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Elastic Adds Native Prometheus and PromQL Support to Elastic Observability

Unify Prometheus metrics with logs and traces, without rewriting queries or rebuilding pipelines

Elastic announced native Prometheus support, including direct ingestion via Remote Write and full PromQL support in Kibana.

These additions enable Site Reliability Engineers (SREs) to analyze Prometheus metrics alongside logs and traces in a single platform, without rewriting queries or rebuilding pipelines.

As organizations scale Kubernetes, Prometheus telemetry cardinality and volumes surge, forcing SREs to juggle multiple tools, duplicate data pipelines, and rewrite queries across systems. This fragmentation slows incident response and drives up operational costs.

With native Prometheus support, Elastic eliminates these fragmentation trade-offs by allowing teams to ingest, store, and analyze native Prometheus data alongside other telemetry data, while preserving existing Prometheus workflows. Instead of stitching together tools, SREs can detect, investigate, and resolve incidents end-to-end across AI and cloud-native environments faster and with less operational overhead.

“Modern incident response is slowed down by tool sprawl and disconnected data, and SREs shouldn’t have to pivot between tools or rewrite queries just to understand what’s happening in production,” said Bahaaldine Azarmi, general manager, Observability at Elastic. “With native Prometheus ingestion and PromQL in Kibana, teams get a single platform that dramatically reduces time to root cause.”

Native Prometheus Ingestion—No Translation Required (tech preview)

Elastic now ingests Prometheus metrics directly via Remote Write, eliminating the need for adapters, schema, or format translations.

SREs can stream Prometheus metrics straight into Elasticsearch while maintaining their original structure and semantics. The result is a single source of truth for observability, without forcing teams to abandon Prometheus. This approach:

  • Removes duplicate storage and pipeline complexity
  • Preserves full metric fidelity and high-cardinality data
  • Enables unified analysis across metrics, logs, and traces

Run PromQL Directly in Kibana (tech preview)

With native PromQL support in Kibana, users can run existing PromQL queries in dashboards and alerts without modification, lowering the barrier to adoption for teams already using Prometheus.

This eliminates query rewrites, one of the biggest adoption barriers in observability platforms. SREs can keep the PromQL they’ve already built, including dashboards, alerts, and workflows, alongside logs and traces in the same environment, while gaining a path from alert to root cause without manual pivoting, enabling deeper, cross-signal analysis during incidents.

Availability

Native Prometheus ingestion and PromQL support in Kibana are available in technical preview.
 

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Elastic Adds Native Prometheus and PromQL Support to Elastic Observability

Unify Prometheus metrics with logs and traces, without rewriting queries or rebuilding pipelines

Elastic announced native Prometheus support, including direct ingestion via Remote Write and full PromQL support in Kibana.

These additions enable Site Reliability Engineers (SREs) to analyze Prometheus metrics alongside logs and traces in a single platform, without rewriting queries or rebuilding pipelines.

As organizations scale Kubernetes, Prometheus telemetry cardinality and volumes surge, forcing SREs to juggle multiple tools, duplicate data pipelines, and rewrite queries across systems. This fragmentation slows incident response and drives up operational costs.

With native Prometheus support, Elastic eliminates these fragmentation trade-offs by allowing teams to ingest, store, and analyze native Prometheus data alongside other telemetry data, while preserving existing Prometheus workflows. Instead of stitching together tools, SREs can detect, investigate, and resolve incidents end-to-end across AI and cloud-native environments faster and with less operational overhead.

“Modern incident response is slowed down by tool sprawl and disconnected data, and SREs shouldn’t have to pivot between tools or rewrite queries just to understand what’s happening in production,” said Bahaaldine Azarmi, general manager, Observability at Elastic. “With native Prometheus ingestion and PromQL in Kibana, teams get a single platform that dramatically reduces time to root cause.”

Native Prometheus Ingestion—No Translation Required (tech preview)

Elastic now ingests Prometheus metrics directly via Remote Write, eliminating the need for adapters, schema, or format translations.

SREs can stream Prometheus metrics straight into Elasticsearch while maintaining their original structure and semantics. The result is a single source of truth for observability, without forcing teams to abandon Prometheus. This approach:

  • Removes duplicate storage and pipeline complexity
  • Preserves full metric fidelity and high-cardinality data
  • Enables unified analysis across metrics, logs, and traces

Run PromQL Directly in Kibana (tech preview)

With native PromQL support in Kibana, users can run existing PromQL queries in dashboards and alerts without modification, lowering the barrier to adoption for teams already using Prometheus.

This eliminates query rewrites, one of the biggest adoption barriers in observability platforms. SREs can keep the PromQL they’ve already built, including dashboards, alerts, and workflows, alongside logs and traces in the same environment, while gaining a path from alert to root cause without manual pivoting, enabling deeper, cross-signal analysis during incidents.

Availability

Native Prometheus ingestion and PromQL support in Kibana are available in technical preview.
 

The Latest

As AI adoption accelerates, operational complexity — not model intelligence — is becoming the primary barrier to reliable AI at scale, according to the State of AI Engineering 2026 from Datadog ... The report highlights a compounding complexity challenge as AI systems scale ... Around 5% of AI model requests fail in production, with nearly 60% of those failures caused by capacity limits ...

For years, production operations teams have treated alert fatigue as a quality-of-life problem: something that makes on-call rotations miserable but isn't considered a direct contributor to outages. That framing doesn't capture how these systems fail, and we now have data to show why. More importantly, it's now clear alert fatigue is a symptom of a deeper issue: production systems have outgrown the current operational approaches ...

I was on a customer call last fall when an enterprise architect said something I haven't been able to shake. Her team had just spent four months trying to swap one AI vendor for another. The original plan said three weeks. "We didn't switch vendors," she told me. "We rebuilt half our integrations and discovered what we'd actually been depending on." Most enterprise leaders don't expect that to be the experience ...

Ask any senior SRE or platform engineer what keeps them up at night, and the answer probably isn't the monitoring tool — it's the data feeding it. The proliferation of APM, observability, and AIOps platforms has created a telemetry sprawl problem that most teams manage reactively rather than architect proactively. Metrics are going to one platform. Traces routed somewhere else. Logs duplicated across multiple backends because nobody wants to be caught without them when something breaks. Every redundant stream costs money ...

80% of respondents agree that the IT role is shifting from operators to orchestrators, according to the 2026 IT Trends Report: The Human Side of Autonomous IT from SolarWinds ...

40% of organizations deploying AI will implement dedicated AI observability tools by 2028 to monitor model performance, bias and outputs, according to Gartner ...

Until AI-powered engineering tools have live visibility of how code behaves at runtime, they cannot be trusted to autonomously ensure reliable systems, according to the State of AI-Powered Engineering Report 2026 report from Lightrun. The report reveals that a major volume of manual work is required when AI-generated code is deployed: 43% of AI-generated code requires manual debugging in production, even after passing QA or staging tests. Furthermore, an average of three manual redeploy cycles are required to verify a single AI-suggested code fix in production ...

Many organizations describe AI as strategic, but they do not manage it strategically. When AI plans are disconnected from strategy, detached from organizational learning, and protected from serious assumptions testing, the problem is no longer technical immaturity; it is a failure of management discipline ... Executives too often tell organizations to "use AI" before they define what AI is supposed to change. The problem deepens in organizations where strategy isn't well articulated in the first place ...

Across the enterprise technology landscape, a quiet crisis is playing out. Organizations have run hundreds, sometimes thousands, of generative AI pilots. Leadership has celebrated the proof of concept (POCs) ... Industry experience points to a sobering reality: only 5-10% of AI POCs that progress to the pilot stage successfully reach scaled production. The remaining 90% fail because the enterprise environment around them was never ready to absorb them, not the AI models ...

Today's modern systems are not what they once were. Organizations now rely on distributed systems, event-driven workflows, hybrid and multi-cloud environments and continuous delivery pipelines. While each adds flexibility, it also introduces new, often invisible failures. Development speed is no longer the primary bottleneck of innovation. Reliability is ...