FeatBit solutions · The problem
AI generates code faster.
Bugs can spread faster, too.
More generated code means more changes to review and more chances for bugs to escape. When each deployment reaches every customer, one missed edge case can become everyone's problem.
See how to limit the impactThe solution
Minimize the impact of every bug that escapes.
FeatBit feature flags control who receives new behavior, how far it spreads, and how to return to a working path. Start with a small audience, observe real production behavior, and keep recovery ready while your team fixes the code.
Limit the first audience
Target an account, role, cohort, or small percentage so an unproven change does not reach every user.
Keep a working fallback
Restore existing behavior through a flag change when the candidate fails, while the fix is still being prepared.
Expand from evidence
Check errors, latency, and user outcomes before allowing a candidate to affect more customers.
Solution scenarios
Choose the boundary your release needs.
Explore a specific risk, the control that limits its impact, and example scenarios for putting it into practice.
Release with precision
Limit the accounts, cohorts, and percentage exposed to unproven changes.
Customer & Role Targeting
Pilot an AI-built reporting workspace with one customer's admins. Keep members and other accounts on the existing path.
See solution & scenariosSegment-Based Releases
Try a search rewrite with an opt-in beta or a regional pilot. Use the same limited audience across related feature flags.
See solution & scenariosProgressive Rollouts
Start an AI-assisted checkout with a small canary. Review errors and completion before more users receive the candidate.
See solution & scenariosContain production bugs
Shorten the time a bug affects users and keep a working fallback available.
Instant Feature Rollback
When a new checkout or exporter breaks, restore the working path through a flag change while your team prepares the fix.
See solution & scenariosSafer AI-Generated Code
Gate an AI-written calculator or parser behind a flag. Validate with a small audience and keep the existing implementation ready.
See solution & scenariosConfigure at runtime
Keep a bad default from affecting every customer.
Learn from production
Find hidden bugs and product regressions before expanding exposure.
Feature Experimentation
Compare two experiences with a stable feature flag split. Follow Exposure and Measuring through the primary metric, posterior distribution, and guardrails.
See solution & scenariosFeature Impact Monitoring
Explore the Release Health design: connect metric sources, define guard conditions, send webhook alerts on threshold breaches, and investigate with release context.
See solution & scenariosAutomate releases
Keep launch timing, incident response, and configuration changes under control.
Automatic Rollback
Connect health alerts to an OFF trigger or restore a previous rollout through the REST API, FeatBit CLI, or MCP, such as returning from 20% to 10%.
See solution & scenariosScheduled Releases
Deploy AI-assisted changes early, then schedule a reviewed pilot for a time when monitoring and recovery owners are ready.
See solution & scenariosTerraform Release Workflows
Manage FeatBit resource definitions as Terraform IaC. Use Git to review the configuration, apply a saved plan, and validate runtime behavior across environments.
See solution & scenarios