Software development teams face pressure to release updates and new features frequently, but traditional deployment methods can introduce performance issues and negatively impact user experience. Measuring the impact of each feature in real-time is challenging, leading to difficulty in making data-driven decisions about feature rollouts.

Split provides a feature management and experimentation platform that enables development teams to decouple deployment from release, accelerating feature delivery while minimizing risk. The platform combines feature flags with automated rollout monitoring and A/B testing, allowing teams to measure the impact of each feature as it's rolled out. By connecting feature flags to contextual data, Split provides feature-level observability, enabling teams to detect and resolve issues instantly and make informed decisions about feature releases. The platform's patented attribution engine connects data to every feature, providing deeper contextual insight across releases.

Feature flags for decoupling deployment from release, enabling faster and safer rollouts

Automated rollout monitoring and A/B testing for data-driven decision-making

Instant Feature Impact Detection (IFID) to pinpoint issues during progressive delivery

Feature-level observability for deeper contextual insight across releases

Patented attribution engine to connect data to every feature

Integrations with customer data, performance monitoring, and error tracking systems

SDKs for various languages and frameworks, including JavaScript, PHP, Python, React Native, and Ruby

Data export to tools like Datadog, Jira, New Relic, and Sumo Logic

Split is designed for product development teams, engineering leaders, and enterprise organizations that need to release features frequently, measure their impact, and minimize risk.