Platforms 5

Harness Feature Management and Experimentation, or FME, combines Harness's delivery platform with the feature management and experimentation heritage of Split.

The platform spans release and measurement

Harness FME covers feature flags, treatments or variations, release monitoring, cloud experimentation and warehouse native experimentation. This makes it relevant to both engineering and product experimentation teams.

Governance is one of its strongest areas

Harness supports environment level approval flows. A proposed feature flag change can be submitted for approval, reviewed as a diff, and locked while the approval is pending.

Harness also supports policy as code using Open Policy Agent and Rego for rules such as naming conventions, required tags, ownership and production approval requirements.

Feature changes can live inside deployment pipelines

Harness FME integrates with Harness pipelines so a delivery workflow can create or update flags, modify rollout behavior and apply approval or failure strategies in the same auditable process.

build
  ↓
deploy
  ↓
approval
  ↓
change feature exposure
  ↓
measure

OpenFeature support is broad

Harness documents official OpenFeature providers across web, React, Angular, Android, iOS, Java, Node.js, Python, .NET and Go. The provider wraps the Harness FME SDK, so OpenFeature standardizes the application facing evaluation API while Harness still supplies the underlying behavior.

Harness is also extending the model to AI configuration

Harness AI Configs applies the same runtime governance idea to prompts, model choices and AI parameters. A schema defines the allowed structure, environments hold their own values, and changes can move through approval and audit workflows without requiring an application deployment.

This is a natural extension of Harness's delivery and governance orientation: AI behavior becomes another production surface that can be versioned, evaluated, promoted and rolled back.

When Harness is a strong choice

Harness is especially attractive when feature management is expected to participate in a larger software delivery platform with formal governance, release pipelines and experimentation.

The tradeoff is that organizations not otherwise adopting Harness may be taking on a broad platform for a capability that could be purchased or operated more narrowly elsewhere.

Further reading

Feature Flags series

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