Feature Flags & Experimentation for Builders
Feature flags let you ship code to production without turning it on for everyone — and turn it off instantly if something goes wrong, with no redeploy. This course covers flag types, targeting and gradual rollouts, the statistics behind A/B testing, choosing a feature flag tool, and the production patterns that keep flags from becoming permanent tech debt.
What you'll learn
Course outline
Free — no account needed
Why Feature Flags Change How You Ship
Decoupling "deployed" from "released" is the single biggest unlock for shipping confidently
The Four Types of Flags
Release, ops, permission, and experiment flags solve different problems — and have different lifespans
Your First Feature Flag
Wire up a real flag tool and gate a feature behind it in under 10 minutes
Full course — $59 one-time
Targeting & Rollout Strategies
Percentage rollouts, user targeting, and the gradual ramp that catches problems early
A/B Testing & Experimentation
The statistics basics that keep you from shipping a "winning" variant that was actually noise
Choosing a Feature Flag Tool
PostHog, LaunchDarkly, Statsig, and Unleash solve overlapping problems at very different price points
Flag Hygiene & Tech Debt
Every flag you never delete is a permanent if-statement and a permanent test case
Production Patterns: Server, Client, and Kill Switches
Where a flag is evaluated changes its performance characteristics and what it can protect against
Get the full course
8 lessons — from your first flag to gradual rollouts, A/B testing, and production kill switches.
About this course
Feature flags decouple deploying code from releasing it to users — code can be merged and shipped to production while remaining invisible until you flip a switch, with no redeploy needed to turn it on, roll it back, or ramp it from 1% to 100% of traffic. Learning feature flags means understanding the different flag types (release, ops, permission, experiment), how gradual rollouts and targeting work, and the statistics behind running a trustworthy A/B test rather than one that just looks like a winner due to noise.
This course is for product engineers and technical founders who want to ship more confidently and run real experiments. After completing it you will be able to wire up a feature flag tool, design a gradual rollout plan for a risky feature, avoid the most common A/B testing mistakes (peeking early, ignoring guardrail metrics), choose between flag platforms like PostHog, LaunchDarkly, and Statsig, and build a genuine production kill switch that fails safe.
Frequently asked questions
What is the difference between a feature flag and an A/B test?
They use the same underlying mechanism — showing different things to different users based on a rule — but serve different purposes. A feature flag (specifically a release flag) is typically a simple on/off switch used to control a rollout, with the goal of eventually reaching 100%. An A/B test (experiment flag) deliberately keeps two or more groups separate for a fixed period to measure a difference in a metric, and is designed to be analysed statistically before a decision is made, not just rolled out.
How do I know if my A/B test result is real or just noise?
Calculate the required sample size before running the test, based on your current baseline conversion rate and the smallest improvement worth detecting — most flag platforms with built-in experimentation (PostHog, Statsig, LaunchDarkly) do this calculation for you. The most common mistake is "peeking" — checking results daily and stopping the moment they look significant — which inflates false positives substantially. Decide the sample size and duration upfront and do not stop early.
Should I build my own feature flag system or use a tool?
For a simple on/off flag with no targeting, a database table and an in-memory cache can work. Once you need percentage rollouts, user targeting, sticky bucketing, or any real experimentation, use an existing tool — PostHog (free tier, bundled with analytics), LaunchDarkly or Statsig (purpose-built, enterprise features), or Unleash/Flagsmith (open source, self-hosted). The statistics and edge cases (consistent bucketing, safe defaults on provider outage) are easy to get subtly wrong building from scratch.
Why do feature flags become technical debt?
A flag is meant to be temporary scaffolding for release and experiment types — once a feature is fully rolled out or a test concludes, the flag and the old code path it gates should be deleted. Teams that never clean up end up with old conditional branches nobody remembers the reason for, multiplying untested code paths. Give every temporary flag an expiry date at creation, and review live flags on a regular cadence to delete what is stale.
Where should a feature flag be evaluated — server or client?
Server-side evaluation avoids a visible flicker as the wrong variant briefly renders before the correct one loads, which matters most for above-the-fold content like a hero section or pricing table. Client-side evaluation is simpler to wire up and fine for below-the-fold or non-visual flags. Most flag SDKs evaluate against a locally cached ruleset rather than calling an API on every check, so performance is rarely the deciding factor — visual flicker usually is.