Elastic Unveils AI-Era Metrics Platform, Claims Major Speed and Storage Gains

Key Points

• Elastic launched a purpose-built metrics platform within Elasticsearch, combining a columnar metrics store with existing log, trace and vector data capabilities to support AI-driven telemetry growth and high-cardinality workloads.
• Elastic claims its metrics solution is 30 times faster than Prometheus and Mimir and eight times faster than ClickHouse, alongside substantial storage-efficiency gains; the benchmarks were company-provided and not independently verified.
• The company is targeting existing Elastic logging customers and Prometheus users, offering PromQL compatibility and migration tools, but expects adoption to build gradually because observability deals typically involve longer sales cycles.

Elastic NYSE: ESTC outlined its new metrics offering during an observability-focused investor call, positioning the product as a faster and more storage-efficient way for customers to manage metrics alongside logs, traces and other data types on a unified platform.

Baha Azarmi, Elastic’s General Manager of Observability, said the offering launched near the start of the company’s fiscal year in June after the company re-architected Elasticsearch for metrics workloads. The product is designed to address rising data volumes associated with artificial intelligence deployments, including monitoring of large language model calls, graphics-processing-unit activity, retrieval-augmented-generation queries and AI agents.

“AI is really causing an explosion in metrics,” Azarmi said, describing how agent-based workloads can create less predictable telemetry volumes than conventional applications. He said organizations may face blind spots if cost pressures force them to limit the metrics they retain.

Columnar Store for Metrics

Elastic said it built a column store within Elasticsearch specifically for metrics, which typically consist of numerical values, timestamps and multiple dimensional labels. Azarmi said the approach is intended to accommodate high-cardinality data, meaning data with a large number of unique dimensional combinations, without restricting customers’ use of dimensions.

The company said it uses techniques including dimension filtering and storage-codec tuning to improve query and storage efficiency. Azarmi said Elastic has published benchmarks and open-source code on GitHub that compare the offering with other products. According to the company’s benchmarks, Elastic’s metrics solution is 30 times faster than Prometheus and Mimir and eight times faster than ClickHouse. Those performance claims were presented by Elastic and were not independently verified on the call.

Azarmi said the platform combines a document store for logs and traces, a column store for metrics, and a vector store for vectorized data. The integrated approach is intended to allow users and AI agents to investigate issues across metrics, traces, logs and knowledge bases from within the same platform.

He also said Elastic recently acquired Deductive AI, a company focused on root-cause-analysis investigations with agents. Elastic plans to share additional observability-related announcements at its ElasticON event in New York on Oct. 8.

Compatibility and Migration Tools

Elastic emphasized compatibility with PromQL, the query language widely used with Prometheus-based metrics systems. Azarmi said users can bring PromQL queries into Kibana, and that Elastic has reached 90% compatibility with popular PromQL dashboards while working toward full compatibility.

The company also highlighted migration tools intended to help customers move from existing metrics platforms, along with professional-services support. Its out-of-the-box experiences include dashboards, visualizations, alerts, service-level objectives, machine-learning jobs and workflows, beginning with Kubernetes and AWS. Elastic also cited technology-specific integrations for Temporal, Supabase and Vercel, including managed endpoints for ingesting metrics.

Users can access metrics through Elastic’s user interface, natural-language queries via its AI agent, or through tools including model context protocol servers, tools, skills and applications, Azarmi said.

Go-to-Market Focus

Elastic identified three primary sales opportunities for the offering:

• Adding metrics capabilities for existing log analytics customers that want logs, metrics and traces consolidated in one platform.
• Targeting Prometheus and PromQL users seeking performance, storage efficiency and support for high-cardinality workloads.
• Competing for customers that face pricing pressure or retention limitations with existing metrics products.

Santosh Krishnan, Elastic’s senior vice president of Security and Observability Solutions, said the product had been in research and development for approximately 12 to 18 months and was developed for about 18 months before its June launch. He characterized the metrics product as an extension of Elastic’s platform advantage in handling unstructured log data into a purpose-built offering for metrics.

Krishnan said the near-term opportunity is primarily among site reliability engineering teams already using Elastic for centralized logging. He noted that many such customers use separate tools for infrastructure monitoring and said those tools may represent spending that exceeds their log-analytics expenditures.

However, he said adoption is likely to be gradual. “These are longer sales cycles,” Krishnan said, adding that the company expects a “ramp style” of adoption rather than an immediate “hockey stick.” Elastic said early design-partner and customer engagement has been positive.

Customer Perspective

Norion Bank, an Elastic customer for roughly eight years, said it has expanded from centralized logging into metrics, application performance monitoring, distributed tracing and OpenTelemetry. Per Lindblad, an observability platform engineer at Norion Bank, said the unified platform enables engineers to move between logs, metrics and traces during investigations without switching among tools.

Johan Andersson, also an observability platform engineer at Norion Bank, said the bank tested Elastic’s time-series data-stream features using a collector for Azure DevOps pull queues. He said storage savings after one month were “substantial.” Andersson added that the bank uses metrics as a continuous baseline for identifying conditions such as heap usage, disk watermarks and ingest lag before they affect users.

Krishnan said incoming metrics data volume could be in the same order of magnitude as logs, though logs are generally retained for longer periods. He said the new offering is focused primarily on observability and SRE workloads, while potential security use cases remain incidental and are still being monitored by the company.

About Elastic (NYSE:ESTC)

Elastic N.V. develops search, observability and security software for organizations that need to find, analyze and act on data. Its core technology, Elasticsearch, is a distributed search and analytics engine that can process structured and unstructured information from applications, websites, logs and other sources.

The company offers its platform through Elastic Cloud, a managed cloud service available across major public-cloud environments, as well as through self-managed deployments.

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