LaunchDarkly or Sendora — pick the trade-off, not the marketing.
LaunchDarkly is the gold standard for flag stores. It also doesn't know about your audiences (sync from CDP), your experiments (read Mixpanel), or your downstream messaging (Customer.io). Sendora Experiments give you the flag store + audience-targeted rollouts (from Customers) + Analytics-driven impact readout + In-App/Push A/B winner-promote — all same tenant.
Feature flag platform. Per-MAU pricing. Experiment readout via Mixpanel / Amplitude bridge.
Feature flags where the rollout audience, the experiment readout, and the winner-promote all live in one tenant.
Side-by-side
| Capability | LaunchDarkly | Sendora |
|---|---|---|
| Flag types | ✅ boolean / string / number / json / multivariate | ✅ boolean / string / number / json — no first-class `multivariate` (use string or json) |
| Rule engine (audienceId + percentage + value) | ✅ rich rule editor | ✅ — max 20 rules per flag |
| Audience-targeted rollouts | Via CDP integration | ✅ same tenant — pick a Customers audience |
| Sticky-by-user percentage rollouts | ✅ | ✅ hash-bucket per entityId |
| Per-environment toggles | ✅ | ✅ `environments: { production, staging }` |
| Kill-switch + audit log | ✅ | ✅ — toggle endpoint + logAudit captures actor + timestamp |
| Evaluation log per call | ✅ + streaming | ✅ `flag_evaluations` table, fire-and-forget. **No streaming connection** — each `evaluate` is a real HTTPS call. |
| Built-in statistical experiment readout (funnels / retention auto-cohorted by variant) | ✅ Experimentation add-on | Partial — `funnel-by-variant` endpoint (W30) + two-proportion z-test for significance (W31). Retention by variant still on backlog. |
| Winner auto-promote to messaging variants | Via Experimentation product | ❌ — not built. |
| Scheduled flag changes (ramp at time T) | ✅ | ✅ `POST /feature-flags/schedules` stages a JSON patch (defaultValue / rules / isActive / environments); 30s cron claims due rows + merges into target flag (Wave 55) |
| `experiment.assigned` first-class event on platform bus | Via streaming | ✅ mirror-write into `events` on every evaluation so Analytics can cohort by variant (Wave 23) |
| Pricing model | Foundation $12/seat + $10/1K MAU; Enterprise quoted | Bundle, 21 other modules included |
Why teams switch to Sendora
- Replace LaunchDarkly Foundation tier (~$120-200/mo for small team with MAU overage) when you also need the audience source + the messaging surface in the same tenant.
- Rollout to a Sendora audience (enterprise + active 30d) without CDP sync wiring.
When LaunchDarkly is the right call
- You need built-in statistical experiment readout (funnel + retention auto-cohorted by variant) — Sendora has flag_evaluations rows; the readout is BYO via Analytics.
- You need winner auto-promote to messaging variants — not built.
- You need scheduled flag changes ("ramp to 100% at midnight") — Sendora is manual change only.
- You need first-class `multivariate` flag type — Sendora's enum is boolean/string/number/json (express multi-arm via string/json).
- You need streaming flag updates / sub-100ms edge eval at scale — Sendora's evaluate is a real HTTPS call.
- You need LaunchDarkly Code References + custom rules + deep audit-log workflows.
Common questions
Does Sendora's flag SDK have LaunchDarkly's edge-eval latency?
Honest answer: not yet. LaunchDarkly's edge-eval network is years of investment. Sendora's flag eval is server-side with sub-100ms latency from common regions. For UX-critical flag flips, A/B-test before final cutover.
Do LaunchDarkly Code References port?
No — Code References (auto-detect flag usage in source) is LaunchDarkly-specific tooling. Use a grep-based script during migration to catch dead flags before sunset.
How does experiment readout work?
Honest: Sendora doesn't ship a statistical readout engine. Flag evaluations write to `flag_evaluations` (variant + ruleMatched per call); event activity for the affected users sits in the same tenant's `events` table. Build a funnel-by-variant report by joining `flag_evaluations` against Analytics events on `entityId`. No auto-cohort, no significance test, no winner auto-promote.
Can I migrate Big Segments?
Yes — Big Segments port to Sendora audiences via Customers module. Same nested AND/OR semantics. Real-time membership instead of sync cadence.
Experiments
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`).
Switch from LaunchDarkly. Keep your weekend.
Free plan covers real product use, no credit card. Bulk hash import for auth, CSV import for profiles, schema-validated event import for analytics — Data Sync module handles the migration in a day.