Control who sees what and when with built-in strategies like gradual rollouts and custom constraints for geography, customer type, or telemetry.

Industry solutions

Ship checkout, cart, pricing, and fulfillment changes behind flags. Release templates create consistent rollout paths; safeguards use conversion, error, and latency signals to progress or pause exposure; instant rollback helps contain issues before they affect wider shopper populations.
Help product, operations, and engineering leaders see who changed a flag, who approved it, which environment changed, and which customer segments or regions were affected. Use change requests and granular RBAC to protect high-traffic paths.
Run Unleash self-hosted or in a single-tenant private instance, and evaluate flags locally in your applications. Target features without sending shopper, session, or order context outside your infrastructure.
Test new checkout steps, recommendation models, or promotional flows with narrow segments while keeping kill switches and approval workflows in place. Move fast on conversion learning without turning every experiment into a site-wide launch.
And what happens when FeatureOps becomes part of how you ship.
Teams:
Ship behind flags, widen exposure when metrics stay healthy, and pause when signals drift.
Standardize templates, safeguards, and cleanup so squads share one FeatureOps posture.
Operations:
Map SSO, RBAC, audit trails, and change requests into existing control frameworks.
Pair progressive exposure with kill switches and instant rollbacks at runtime.
Control who sees what and when with built-in strategies like gradual rollouts and custom constraints for geography, customer type, or telemetry.



Traditional deployments carry risk. When you ship a retirement benefits module that needs to integrate with legacy COBOL systems and new AI recommendation engines, for example, a single failed release can break service for millions of customers. Feature flags reduce the blast radius by enabling gradual rollouts and instant rollbacks. This becomes more complicated when […]


Feature flags decouple deployment from release so you validate checkout and catalog behavior with narrow segments first. That pattern supports safe experimentation on live traffic without site-wide risk.
You define thresholds for metrics like error rates, latency, or conversion drift, and Unleash can pause rollouts when signals cross safety bands. That keeps widening exposure a data-backed decision during high-traffic periods.
Evidence typically includes who approved the change, which environment and segments were affected, and what metrics justified the next step. Detailed audit logs support that narrative.
See the gradual rollout mockup for activation strategies, targeting, and operations notes in one long-form page.