AWS AppConfig launches managed A/B testing and experimentation tools
AWS AppConfig now offers managed tools for running A/B tests and feature experiments, removing the need for separate infrastructure. These tools incorporate Amazon's best practices and AI-driven guidance to help users build robust experiments with controlled exposure and traffic allocation. Available for applications running on various AWS services and on-premises, this feature enables data-driven decision-making for feature rollouts. Users set up experiments in AppConfig and analyze results via CloudWatch or other tools, promoting winning variations through safe rollouts.
- →General availability of AWS AppConfig managed experimentation tools
- →AI-assisted experiment design and configuration options
- →Support for diverse application environments
- →Integration with CloudWatch for result analysis
- →Safe promotion of winning treatments
Features (2) ›
- General availability of AWS AppConfig managed experimentation tools
AWS AppConfig now offers managed tools for A/B testing and feature experiments, eliminating the need to build or manage separate experimentation infrastructure. These tools are built on Amazon's best practices and include AI-driven guidance for experiment design.
- AI-assisted experiment design and configuration options
The tools provide AI-assisted guidance to validate experiment setups against Amazon's best practices, aiming for sufficient statistical power. Users can define feature variations, target audiences using a rule builder, and set traffic allocation percentages via the AWS Management Console, CLI, API, or AWS CDK.
Enhancements (3) ›
- Support for diverse application environments
Experiments can be run across applications hosted on Amazon EC2, AWS Lambda, Amazon ECS, Amazon EKS, and even on-premises servers via the AWS AppConfig Agent. This broad compatibility allows for comprehensive testing across different deployment scenarios.
- Integration with CloudWatch for result analysis
After setting up and running experiments within AWS AppConfig, users can analyze the results using Amazon CloudWatch or their existing analytics tools. This allows for seamless integration into current monitoring and reporting workflows.
- Safe promotion of winning treatments
At the conclusion of an experiment, the winning treatment can be promoted to production using the standard safe rollout process of AWS AppConfig. This ensures a controlled and gradual introduction of successful variations to end-users.
https://aws.amazon.com/about-aws/whats-new/2026/6/aws-appconfig-experimentation/