Pulse for Elasticsearch and OpenSearch

Pulse scores the health of every cluster it watches and keeps that score updated as conditions change. You see what's stable and what needs attention side by side, without opening a dozen consoles. It's the difference between hunting for problems and being told where they are.

The platform traces query patterns across each cluster and fingerprints them, then surfaces the slow, expensive and redundant queries behind your latency and cost. You get per-query latency, call counts and resource usage, plus concrete recommendations on indexes, projections and rewrites. That turns a vague "it feels slow" into a specific fix.

When an alert fires, Pulse goes past the raw signal and explains what likely caused it, along with the actions to take. It's built to complement monitoring tools like Grafana and Datadog rather than replace them, adding database-specific root-cause analysis those tools don't provide. The point is to cut the time between a warning and a fix.

Pulse flags the choices quietly inflating your cloud bill, such as over-provisioned nodes and indexes that cost more than they earn. The cost optimizer currently works for cloud deployments, so teams on self-managed infrastructure won't get every cost signal. Even a quarterly review can pay for itself when a cluster is over-built.

Every database you run lands in one view, across clusters, environments and clouds, and you can group them the way your team thinks about them. Each cluster's health score and active alerts sit right next to it. For anyone juggling multiple consoles, that consolidation is the feature that saves the most time.

A full REST API and an MCP server let you wire Pulse into your workflows, CI pipelines and tools. You can connect it to Claude Code, Codex and Cursor so your AI agents reason about the data platform using real insights instead of guesses. It also creates tickets from recommendations in systems like GitHub and Jira.