and no hypothesis about a user outcome. Optimizely’s Feature Experimentation documentation describes its product-specific distinction between rollouts with one variation and A/B tests with two or more (Optimizely rollout documentation). That is a platform’s implementation model, not a universal definition of every vendor’s features.

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When should you use an A/B test?

Run an experiment when you have competing alternatives and a measurable question—for example, whether a revised onboarding flow improves completion without increasing errors. Define the hypothesis and primary outcome before looking at results. Depending on the change, the outcome might be a user action or a technical measure such as latency, error rate, cost, or throughput. LaunchDarkly describes experimentation capabilities including metrics and A/B/n tests in its experimentation documentation.

A useful test plan identifies the eligible population, the versions being compared, how a unit is assigned, when exposure is recorded, the primary metric, and relevant guardrails. Guardrails help catch a trade-off—for instance, a higher conversion rate that comes with worse reliability. A test is only as interpretable as its assignment and instrumentation: without stable assignment and reliable event logging, apparent differences may reflect inconsistent exposure or missing data rather than the change being evaluated.

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There is no universal sample size or test duration that follows from the vendor guides cited here. Those decisions depend on the outcome, traffic, expected effect, experiment design, and analysis method. Use the platform’s statistical method and a planned stopping and decision approach; do not treat an early favorable fluctuation as a definitive winner.

Choose the approach that fits the job

Situation Prefer Reason
Preview a feature internally, target a beta audience, launch by region, or retain a quick off switch Feature flag or rollout Controls exposure and release risk; experiment analytics may not be needed.
Compare competing implementations against a defined, measurable hypothesis A/B test Provides a controlled comparison across variants using selected metrics.
Safely launch the version supported by experiment results Both, in sequence Finish the comparison, then use rollout controls to expand exposure.
Ship one known change gradually while monitoring its technical impact Rollout with metrics, if supported Measures impact without implying that multiple alternatives were compared.

Asa Schachar, writing for Optimizely in an article published on April 23, 2020, put the distinction this way: “Feature flags allow seamless feature releases and rollbacks. Phased rollouts catch bugs early. A/B tests make sure you’re building the right thing.” The conceptual point remains useful; current product implementation should be checked against current documentation (Optimizely’s article).

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How to combine a flag and an experiment

  1. Define the problem and outcome. State what needs to improve or what risk needs to be controlled. If the goal is learning, select a primary metric and relevant guardrails before building variants.
  2. Separate deployment from exposure. Put the change behind a flag when you need to control its availability. Set appropriate audience rules or an internal allowlist for previews.
  3. Assign eligible units consistently. For an experiment, randomize a stable unit such as a user identifier into the baseline and one or more variants. Keep assignment consistent for the relevant test period. Google Cloud’s documentation describes stable bucketing in its own product context (Google Cloud allocation guide).
  4. Validate assignment and instrumentation. Check that the intended groups receive the intended versions and that exposure and outcome events are logged. An A/A test—comparing nominally identical experiences—can help reveal allocation or metric-stability problems before a real comparison; LaunchDarkly documents this as one of its experimentation capabilities.
  5. Monitor outcomes and guardrails. Track the primary measure alongside technical or user-impact safeguards relevant to the change. Interpret results using the platform’s stated statistical approach and the decision plan.
  6. Act on the result. If the evidence supports a version, expand exposure progressively and monitor the release. If there is a problem, reduce exposure or disable the flag.
  7. Close the loop. Record an owner and a removal condition for temporary flags, then remove obsolete flag logic and configuration when it is no longer needed.
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What to check when comparing platforms

Do not choose a vendor based on the labels “flag” or “experiment” alone. The same terms can describe different implementation and analysis features. Compare what the team actually needs:

  • Technical fit: SDK coverage for your applications and services, and how evaluation behaves in your deployment environments.
  • Exposure controls: targeting, internal previews, staged rollouts, and how quickly a change can be disabled.
  • Experiment design and analysis: supported assignment methods, exposure and outcome measurement, metrics, and statistical views.
  • Data and integrations: whether the platform can connect to the events, analytics, and workflows your team relies on.
  • Governance: permissions, ownership, auditability, and a practical way to identify and clean up temporary flags.
  • Constraints and total fit: plan restrictions, SDK or implementation requirements, allocation limits, and vendor lock-in. Confirm these directly in current product documentation; they are not universal properties of flags or experiments.

For example, Statsig distinguishes boolean feature gates from experiments that return variant configuration in its guide (Statsig decision guide). Optimizely documents rollouts and A/B tests as separate rule types in its product. LaunchDarkly documents options such as frequentist or Bayesian uncertainty views and multi-armed bandits in its own service. These are vendor-specific capabilities, not requirements for every valid experiment.

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Google Cloud’s cited App Lifecycle Manager page describes allocation-based tests and stable bucketing, but marks the feature Preview / Pre-GA and warns of limited support. Check its current launch stage and suitability before relying on it (Google Cloud documentation). Across all vendors, SDK needs, analytics behavior, availability, and plan terms can change.

Common mistakes to avoid

  • Calling every staged release an experiment. Gradual exposure of one chosen version is a rollout; it does not by itself establish a controlled comparison.
  • Testing without a decision question. If no primary outcome or hypothesis was set, it is easy to overinterpret whichever metric looks favorable afterward.
  • Ignoring assignment and exposure quality. Inconsistent bucketing or incomplete event logging can undermine the comparison.
  • Confusing product features with general definitions. A vendor’s rule types, statistical options, or plan gates describe that service, not the whole category.
  • Leaving flags behind indefinitely. Temporary controls can become maintenance burden. Name an owner and a condition for removal when creating them.

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