Elastic Inference
Elastic Cloud Serverless projects are autoscaled environments, fully managed by Elastic and available on Elastic Cloud. Availability This functionality is generally available and ready for production usage. Serverless interfaces and procedures might differ from classic Elastic Stack deployments. The Elastic Stack includes Elastic's core products such as Elasticsearch, Kibana, Logstash, and Beats. Availability This functionality is generally available and ready for production usage. Unless stated otherwise on the page, this functionality is available when your Elastic Stack is deployed on Elastic Cloud Hosted, Elastic Cloud Enterprise, Elastic Cloud on Kubernetes, and self-managed environments. This documentation corresponds to the latest patch available for each minor version. If you're not using the latest patch, check the release notes for changes.
Serverless
Generally available
Stack
Generally available
Inference is a process of using a machine learning trained model to make predictions or operations - such as text embedding, or reranking - on your data. You can use inference during ingest time (for example, to create embeddings from textual data you ingest) or search time (to perform semantic search based on the embeddings created previously). There are several ways to perform inference in the Elastic Stack, depending on the underlying inference infrastructure and the interface you use:
Inference infrastructure:
- Elastic Inference Service: a managed service that runs inference outside your cluster resources.
- Trained models deployed in your cluster: models run on your own machine learning nodes
- External inference: third-party services that provide models.
Access methods:
- The
semantic_textworkflow: a simplified method that uses the inference API behind the scenes to enable semantic search. - The inference API: a general-purpose API that enables you to run inference using EIS, your own models, or third-party services.
- The