Best practices for ElastiCache Redis autoscaling–and how to do better

ElastiCache Redis is capable of scaling to handle significant data and query demands, but careful management of its autoscaling features is crucial to avoid overprovisioning. Scaling decisions should be based on key metrics like RAM utilization, CPU load, and network bandwidth, each presenting unique challenges, particularly the tendency for EC2 instances to throttle bandwidth after brief burst periods. ElastiCache Redis offers scaling through shards and replicas to manage data distribution and throughput, though official AWS guidelines on autoscaling—such as disabling scale-in and scaling on a single metric—pose potential inefficiencies. While autoscaling was revolutionary, its complexity and the risk of outages during crucial business moments suggest that serverless-first solutions like Momento Cache, which automatically manages capacity and handles high transaction rates, may offer a more efficient alternative by mitigating the intricacies of capacity management.

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