Elastic AI Assistant brings generative AI to the Elastic Stack, helping SREs and security teams investigate issues, write queries and resolve incidents faster.
The Elastic AI Assistant is woven into Observability and Security solutions on the Elastic Stack. It explains errors and alerts, generates ES|QL and KQL queries from natural language, suggests runbook steps and summarizes incident context — all grounded in the customer’s own logs, metrics and traces indexed in Elasticsearch.
Because Elastic is open and flexible, teams choose their own LLM provider (including self-hosted models) for the assistant, keeping data governance intact. The assistant works alongside Elastic’s APM, logs, synthetics and profiling tools, making it useful across detection, investigation and remediation.
The assistant works across Elastic’s Observability and Security solutions, helping users generate queries, interpret alerts and summarize incidents using context from their own Elasticsearch data. A key design choice is model flexibility: organizations can connect their preferred LLM provider, including self-hosted models, keeping data governance intact. Combined with Elastic’s open architecture, logs, metrics, traces and profiling data all feed into AI-assisted investigation workflows.
Elastic offers a free tier and pay-as-you-go cloud pricing; AI Assistant capabilities are included in Elastic’s Observability and Security solutions on paid tiers.
Organizations running on the Elastic Stack that want AI assistance inside their existing observability and security workflows.
Elastic has a free tier, but AI Assistant features are part of paid Observability/Security plans.
Elastic’s platform is fully API-driven, and the assistant works alongside programmatic querying and automation.
Model flexibility and grounding in your own Elasticsearch data give it a governance advantage over closed assistants.
Yes — Elastic supports connecting your choice of LLM providers, including self-hosted options, for AI Assistant features.
SREs use the assistant to generate and explain queries, summarize incidents and suggest remediation steps based on their own telemetry. Security teams apply it to investigation workflows over logs and detections. Because teams choose the underlying model, it fits organizations with strict data governance requirements.
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