Keep Every Metric. Keep Your PromQL. Cut Your Bill in Half.

  • Elastic
Keep Every Metric. Keep Your PromQL. Cut Your Bill in Half.

About this webinar

Elasticsearch now stores metrics in a columnar time series engine built for high-cardinality data: up to 2.5x more storage-efficient than Prometheus, with ES|QL queries running up to 30x faster on gauge averages and counter rates, and no custom-metric classification or cardinality-based billing.

In this session we'll walk through what we recently shipped and what it could mean for your stack and share some anecdotal stories of things we've been seeing in the field.

What you will learn

  1. Why the performance and cost numbers hold up — how the columnar TSDS engine stores time series, and why Elasticsearch doesn't keep a per-series in-memory state that scales with cardinality, so new Kubernetes labels or OTel dimensions don't drive memory pressure.
  2. How to get your existing metrics in without rewriting anything — Prometheus Remote Write, the native OTLP endpoint, and what changes when your ECH deployment moves to the native path on Elastic Stack 9.5.3 or later (histogram fidelity, temporality as a dimension, and the queries worth reviewing).
  3. How to explore and migrate — charting metrics in Discover with one TS query, splitting by any dimension, and using the Observability Migration Platform to convert Datadog and Grafana dashboards, alert rules, and PromQL into Kibana-native outputs.

Speakers

  1. Scott Bekker Webinar Moderator Future B2B
  2. Frank Swain Principal Competitive Intelligence Manager Elastic
  3. Peter Simkins Principal Solutions Architect Elastic
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