Why Consolidating Feature Flags and Experiments Cuts Hidden Costs

Description and use

Consolidating feature flag delivery and experiment measurement in AWS AppConfig cuts hidden costs by eliminating duplicate tooling, reducing SDK sprawl, lowering operational overhead, and ensuring consistent user assignment, while still allowing teams to use their preferred analytics platform.

Problem

Teams maintain separate systems for feature flag delivery and experiment measurement, incurring integration, SDK sprawl, operational overhead, and assignment drift costs.

Outcome

Consolidating feature flags and experiments into AWS AppConfig eliminates duplicate tooling, reduces SDK sprawl, lowers operational overhead, and ensures consistent user assignment, while still allowing teams to use their preferred analytics platform.

Workflow

• Define a feature flag in AppConfig.
• Create an experiment definition linked to the flag using CfnExperimentDefinition.
• Specify audience rules, traffic split, and treatment variations in the experiment.
• Deploy the experiment via AppConfig Agent or API.
• Monitor metrics during the experiment run.
• Promote the winning variation by updating the flag configuration while the experiment is active.
• Stop the experiment to finalize the rollout.
• Configure an external analytics platform to capture experiment data.
• Pay for AppConfig experimentation per run hour and standard configuration requests.

Evidence

Two systems to wire into your application, each with its own setup, authentication, and configuration.

Source

SDK sprawl. A flag‑evaluation SDK here, an experiment or analytics SDK there, multiplied across every language and every service you run.

Source

The flag is the experiment surface. An experiment selects an existing feature flag, and each treatment is a variation of that flag's configuration.

Source

The CfnExperimentDefinition construct is only in recent versions of aws-cdk-lib (2.268.0 or later).

Source