Autoscaling

The autoscaling feature adjusts resources based on demand. A deployment can use autoscaling to scale resources as needed, ensuring sufficient capacity to meet workload requirements. In Elastic Cloud Enterprise, Elastic Cloud on Kubernetes, and Elastic Cloud Hosted deployments, autoscaling follows predefined policies, while in Serverless, it is fully managed and automatic.

Cluster autoscaling allows an operator to create tiers of nodes that monitor themselves and determine if scaling is needed based on an operator-defined policy. An Elasticsearch cluster can use the autoscaling API to report when additional resources are required. For example, an operator can define a policy that scales a warm tier based on available disk space. Elasticsearch monitors disk space in the warm tier. If it predicts low disk space for current and future shard copies, the autoscaling API reports that the cluster needs to scale. It remains the responsibility of the operator to add the additional resources that the cluster signals it requires.

A policy is composed of a list of roles and a list of deciders. The policy governs the nodes matching the roles. The deciders provide independent estimates of the capacity required. See Autoscaling deciders for details on available deciders.

Cluster autoscaling supports:

• Scaling machine learning nodes up and down.
• Scaling data nodes up based on storage.

Autoscaling only scales data nodes up and doesn't scale them down. If you delete data and want to reduce the size of a data tier in Elastic Cloud Hosted or Elastic Cloud Enterprise, adjust its size manually.

In Elastic Cloud on Kubernetes, storage can't be scaled down. For more information, refer to the storage restrictions for ECK autoscaling.

Trained model autoscaling automatically adjusts the resources allocated to trained model deployments based on demand. This feature is available on all cloud deployments (ECE, ECK, ECH) and Serverless. Refer to Trained model autoscaling for details.

To ensure availability and avoid unnecessary scaling, trained model deployments operate with defined cooldown periods.

Trained model autoscaling supports:

• Scaling trained model deployments

Find instructions on setting up and managing autoscaling, including supported environments, configuration options, and examples:

• Autoscaling in Elastic Cloud Enterprise and Elastic Cloud Hosted
• Autoscaling in Elastic Cloud on Kubernetes
• Autoscaling deciders
• Trained model autoscaling