Native PromQL, out-of-the-box Kubernetes agentic investigation, and automated migration from Datadog and Grafana, all on the platform many SREs already use for logs. Elasticsearch, the Search AI company (NYSE: ESTC), today announced a wave of new capabilities that bring the same scale, performance, and operational simplicity of Elastic’s trusted platform, already highly regarded for logs, to the realm of metrics data. With native support for Prometheus and PromQL, out-of-the-box Kubernetes investigation workflows, and automated migration from Datadog and Grafana to Elasticsearch, Elastic now delivers a unified platform for both metrics and logs. Built on Elasticsearch’s columnar metrics engine, the platform can query metrics up to 30x faster and store data up to 2.5x more efficiently than Prometheus, with no cardinality limits or penalties for custom metrics. The metrics landscape has changed dramatically. Kubernetes and microservices have already expanded the time-series data targeted by observability systems from thousands to millions of data points. AI workloads are now accelerating this expansion, making metrics not just a scalability problem, but a strategic cost and reliability problem. For most platforms, this expansion comes with a significant cost. Premium vendors increase costs with higher cardinality, while lower-cost alternatives force metrics and logs into separate backends and query languages. This often leads to reduced data collection to control costs, leaving engineers with less context to understand what happened when an incident occurs. Elastic Observability addresses both problems on a single platform, storing OpenTelemetry, Prometheus native, and application-defined metrics alongside logs and traces at full resolution. No separate backends are needed, and there are no trade-offs in data retention. This release targets the metrics engine that powers this platform and the various capabilities that run on top of it. Native PromQL and Prometheus Rem