What it is: Predictive auto-scaling is an enhancement to Atlas auto-scaling. This predictive system uses demand forecasting to anticipate workload surges, meaning clusters' compute can scale up preemptively using past cyclical patterns, providing clusters with the necessary resources before a workload spike occurs. Note that this only occurs in scale-up events; scale-down is still triggered only by existing scaling logic. This enhancement adheres to the min and max cluster tiers configured by users. Who it’s for: Customers who enable auto-scaling on their clusters to automatically handle increases and decreases in usage and traffic. This is ideal for users with cyclical or seasonal workloads such as batch jobs or applications with predictable, cyclical high user demand (such as consistent daily or weekly spikes). Why it matters: Prior to predictive auto-scaling, scale-up events have only been reactive, only being triggered after a spike in demand is observed. Predictive auto-scaling reduces resource constraints and maintains consistent query performance and availability during predictable, cyclical spikes (e.g., daily morning usage, scheduled batch jobs, or seasonal trends). This helps teams to stay ahead of demand by proactively handling dynamic workloads that see large, rapid, and predictable increases in workload. How to get started: This functionality began rolling out on November 3rd to some existing auto-scaling clusters and will be available for all eligible clusters in the next few months. No additional setup or configuration is required to utilize this new feature other than enabling auto-scaling for the cluster.