Elastic announced new capabilities that bring the same scale, performance, and operational simplicity that have made Elastic a trusted platform for logs to metrics.
With native Prometheus and PromQL support, out-of-the-box Kubernetes investigation workflows, and automated migration from Datadog and Grafana, Elastic now delivers a unified platform for metrics and logs. Built on Elasticsearch's columnar metrics engine, the platform can query metrics up to 30x faster than Prometheus and store data up to 2.5x more efficiently, without cardinality limits or custom metric penalties.
The metrics landscape has changed dramatically. Kubernetes and microservices have already pushed observability systems from thousands to millions of time series. Now AI workloads are accelerating that growth, making metrics not only a scale challenge but also a strategic cost and reliability problem. Most platforms make that growth expensive: premium vendors increase costs as cardinality grows, while lower-cost alternatives fragment metrics and logs across separate backends and query languages. The result is that teams often reduce data collection to control costs, leaving engineers with less context when incidents occur.
Elastic Observability addresses both problems in a single platform that stores OpenTelemetry, Prometheus-native, and application-defined metrics at full resolution alongside logs and traces, with no separate backends and no retention trade-offs. The release spans the metrics engine and the capabilities built on it:
- Native PromQL and Prometheus Remote Write: PromQL queries run natively in Kibana and Prometheus metrics arrive via Remote Write, so existing dashboards, alert rules, and scrape configs work without modification.
- Out-of-the-box Kubernetes workflows and content: SREs now go straight from an alert to the root cause through out-of-the-box agentic workflows, alert templates, ML anomaly detection jobs, and pre-built dashboards that activate at ingest for Kubernetes. SRE teams do not need to configure infrastructure from scratch before they get value.
- Agentic investigations: When an alert fires, Elastic correlates metrics, logs, and traces that already share a single backend, using workflows with ML anomaly detection to surface what changed and how severe the deviation is before anyone is paged. The Observability MCP App and agent skills bring the same investigation capabilities to Claude, Cursor, VS Code, and any MCP-compatible tool.
- Automated migration from Datadog and Grafana: The Observability Migration Platform converts dashboards, alert rules, and PromQL queries into Kibana equivalents automatically, so teams move what they've already built rather than rebuilding it.
"Elastic was already the platform many SREs trusted for logs at scale. Now we're bringing that same impressive scale, performance, and operational simplicity to metrics, delivering up to 30x faster metric queries than Prometheus, native Prometheus compatibility, and a more predictable cost model for high-cardinality metrics," said Baha Azarmi, GM, Observability at Elastic. "With a single backend for every signal, a single query language, and investigations that start before anyone is paged, SREs get complete context at the moment they need it most — without the bills that have forced teams to compromise on the data they keep."
“As we’ve moved more applications into Kubernetes and expanded our cloud footprint, data is growing rapidly and our need for granular, high-cardinality metrics is increasing," said Jeff Beagley, manager of DevOps, SRE, and Cloud Engineering, Bass Pro Shops. “Elastic’s new metrics capabilities let us handle that volume and surface the insights we need. Coupled with Elastic’s OpenTelemetry support, we get visibility into an increasingly complex architecture — all while keeping performance up and costs down.”
“At Eurowings, the improved metrics performance, native Prometheus support, logsdb and incident-handling workflows in Elasticsearch have helped our teams achieve faster incident response times and a more unified view across signals without jumping between systems,” said Iosif Tournas, Cyber Security & Elastic Platform Lead, Eurowings Aviation. “These new metrics capabilities complement the millions of log events per minute and APM traces we’re already handling in Elastic Observability. This unified view reduces operational friction, breaks the silos between teams and the time it takes to detect and respond to issues.”
The columnar metrics engine (TSDS), ES|QL time series support, PromQL in Kibana, and Prometheus Remote Write ingest are generally available. Out-of-the-box Kubernetes infrastructure content including dashboards, alert templates, SLO and ML anomaly detection jobs are also generally available. The Observability MCP App, Agent Skills, and the Observability Migration Platform are available in tech preview. All capabilities run across Elastic Cloud, serverless, and self-managed deployments.
While Datadog does not offer an on-premises option and Grafana limits its highest-value features to hosted deployments, Elastic gives organizations the flexibility to run observability workloads where their data and operational requirements demand.
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