A/B test models in production

Together AI’s endpoint-level A/B testing feature lets teams divide live inference traffic among one control deployment and up to 20 variants using fixed percentage cohorts, enabling measurement of real product outcomes such as user ratings, retention, and task completion without application-side routing logic. Experiments route traffic only through the control’s base traffic share, require variants to have zero endpoint traffic weight, and use percentages that remain independent of replica counts and autoscaling. Teams can begin with low exposure such as 95/5, ramp variants by replacing the experiment’s member configuration through etag-protected updates, and run multi-way tests so long as percentages total 100% and exactly one control is present. Platform metrics are available by deployment, while response metadata enables product analytics to attribute quality outcomes to each cohort. If a variant wins, it can be promoted through a blue-green rollout before the experiment is deleted; if it loses or degrades, removing it from the experiment or deleting the experiment returns traffic to the control. Requests can be assigned consistently using a stable sampling key, while key-less requests are sampled per request. In a live demonstration, observed traffic closely matched configured 95/5, 80/20, and 50/50 splits, and deleting the experiment restored all traffic to the control after propagation.

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