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Audience-targeted feature flags with percentage rollouts + kill-switch toggle + evaluation log. Honest about not being LaunchDarkly statistical engine.

LaunchDarkly is a flag store. Statsig is an experiment platform. Sendora's Experiments module is leaner: typed flags (`boolean / string / number / json`), rule-based rollouts (audienceId + percentage + value), evaluate + evaluate-all endpoints with the same shape as LaunchDarkly's SDK contract, instant toggle + audit log. Honest about scope: no first-class `multivariate` flag type (use `string` or `json`), no statistical experiment readout engine, no winner auto-promote to In-App / Push, no `experiment.assigned` event on the platform bus (evaluations write to `flag_evaluations` table, not `events`).

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Features

  • Flag types — boolean / string / number / json per the Zod schema. Honest: no first-class multivariate enum value — express multi-arm via string or json.
  • Rule engine — each rule is { audienceId?, percentage?, value? }. First matching rule wins; default value otherwise. Max 20 rules per flag.
  • Audience-targeted rollouts — pick audiences from Customers; real-time membership (audiences evaluate at query time).
  • Percentage rollouts — hash-bucket per entityId so sticky-by-user roll-out is deterministic.
  • Per-environment toggles — environments: { production: true, staging: false } per flag.
  • Kill-switch toggle — POST /feature-flags/:id/toggle flips isActive; logAudit captures actor + timestamp.
  • Evaluation log — every evaluate + evaluate-all call writes to flag_evaluations (fire-and-forget) with the rule matched + the final value.
  • SDK helpers — Web sendora.flags.*; RN 0.18.7+ ships flags.evaluate(key) + flags.evaluateAll().
  • Honest non-features: no statistical experiment readout (no funnel / retention auto-cohorting by variant); no winner auto-promote to In-App / Push A/B; no experiment.assigned event on the platform events bus (flag_evaluations is a separate table); no SDK streaming connection (each evaluate is a real HTTPS call); no scheduled flag changes.

Common use cases

Audience-gated feature rollouts (enterprise + active 30d gets the new dashboard) with kill-switch + audit log.

Percentage canary releases with sticky-by-user assignment.

Operational kill-switches where the audit trail (who flipped what, when) is the regulatory deliverable.

Build your own experiment readout by querying `flag_evaluations` + Analytics events in the same tenant.

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The free tier covers most side projects. Every module is turn-key and every SDK is first-party.

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