Imagine shipping new features was as simple as flipping a switch. What if you could test in production with minimal risk?

Feature flags make this a reality by allowing you to decouple deployment from release. They put you in control of who sees what, when.

What are feature flags?

Feature flags (also known as feature toggles, feature switches, or feature flips) are a software development technique that turns certain functionality on and off during runtime, without deploying new code. This allows for better control and more experimentation over the full lifecycle of features.

The idea behind feature flags is to build conditional feature branches into code in order to make logic available only to certain groups of users at a time. If the flag is "on," new code is executed; if the flag is "off," the code is skipped. Feature flags act as a powerful tool for testing in production, enabling continuous delivery, and more.

Feature toggles serve as "switches" to hide, enable or disable features during run time. For example, during the development process, a developer can enable the feature for testing and disable it for other users. Feature toggles are also a core component of trunk-based development, allowing developers to test their features in production, without disrupting the default code base.

Key benefits of using feature flags

Testing in production: Feature flags enable testing new features in the live environment with real users and real data. You can gradually roll out features to a small subset of users first. If anything goes wrong, you can quickly roll back the changes via a "kill switch" without redeploying code. Learn more about feature management and sequential testing to enhance your production testing strategies.

Quicker release cycles: Feature flags decouple feature lifecycle management from code deployment. This allows for faster and more flexible release cycles. You can continuously deploy code without worrying about a specific feature being ready or not. Development teams can work on multiple features simultaneously in the same environment. With feature flags, you can separate feature rollout from code deployment. This means you can deploy code that isn't ready for public consumption, then gradually release it to your users. Decouple deployment from release to ship features faster and more safely.

Progressive delivery: Implement canary releases by rolling out features to a small subset of users first. Then analyze performance and user feedback before rolling it out to everyone. Minimize risk by limiting exposure of new features.

A/B testing: Use feature flags to A/B test product changes and capture metrics on their impact. Turn on a feature for a subset of users and compare their behavior to a control group. Measure the impact on key metrics to make data-driven decisions. Discover how to run effective experiments and improve your product iteratively.

Customized user experiences: Use feature flags to create customized experiences for different user segments. Turn on specific features for beta testers or paid subscribers. Gradually roll out premium features from your free to paid tiers. Tailor the experience to each user's needs. Check out the startup program to explore feature flags benefits for early-stage companies.

Different types of feature flags

Boolean flags are the simplest type of feature flag. They have two states: true or false. Boolean flags are useful for turning features on or off. For example, you might use a boolean flag to control access to a beta feature. Learn more about using feature flags in React apps.

Multivariate flags allow for more than two variations. They can be used to test multiple versions of a feature simultaneously. Common types include:

• String flags: Store a string value, such as a message or URL. For example, check out how AI companies use Statsig for growth.

• Number flags: Store a numeric value, such as a price or percentage. See how to run experiments on Google Analytics data.

• JSON flags: Store a JSON object for more complex configurations. For detailed usage, refer to experimenting with generative AI apps.

Multivariate flags enable more advanced use cases. For example, you could use a string flag to test different copy variations. Or use a JSON flag to configure a complex feature with multiple settings.

Percentage rollout flags allow you to gradually release a feature to a percentage of users. They're useful for controlling the pace of a rollout. You can slowly increase the percentage over time as you gain confidence in the feature. Check out how to master marketplace experiments for detailed guidance.

Targeted flags allow you to enable a feature for specific user segments. For example, you could release a feature only to users in a certain country. Or to users on a specific plan tier. Targeted flags help you customize the user experience for different groups. Learn more about product observability for effective feature management.

Experiment flags are used to run A/B tests. They allow you to test different variations of a feature and measure their impact. Experiment flags are typically percentage rollout or targeted flags. They're used in conjunction with an experimentation platform to analyze results. For example, read about CUPED, a method to run experiments faster and with less bias.

Kill switches are a type of flag used to quickly disable a feature in production. They act as a safety mechanism in case something goes wrong. Kill switches are usually boolean flags that default to "off". They allow you to quickly turn off a feature without redeploying code. For more insights, explore how to manage feature flags.

Implementing feature flags in your workflow

Setting up flags is straightforward with a feature flag service. First, install the SDK in your codebase. Then create the flags in the platform UI. Wrap the relevant code paths with the flag conditions.

Managing flags is crucial to avoid technical debt. Review flags regularly and remove obsolete ones. Automate this process with expiration dates or cleanup workflows. Keeping your flags tidy maintains a clean, manageable codebase.

To integrate flags into your development process, follow these best practices:

• Use short-lived branch flags for new features. Remove them once the feature is stable.

• Use long-lived flags for release toggles and kill switches. Document these thoroughly.

• Naming is key - use clear, descriptive names for your flags. Follow a consistent convention.

• Keep flag logic separate from core application logic. This maintains separation of concerns.

• Audit your flags periodically. Remove flags that are no longer needed to keep your codebase clean.

Experiment-driven development is a powerful technique. Use feature flags to test new ideas. Instrument your code to track key metrics. Compare flagged vs unflagged users to measure impact. Roll out winning variations or roll back underperforming ones.

Progressive delivery minimizes risk when releasing new features. Start with a small percentage rollout. Monitor key metrics like performance and error rates. Gradually ramp up the percentage as you gain confidence. Pause or rollback if you detect issues.

Targeted rollouts allow you to enable features for specific user segments. Use targeting rules based on user attributes. Examples include region, device type, or account tier. Verify your targeting with QA users before expanding. Analyze metrics for each segment to ensure desired behavior.

Advanced use cases and strategies

Canary releases allow you to test new features on a small subset of users. Use feature flags to control the rollout. Monitor key metrics to ensure the feature performs as expected. Gradually expand the rollout until it reaches all users.

A/B testing and experimentation become easier with feature flags. Create multiple variations of a feature, each controlled by a flag. Assign users to variations and track their behavior. Analyze the results to determine the winning variation. Roll out the winner to all users.

Feature flags also enable non-engineers to run experiments. Product managers can control rollouts without deploying new code. They can target specific user segments and analyze results. This empowers data-driven decision making across the organization.

Server-side A/B testing offers several advantages over client-side testing:

• Faster performance - no need to load additional scripts

• More control - test any aspect of the system, not just UI changes

• Improved data quality - no skewed results from ad blockers or script errors

To implement server-side testing, use feature flags to control the variations. Assign variations based on user IDs or other attributes. Track conversion events and analyze the results. Use the learnings to iterate and optimize.

Targeted rollouts are another powerful use case for feature flags. Release new features to specific user segments based on attributes like:

• Geographic region

• Device type

• Account tier

• User behavior

This allows you to tailor experiences to different user groups. You can also mitigate risk by starting with a small, low-risk group. As you gain confidence, expand the rollout to larger segments.

Progressive delivery is a key benefit of feature flags. Release changes to a small percentage of users and monitor performance. If all looks good, gradually ramp up the percentage. If you detect issues, pause or roll back the change. This approach minimizes the blast radius of potential problems.