Enterprise feature flagging — progressive rollouts, targeting, and kill switches
LaunchDarkly is the gold standard for feature flag management — the infrastructure that lets engineering teams deploy code without releasing features, and product teams control feature rollout percentages, targeting rules, and kill switches without code deployments. For Indian Series B+ startups deploying multiple times per day, LaunchDarkly's reliability and SDK quality justify the premium. For earlier-stage teams or teams comfortable with open-source, GrowthBook (free, self-hostable) covers 80% of LaunchDarkly's feature flagging capability at zero cost. The Firebase Remote Config option covers basic boolean flags for free on mobile. LaunchDarkly earns its price tag when you need enterprise-grade targeting, audit trails, and SDKs that your engineering team trusts at scale.
LaunchDarkly is a feature management platform founded in 2014 in Oakland, California. It provides the infrastructure for feature flags — code-level toggles that separate feature deployment from feature release. Engineers merge code to production with the feature behind a flag set to "off". Product managers then control when to turn it on, for which users, and at what rollout percentage — all without another code deployment.
For Indian engineering and product teams, feature flags solve a critical problem at scale: the coupling of "code is deployed" and "feature is live". Without flags, every deployment is a release decision, every bug is a rollback, and every experiment requires an engineering cycle. With LaunchDarkly, engineering deploys continuously, product controls releases, and a bad feature gets turned off in 30 seconds instead of requiring a hotfix deployment.
LaunchDarkly is particularly valuable for Indian fintech and B2C apps deploying to millions of users: gradually rolling out a new payment flow to 1% → 5% → 20% → 100% of users while monitoring error rates, targeting a new feature to users in Karnataka before national rollout, or giving your QA team access to a feature in production without making it visible to customers.
Release a feature to 1%, 5%, 20%, 50%, 100% of users in sequence — monitoring metrics at each stage. Automatic rollback rules trigger if error rate or latency spikes beyond a threshold. For Indian payment flows and KYC updates, this rollout control reduces incident risk dramatically.
Show features to specific users based on any attribute: user ID, email, plan tier, city, device type, app version, custom segments. Target internal team members, beta users, or enterprise clients with features before general availability. Build user segments in LaunchDarkly and re-use them across multiple flags.
Turn off a misbehaving feature in production in seconds — no code deployment, no hotfix, no incident bridge call. The PM or on-call engineer can disable the feature directly from the LaunchDarkly dashboard. For Indian fintech teams where a payment bug at 2 AM needs immediate resolution, this is the most valuable feature in the entire platform.
A/B testing integrated with feature flags — assign users to variants and track metrics. Bayesian or frequentist statistical analysis built in. For Indian product teams running conversion experiments on checkout flows, LaunchDarkly's experiment layer eliminates the need for a separate A/B testing tool if you're already using flags for rollouts.
Free option: GrowthBook (open-source, self-hostable) + Firebase Remote Config for mobile flags. This covers 80% of feature flag use cases at ₹0.
Paid option that's 70% cheaper: Statsig — similar feature set to LaunchDarkly, free up to 1M events, significantly cheaper at scale.
LaunchDarkly is justified: When you have 50+ engineers, are deploying to millions of users, need enterprise-grade audit trails (regulated fintech), or when flag reliability is so critical that a 5-minute outage is a company-level incident.
| Factor | LaunchDarkly | Statsig | GrowthBook |
|---|---|---|---|
| Reliability / uptime | 99.99% SLA | 99.9% SLA | Self-hosted — yours |
| Pricing | Expensive | Free up to 1M events | Free self-hosted |
| SDK quality | Best-in-class | Very good | Good |
| Targeting rules | Most powerful | Strong | Good |
| Experimentation | Full platform | Full platform | Full platform |
| Audit trail | Enterprise-grade | Good | Self-managed |
| Best for | Large Indian teams, regulated fintech | Series A–B with experiments | Developer-led, budget-conscious |
LaunchDarkly charges per seat (team member). USD billing — 18% GST reverse charge for Indian companies. No free tier for teams.
$20/seat/month for up to 5 seats. Unlimited flags, basic targeting, rollouts. Entry point for small Indian teams. At ₹1,700/month for 5 engineers, the productivity value from safer deployments usually justifies the cost within the first month.
Custom pricing around $300–500/month for 10–20 seats. Full experimentation, advanced targeting, audit log, and SSO. Most Indian Series B+ teams land here. Compare with Statsig (free up to 1M events) before committing to this tier.
Custom contract for large teams. Includes dedicated infrastructure, custom SLAs, and advanced compliance features. Relevant for large Indian banks, insurance companies, or Tier 1 fintechs where downtime cost exceeds tool cost by orders of magnitude.
💡 Consider Statsig first: Statsig offers comparable feature flagging + experimentation, free up to 1M events/month, and paid plans starting at $150/month for unlimited events. Most Indian Series A–B teams find Statsig covers their needs at 80% of LaunchDarkly's quality at a fraction of the price.
Feature flags + A/B testing + analytics. Free up to 1M events. Best value alternative to LaunchDarkly for Indian Series A–B teams. Covers most use cases at significantly lower cost.
Open-source, self-hostable feature flags + A/B testing. Free forever when self-hosted. Best for Indian developer-led teams who can manage their own infrastructure. Strong community.
Free basic feature flags for mobile apps. Limited targeting, no experimentation stats. Best as a free entry point for Indian mobile apps before adopting a full feature flag platform.
new-checkout, new_checkout_v2, checkout-redesign-test, and checkout-flow-experiment all potentially active simultaneously. Establish a standard: [team]-[feature]-[type] e.g., payments-new-checkout-rollout, growth-referral-experiment, ops-bulk-kyc-killswitch. Write this in your engineering handbook before creating any flags.We help Indian engineering and product teams implement safe, scalable feature flag infrastructure — from tool selection to flag lifecycle management.
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