Rolling deployments for zero-downtime model updates

Rolling deployments provide a method for incrementally updating model versions without doubling GPU usage or risking downtime, addressing challenges faced by users of other inference platforms who had to choose between costly blue-green deployments and risky hard cutovers. This approach replaces replicas one at a time, shifting traffic gradually, which allows for pausing, resuming, or canceling the deployment as needed, thereby reducing operational overhead and enabling more frequent updates. Two provisioning modes, max_surge and max_unavailable, cater to different constraints, balancing latency sensitivity and compute cost, with each step in the deployment process being managed by a durable workflow engine that ensures stability and records deployment history. Customers using rolling deployments report a 50-60% increase in deployment frequency, thanks to the reduced risk and the ability to manage deployments without off-peak manual supervision. The system's design allows for efficient coordination of traffic and scaling, even amidst load spikes, by maintaining a controlled traffic split and allowing time for operators to confirm system stability before proceeding to the next step.

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