Split: Feature Management and Experimentation

Split Software

Split: Feature Management and Experimentation

Split Software

Speed up development cycles, reduce release risk, and measure the impact of every feature

The new mandate for software development is impact: not just delivering more features with speed and reliability but delivering more features that matter for making a difference in the lives of consumers, staying ahead of the competition, and actually moving the needle for the business.

Unfortunately, for most engineering teams, impact is elusive due to:
  • Delivery delays: feature code sits in “merge hell” for weeks or months
  • Unreliable releases: 23% of releases fail, breaking the user experience
  • Misguided features: 80% of features have negative or no business impact

Why choose Split?

Split provides a unified platform to address these issues, helping development teams manage feature flags, monitor release performance, and surface data to make ongoing, data-driven decisions. Split’s Azure DevOps integration increases agility and safety by weaving robust feature flagging capabilities into the development lifecycle. Once configured, users can associate Split feature flags with work items to track features in Azure Boards and leverage Split’s granular targeting capabilities to define custom rollouts to run in Azure Pipelines.

As a result, engineering teams achieve greater impact from their software development work by deploying frequently, increasing the reliability of each release, and creating customer feedback loops.

Deploy frequently

Unblock teams by separating deploy from release. Merge code quickly and test in production.

Increase reliability of each release

Use progressive rollouts to monitor for impact. Get immediate feedback on errors and performance degradation and use the instant kill switch to address the source of the issue.

Create dynamic customer feedback loops

Ingest data, map features to guardrail and business metrics, and export to your preferred BI tools. Quickly and accurately attribute features to outcomes to drive data-driven decision-making.

Common use cases:

  • Continuous Integration/Continuous Delivery
  • Targeted rollouts
  • Dark launches
  • Canary releases
  • A/B testing
  • Ongoing experimentation
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