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Elastic Redefines Observability with AI-Powered Streams

Streams presents a unified intelligence layer for extracting structure from messy, unstructured logs, enabling rapid root cause identification and remediation

Elastic announced Streams, an agentic AI-powered solution that rethinks how teams work with logs to enable much faster incident investigation and resolution.

Streams uses AI to automatically partition and parse raw logs to extract relevant fields, greatly reducing the effort required of Site Reliability Engineers (SREs) to make logs usable. Streams also automatically surfaces significant events such as critical errors and anomalies from context-rich logs, giving SREs early warnings and a clear understanding of their workloads, enabling them to investigate and resolve issues faster.

SREs are often overwhelmed by dashboards and alerts that show what and where things are broken, but fail to reveal why. This industry-wide focus on visualizing symptoms forces engineers to manually hunt for answers. The crucial "why" is buried in logs, but their massive volume and unstructured nature have led the industry to toss them aside or treat them as lesser. This has forced teams into costly tradeoffs: either spend countless hours building complex data pipelines, drop valuable log data and risk critical visibility gaps, or log and forget.

Streams directly addresses this challenge by reimagining the entire log pipeline. It leverages the Elasticsearch platform to combine AI-driven parsing, which automatically adapts to new log formats. Instead of forcing SREs to comb through noise, Streams automatically surfaces significant events, such as out-of-memory errors, internal server failures, and critical startup or shutdown messages. These events act as actionable markers, providing a clear investigative focus and an early warning before a service impact occurs.

“For too long, SREs have been forced to treat logs as a noisy, expensive last resort for investigations. Teams hunt through dashboards for what is broken, while the actual why is buried,” said Ken Exner, chief product officer at Elastic. “Streams make logs your most valuable asset. It automatically finds the signal in the noise, surfacing critical events from any log source. This gives SREs time back, allowing them to move from symptom to solution in minutes.”

Streams can:

  • Log everything, effortlessly: Ingest any log format from any source directly, with AI-driven processing making data "ready for investigation."
  • Get answers, not just data: Streams surfaces "Significant Events" like critical errors and anomalies, providing prioritized starting points.
  • Achieve complete, cost-effective visibility: Intelligently manage and organize data to reduce operational complexity and lower total ownership costs.

Streams in Elasticsearch is available today in both serverless and version 9.2.

The Latest

AI observability serves several roles across an organization. AI engineers use it to build, evaluate, and improve AI systems. SREs use it to operate those systems reliably and efficiently in production, including managing their cost. Leaders need it to connect AI behavior and operational performance to business outcomes. The common need is a clear understanding of whether AI systems are delivering the intended result ...

Part 4 outlines the challenges enterprise face when adopting AI observability. The main challenge is that AI complexity compounds at every stage of maturity. LLM-based applications introduce new behavior, dependencies, and quality considerations, while Agentic AI adds further failure modes as systems make decisions and take actions ...

Part 3 reports on the current state of AI observability. Most experts agree: enterprises are moving faster with AI than they are with the operational discipline needed to run it safely ...

When discussing AI Observability, one of the first questions that arises: Is this just an extension of traditional observability or is this a whole new type of tool? Part 2 compares traditional observability with AI observability ...

In this extensive 14-part series that will post over the next three weeks, experts across observability and AI will provide insight and opinions on the need for AI Observability, the challenges, the benefits, the current state, and predictions for the future ...

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Most enterprise IT leaders can tell you exactly how many applications are running across their organization. They can surface a dashboard showing telecom spend by region, pull a report on SaaS license utilization, or call up a real-time view of mobile device inventory. The data is there, and IT leaders have largely delivered on the promise of visibility. Yet, in most organizations, that visibility has not translated into business value ...

Elastic Redefines Observability with AI-Powered Streams

Streams presents a unified intelligence layer for extracting structure from messy, unstructured logs, enabling rapid root cause identification and remediation

Elastic announced Streams, an agentic AI-powered solution that rethinks how teams work with logs to enable much faster incident investigation and resolution.

Streams uses AI to automatically partition and parse raw logs to extract relevant fields, greatly reducing the effort required of Site Reliability Engineers (SREs) to make logs usable. Streams also automatically surfaces significant events such as critical errors and anomalies from context-rich logs, giving SREs early warnings and a clear understanding of their workloads, enabling them to investigate and resolve issues faster.

SREs are often overwhelmed by dashboards and alerts that show what and where things are broken, but fail to reveal why. This industry-wide focus on visualizing symptoms forces engineers to manually hunt for answers. The crucial "why" is buried in logs, but their massive volume and unstructured nature have led the industry to toss them aside or treat them as lesser. This has forced teams into costly tradeoffs: either spend countless hours building complex data pipelines, drop valuable log data and risk critical visibility gaps, or log and forget.

Streams directly addresses this challenge by reimagining the entire log pipeline. It leverages the Elasticsearch platform to combine AI-driven parsing, which automatically adapts to new log formats. Instead of forcing SREs to comb through noise, Streams automatically surfaces significant events, such as out-of-memory errors, internal server failures, and critical startup or shutdown messages. These events act as actionable markers, providing a clear investigative focus and an early warning before a service impact occurs.

“For too long, SREs have been forced to treat logs as a noisy, expensive last resort for investigations. Teams hunt through dashboards for what is broken, while the actual why is buried,” said Ken Exner, chief product officer at Elastic. “Streams make logs your most valuable asset. It automatically finds the signal in the noise, surfacing critical events from any log source. This gives SREs time back, allowing them to move from symptom to solution in minutes.”

Streams can:

  • Log everything, effortlessly: Ingest any log format from any source directly, with AI-driven processing making data "ready for investigation."
  • Get answers, not just data: Streams surfaces "Significant Events" like critical errors and anomalies, providing prioritized starting points.
  • Achieve complete, cost-effective visibility: Intelligently manage and organize data to reduce operational complexity and lower total ownership costs.

Streams in Elasticsearch is available today in both serverless and version 9.2.

The Latest

AI observability serves several roles across an organization. AI engineers use it to build, evaluate, and improve AI systems. SREs use it to operate those systems reliably and efficiently in production, including managing their cost. Leaders need it to connect AI behavior and operational performance to business outcomes. The common need is a clear understanding of whether AI systems are delivering the intended result ...

Part 4 outlines the challenges enterprise face when adopting AI observability. The main challenge is that AI complexity compounds at every stage of maturity. LLM-based applications introduce new behavior, dependencies, and quality considerations, while Agentic AI adds further failure modes as systems make decisions and take actions ...

Part 3 reports on the current state of AI observability. Most experts agree: enterprises are moving faster with AI than they are with the operational discipline needed to run it safely ...

When discussing AI Observability, one of the first questions that arises: Is this just an extension of traditional observability or is this a whole new type of tool? Part 2 compares traditional observability with AI observability ...

In this extensive 14-part series that will post over the next three weeks, experts across observability and AI will provide insight and opinions on the need for AI Observability, the challenges, the benefits, the current state, and predictions for the future ...

Amid rising complexity, organizations are moving quickly toward agent-driven operations: AgenticOps, according to The Impact of Agentic AI on Network Operations, a new report from Cisco ...

AI, automation, and enterprise software give organizations an extraordinary amount of leverage. Work that once required hours can happen in minutes. Information buried across systems can be analyzed almost instantly. Complex workflows can be coordinated across teams with far less manual intervention. That leverage creates a temptation to start with the technology ...

Everyone wants to talk about models. Compute clusters. Benchmark scores that climb a fraction of a point and make headlines anyway. It's an easy conversation to have because it's measurable. But it's also increasingly beside the point ...

For more than a decade, the default infrastructure play has been simple. Move more workloads to the cloud, retire on-premises gear and treat the data center as a cost to shrink. That logic held while public cloud offered near-limitless capacity at predictable prices, and AI demand is now testing both halves of that assumption ...

Most enterprise IT leaders can tell you exactly how many applications are running across their organization. They can surface a dashboard showing telecom spend by region, pull a report on SaaS license utilization, or call up a real-time view of mobile device inventory. The data is there, and IT leaders have largely delivered on the promise of visibility. Yet, in most organizations, that visibility has not translated into business value ...