Feature Flag Rollout Simulator

Feature flags decouple code deployment from feature release. New code ships hidden behind conditional gates, so a feature can be turned on for 1% of users, watched for problems, then ramped to 100% — or killed instantly with a single switch — without redeploying or restarting the application.

Mental Model

Feature flags decouple code deployment from feature release. Rather than deploying a giant merge block and hoping it works, code is deployed hidden behind conditional gates.

• Progressive Delivery: Releasing a feature gradually (e.g., to 1%, 5%, then 100% of users) to observe system health and conversion rates before exposing all users.
• Segment Targeting: Overriding percentages to force specific cohorts (like internal testers or beta users) into a new feature variant directly.
• Kill Switch: An emergency override that instantly disables a feature cluster-wide and redirects all traffic to the safe control variant without requiring a code redeployment or server restart.

Components and Why They Exist

Users Group

This component exists to represent active incoming traffic segments with diverse properties (like location, email, or client type) to test targeting. It sends request streams with user identifiers and client segment tags to verify targeting and rollout rules. In the demo, observe request particle flows and conversion rates across different variant targets.

Flag Service Registry

This component exists to store flag rules and publish dynamic updates to application servers. It holds the rules mapping keys to variants, manages percentage limits, and hosts the global kill switch. In the demo, watch the active rules configuration panel and sync propagation cues in the demo.

App Instance (CONTROL)

This component exists to run the legacy codebase, establishing the baseline conversion metrics for comparisons. It handles requests that do not fall into any active rollout bucket or targeting rule. In the demo, watch request counts, error rates, and conversion metrics on the control server dashboard.

App Instance (V1)

This component exists to run the first candidate code path under percentage-based progressive exposure. It runs alternative frontend styling or backend methods for users matching the V1 target bucket. In the demo, watch the metrics cards compared to Control when variant V1 is active.

App Instance (V2)

This component exists to run the second alternative candidate version, enabling A/B/C testing. It processes transactions for users assigned to the V2 segment. In the demo, watch metric spikes or conversions associated with V2 variant actions.

App Instance (V3)

This component exists to run the third alternative candidate version, helping to test targeted overrides. It handles request transactions for user groups mapped specifically to the V3 cohort. In the demo, watch the conversion rate and check targeting rules bypass flags in action.

Edge Cases and Misconceptions

• Flag configuration is not free: Feature flags add code complexity. Every flag introduces branching conditional checks (if (flags.isEnabled('new-feature'))). If stale flags are not regularly removed from code after reaching 100% rollout, the codebase accumulates technical debt, increasing test surface areas and code confusion.
• Stale cache synchronization: In client-side SDK mode, clients poll the flag registry at intervals (e.g., every 10 seconds). If you trigger a kill switch, clients will continue to execute the broken variant for up to 10 seconds until their local cache refreshes. Server-Sent Events (SSE) streaming resolves this by pushing updates in real-time.
• Targeting order evaluation: Rule evaluation order is critical. Targeting rules (e.g., “employees get V1”) must be evaluated before percentage rollouts (e.g., “10% of users get V1”). If percentage rollouts are checked first, an employee might randomly get assigned to Control, violating the targeting policy.