How to Build a Feature Flag Strategy in Statsig
Feature flags decouple deployment from release, letting your team push code to production without exposing it to users until you are ready. In Statsig, flags go beyond simple on/off toggles. They support percentage rollouts, user targeting, A/B test integration, and automated metric monitoring, making them a core part of your release and experimentation workflow.
But feature flags without strategy become a liability. Stale flags accumulate in your codebase, making code harder to read and debug. Overlapping flags create unexpected interactions. And flags without clear ownership become permanent fixtures that nobody dares to remove. This guide covers building a flag strategy that captures the benefits while avoiding the common traps.
Step-by-step guide
Define your flag taxonomy and naming convention
Before creating your first flag in Statsig, establish a naming convention that encodes purpose and ownership. Use the format: 'team_feature_variant' (e.g., 'growth_new_onboarding_v2' or 'platform_dark_mode'). Create categories for your flags: Release flags (temporary, for gradual rollouts), Experiment flags (temporary, for A/B tests), Ops flags (semi-permanent, for operational toggles like maintenance mode), and Permission flags (permanent, for plan-gated features).
- Document naming convention: team_feature_purpose
- Define flag categories: release, experiment, ops, permission
- Set expected lifetime for each category
Set up your Statsig project structure
In Statsig, organize your flags with tags that map to your taxonomy. Create tags for each team and each flag category. Set up environments for development, staging, and production so flags can be tested in lower environments before reaching users. Configure the Statsig SDK in your application with proper environment detection and a local cache fallback for when the Statsig service is unreachable.
Create your first feature gate with targeting rules
Navigate to Feature Gates in Statsig and create a new gate. Add targeting rules in order of specificity: first, a rule targeting internal team members by email domain (so your team always sees new features). Second, a rule targeting beta users by a user property like 'beta_access: true.' Third, a percentage rollout rule starting at 5% of remaining users. This layered approach gives you granular control over who sees the feature at each stage.
- Create the gate with your naming convention
- Add internal team targeting as the first rule
- Add beta user targeting as the second rule
- Add a percentage rollout starting at 5%
Configure metric monitoring for safe rollouts
Attach key metrics to your feature gate. Statsig automatically monitors these metrics during rollout and alerts you if the flag is causing a statistically significant regression. Add your core engagement metric (like daily active usage), your key business metric (like conversion rate), and your reliability metric (like error rate). If any metric degrades beyond the threshold, Statsig can automatically disable the flag, preventing a slow rollout from becoming a full-blown incident.
Build the progressive rollout playbook
Document your standard rollout procedure: 5% for 24 hours (catch major bugs), 25% for 48 hours (validate with metric monitoring), 50% for 48 hours (confirm at scale), 100% (full release). At each stage, review the Statsig pulse results and only proceed if metrics are neutral or positive. Create a Statsig saved view showing all gates currently in rollout so the team has a single dashboard of in-progress releases.
- Document percentage stages and hold durations
- Define go/no-go criteria for each stage
- Create a team dashboard of in-progress rollouts
Set up flag lifecycle management
Create a process for flag cleanup. In Statsig, add an 'expected removal date' in the flag description when creating it. Set up a monthly review where the team identifies flags that have been at 100% for more than 2 weeks (release flags) or more than the experiment duration (experiment flags). Create Jira or Linear tickets for flag removal as part of the release process, not as an afterthought.
Implement flag dependencies and mutual exclusion
For flags that interact with each other, use Statsig's layer system to ensure mutual exclusion between experiments. If two experiments modify the same user flow, they must be in the same layer so a user is only in one at a time. Document flag dependencies in the gate description. For example, if feature B depends on feature A, note this so feature A's flag is not removed while feature B's flag is still active.
Common mistakes
Not cleaning up flags after full rollout
The most common feature flag mistake is leaving them in the code after the feature is fully released. Every flag adds a conditional branch that makes code harder to read, test, and debug. Build flag removal into your definition of done: a feature is not shipped until the flag is removed from the codebase.
Using flags as a permanent permission system
While some flags are legitimately permanent (plan-gated features), using temporary release flags as a permanent authorization mechanism creates a shadow permission system that is undocumented and fragile. If a feature should be gated by plan, implement it in your proper authorization layer and use the flag only during the rollout period.
Rolling out to 100% without metric validation
Skipping metric monitoring and jumping from 5% to 100% negates the primary benefit of gradual rollouts. Even if manual testing passes, edge cases at scale can cause regressions. Always hold at 50% for at least 24 hours with metric monitoring before going to 100%.
Tips
Use Statsig's 'Override' feature to let individual users opt into or out of specific flags, useful for debugging and customer support.
Create a Statsig dynamic config alongside each feature gate to manage feature-specific settings (like thresholds or copy) that you might want to change without a code deploy.
Set up a Slack notification for when any flag's monitored metrics hit the alert threshold, so the team can investigate immediately rather than waiting for the next review.
Tag every flag with its creator and the Jira or Linear ticket it relates to, so ownership is always clear even months later.
How Vantage helps
Vantage helps PMs plan feature rollouts as part of their PRDs. When you define a feature in Vantage, you can specify the rollout strategy as a requirement, and generated tickets will include flag creation, metric monitoring setup, and flag cleanup tasks, ensuring the full feature flag lifecycle is tracked from the start.