Do I need feature flags for experiment A/B testing?

Not always, but feature flags are often the easiest way to run A/B tests, especially if you want to:

• Roll out different variants to specific user groups
• Turn experiments on/off without redeploying
• Target by attributes like geography, plan type, device, or cohort
• Gradually ramp traffic and keep a kill switch handy

When feature flags are a good fit

Use them if your A/B test is really a controlled rollout of a product change:

• variant A = control
• variant B = new UI/behavior
• you want random assignment and traffic splitting
• you want operational safety and quick rollback

When you might not need feature flags

You may not need them if:

• your experimentation platform already handles assignment and serving variants
• the test is done purely in analytics / backend logic
• it’s a one-off prototype or small internal test
• the variation is built directly into the app and won’t be reused

Important distinction

A feature flag is mainly about delivery/control.
An A/B testing system is mainly about assignment/measurement.

They often work together:

• flag decides which variant a user sees
• analytics tracks outcomes

Practical recommendation

If you don’t already have an experimentation platform, feature flags are usually the simplest foundation for A/B testing.

If you want, I can also show:

1. a simple architecture for flags + experiments, or
2. when to use flags vs a dedicated experimentation tool.