10 Feature Flag Best Practices You Should be Using in 2024
Unlock the power of feature flags with these 10 essential best practices. Learn how to streamline releases, boost experimentation, and optimize your SaaS product's development.
Curious about why implementing feature flagging before rolling out new features can help you retain and even boost the number of users that find value in your SaaS product?
In this guide, we’ll take a look at 10 feature flag best practices you should follow if you want to get the most out of this handy experimentation feature.
Let’s get started with the first tip.
1. Understand your why for using feature flags
A successful feature flag approach requires strategic planning to make sure they are as effective as they can be. Before diving into implementation, it's crucial to ask yourself what the goal behind using feature flags is:
Do you aim for safer releases?
Are you looking to do some data-driven experimentation?
Are you looking for a way to disable problematic features quickly?
Different objectives may require different tools — for example, an open source flagging package might be perfectly suited to make near-immediate changes for infrastructure use cases like disabling down servers or problematic features. But it may lack the type of granular targeting and careful randomization needed to run reliable experiments.
Once you understand your objectives, carefully evaluate your toolkit and its ability to meet each case.
You should also think strategically about each individual use of flags.
If it's a controlled release, map out the rollout phases. If experimentation is the focus, determine the metrics that measure tangible business success for your company (key metrics like revenue and retention should be on your radar).
Understanding dependencies is also vital. Can multiple flags interact, causing unexpected results? Document these relationships to avoid any surprises.
Finally, decide how you'll manage your feature flags. Simple use cases might only need configuration files. Larger, more complex applications might benefit from an in-house solution or a specialized feature management platform.
But remember, a comprehensive strategy goes beyond the technical. Establish clear naming conventions, keep meticulous documentation explaining each flag's role, and have a process to clean up obsolete flags to avoid technical clutter.
2. Use descriptive flag names
Think of your feature flags like variables in your SaaS product’s code — a confusing, non-descriptive name makes it much harder to understand their purpose. Clear and descriptive naming conventions are essential for several reasons:
It makes collaboration easier: Everyone on your team can quickly grasp what a feature flag controls, saving time and reducing miscommunication.
Allows for self-documentation: Descriptive names make your flags self-documenting, reducing reliance on separate documentation that may become outdated.
It’s easier for tracking and auditing: Descriptive names are vital for rollbacks, troubleshooting, and understanding the history of feature flag changes.
For example, instead of a vague name like "new_feature_toggle," a more descriptive name like "enable_redesigned_checkout_page" provides instant clarity.
Consider adopting a consistent pattern for naming flags that includes elements like the feature area, the variation it controls, and perhaps even its status (e.g., "checkout_redesign_beta" or "promotional_banner_experiment").
3. Make sure your feature flags are temporary
While feature flags are super useful for testing features, it's easy to fall into the trap of letting them linger in your codebase long after their initial purpose. This can lead to technical debt.
Here's why removing obsolete flags is a must:
Clearer code: A codebase with fewer feature flags is easier to read and understand. Developers can focus on active features rather than deciphering outdated logic paths.
Less risk: Old flags become potential liabilities. Unexpected interactions with new code or changes could lead to errors.
Easier experimentation: Cleaning up old flags frees up your team’s time, bandwidth, and resources to focus on new experiments and innovations.
To make sure your feature flags stay temporary, incorporate cleanup into your process (a topic we’ll develop further down the list).
Remember, feature toggle best practices also include regularly reviewing active flags, checking their status, and having a clear plan for removing those no longer needed.
4. Power product changes through feature flags
Imagine being able to instantly modify your product's user experience without pushing a single line of code.
That's the power of feature flags. By decoupling feature management from code deployments, you can iterate, experiment, and personalize experiences in real time.
Feature flags, especially when managed through a centralized platform, give you unprecedented control over your product's evolution:
Rollout features gradually: Release new features to a small percentage of users initially, then gradually increase exposure as you gain confidence. This minimizes risk and allows for valuable feedback.
A/B test multiple variations: Experiment with different versions of a feature to see which performs best, all while the feature is live. This data-driven strategy helps guarantee that changes actually improve the user experience.
Dynamic configuration: Fine-tune your product's settings and parameters on the fly, tailoring the experience to specific user segments or responding to real-time events. No code changes required.
This dynamic approach allows product teams to iterate rapidly, test hypotheses, and personalize experiences, all while maintaining a stable codebase and minimizing risk.
Specialized experimentation platforms like Eppo also offer advanced capabilities like sophisticated targeting, real-time monitoring, and full native data warehouse integration for trustworthy data based on your company’s internal source of truth.
5. Automate flag cleanup (if possible)
We've already discussed why making feature flags temporary is essential. However, manually hunting through your codebase to remove old flags is tedious and error-prone. One potential solution to preventing technical debt caused by stale flags is automation.
It’s important to think about this carefully in each use case for feature flags and ensure that automated flag cleanup won’t introduce any risks. Assuming the context allows for it though, here are some tips to keep in mind:
First, you should set clear time limits when creating a feature flag. This could be aligned with a specific release cycle, the duration of an experiment, or a fixed date.
Next, implement a system of reminders and alerts to notify the responsible team members when a flag nears its designated expiry. This allows you to review flags periodically and make informed decisions on whether to extend the flag or remove it.
If possible, use automated removal tools offered by some feature management platforms. These tools can remove flags automatically after a certain period or based on specific criteria, streamlining your cleanup process.
6. Roll out most features progressively
One of the major strengths of feature flagging is the ability to release new features in a controlled and gradual manner. This greatly reduces risk compared to the traditional "big bang" approach where a feature is made available to everyone at once.
With progressive rollouts, you start by exposing the new feature to a small subset of users. This could be internal testers, a beta group, or a percentage of your overall user base.
Remember to closely monitor the performance of your feature during this initial phase. Look for bugs, unexpected behavior, or any negative impact on key metrics (like a strong dip in customer retention). Be wary of early fluctuations, as these could be due to randomness and not an actual impact from the feature. Statistical analysis like that built-in to platforms like Eppo will help you gauge what is real and what is randomness.
If everything goes smoothly, you can gradually expand the rollout to larger segments of your audience. This phased approach gives you the confidence to iterate and address any issues before the feature reaches the entirety of your user base.
Here are a few ways to control progressive rollouts with feature flags:
Use a percentage-based rollout: Release the feature to a specific percentage of users, gradually increasing over time. This is the most traditional way of doing rollouts since you’re not using any particular criteria other than percentages to get statistical significance.
Do it through user segmentation: Target specific groups based on demographics, behavior, or other criteria. This helps if you want to see if this particular new feature works well with a particular segment of your user base.
Consider canary releases: This means you deploy the new feature to a small fraction of production infrastructure, minimizing potential customer frustration if the feature doesn’t jell with users.
If the goal of your progressive rollout is to measure impact to key metrics, you’ll need to carefully plan the allocation of users who receive the old experience vs. new feature, since there are statistical implications to altering this allocation over time. Without careful planning, you could lose efficiency at best, or end up with incorrect results due to Simpsons Paradox.
7. Limit the scope of your feature flags
With feature flags, creating overly broad flags that control multiple functionalities or sprawling sections of your codebase can quickly lead to complexity and confusion for your entire development team.
The best practice is to keep your feature flags narrowly focused. Each flag should ideally control a single, well-defined feature or a small, isolated component within a service. This makes the purpose of each flag clear and reduces the risk of unintended side effects.
Limiting scope has several benefits, such as:
Easier debugging: If an issue arises, it's much simpler to pinpoint the root cause when flags have a limited area of impact.
Reduced dependencies: Granular flags are less likely to become entangled with other parts of the system, minimizing potential conflicts or unexpected interactions.
Improved maintainability: A codebase with smaller, focused flags is easier to understand and maintain over time, making development quicker in the long run.
8. Establish clear ownership over feature flags
Feature flags shouldn't exist in a vacuum, especially in larger projects. To guarantee their effective management and prevent them from becoming abandoned code, it's critical to define clear ownership for each flag.
This means assigning an individual or a team responsible for the entire lifecycle of a flag. Think of them as "flag custodians," responsible for the creation, usage, monitoring, and eventual retirement of feature flags.
Having designated ownership brings accountability. It ensures that someone is keeping track of the flag's status, preventing it from lingering indefinitely. Document this ownership alongside the flag itself, making the information easily accessible to your team.
9. Use flag change approval processes
Making changes to feature flags, particularly in production environments, shouldn't be a free-for-all. Implementing an approval process adds a valuable layer of control and helps prevent unintended results (like setting up flags incorrectly or delaying testing).
The level of formality in your approval process will depend on the scale of your project and the sensitivity of the features being controlled.
For smaller teams, this might involve a simple review by a senior developer or a designated product manager. In larger organizations, a more structured workflow with multiple approvers might be necessary.
Here's why approval processes matter:
Risk mitigation: An additional review step helps catch potential errors or conflicts before changes go live, protecting your production environment.
Coordination: In teams with multiple people working on feature flags, an approval process ensures everyone is aware of the changes and their potential impact.
Audit trail: Keeping a record of flag changes and approvals creates a valuable reference for troubleshooting or for understanding the history of your product's evolution.
10. Document every flag
Think of feature flag documentation as the instruction manual for your evolving codebase. Without it, you risk confusion, misinterpretations, and the dreaded "What does this flag even do?" moments.
Thorough documentation has far-ranging benefits:
Knowledge preservation: Documentation ensures knowledge isn't lost as team members change or memories fade.
Easier collaboration: Everyone working with the flags has a shared understanding, improving communication and coordination.
Smoother maintenance: Clear documentation makes it quicker to understand the dependencies and potential impacts when flags need to be modified or removed.
Here's what to document for each flag:
Purpose: Clearly describe the why behind the flag. What feature does it control, and what problem is it intended to solve?
Configuration: Detail how the flag is configured (values, targeting criteria, etc.).
Ownership: Specify the individual or team responsible for the flag's lifecycle.
Expected lifespan: Indicate whether it's a long-term flag, tied to a specific experiment, or if it has a planned removal date.
This documentation shouldn't live in a separate, forgotten file. Ideally, it should be directly associated with the flag in your code or within your feature management platform.
What’s more, if the feature management platform you use is data warehouse native, like Eppo, you can rest easy knowing that discrepancy-free data will be readily accessible when needed.
Quick extra tip: Treat documentation as an essential part of the feature flag creation process. It might feel a bit tedious at first, but it's an investment that pays dividends for the long-term health and readability of your codebase.
Next steps
With these handy feature flag best practices, you should be ready to start experimenting.
If you want to do it with accurate data and can’t afford the time and effort of having a statistician next to you monitoring the data, you should consider using Eppo.
Eppo is an experimentation and feature management platform designed to unlock the full potential of feature flags. With Eppo, you can go far beyond simple on/off toggles, enabling safe rollouts, data-driven experimentation, and streamlined management for your features.
Here’s why Eppo is ideal for feature-flagging experiments:
Sophisticated targeting: Deliver feature variations based on user attributes, demographics, device types, behavioral patterns, or any criteria important to your business.
Warehouse-native architecture: Eppo sits directly on top of your data warehouse (Snowflake, BigQuery, Redshift, etc.). This ensures calculations use your most accurate and complete data, minimizes data duplication, and reduces cost.
Advanced experimentation: Eppo supports CUPED experiment acceleration, sequential analysis, and contextual bandits. This lets you discover winning variations faster and optimize personalized experiences with unparalleled precision.
Rigorous and trustworthy: Eppo prioritizes statistical rigor and fosters a culture of experimentation within your organization. Make confident, data-driven decisions to improve your product with less risk.
Kill switch capabilities: Quickly disable problematic features without full redeployments, protecting your user experience.
Unlock the power of feature flags with these 10 essential best practices. Learn how to streamline releases, boost experimentation, and optimize your SaaS product's development.
Table of contents
Ready to go from knowledge to action?
Talk to our team of experts and see why companies like Twitch, DraftKings, and Perplexity use Eppo to power experimentation for every team.
Get a demo
Keep reading
