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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.

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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.

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The gap is widening between what teams spend on observability tools and the value they receive amid surging data volumes and budget pressures, according to The Breaking Point for Observability Leaders, a report from Imply ...

Seamless shopping is a basic demand of today's boundaryless consumer — one with little patience for friction, limited tolerance for disconnected experiences and minimal hesitation in switching brands. Customers expect intuitive, highly personalized experiences and the ability to move effortlessly across physical and digital channels within the same journey. Failure to deliver can cost dearly ...

If your best engineers spend their days sorting tickets and resetting access, you are wasting talent. New global data shows that employees in the IT sector rank among the least motivated across industries. They're under a lot of pressure from many angles. Pressure to upskill and uncertainty around what agentic AI means for job security is creating anxiety. Meanwhile, these roles often function like an on-call job and require many repetitive tasks ...

In a 2026 survey conducted by Liquibase, the research found that 96.5% of organizations reported at least one AI or LLM interaction with their production databases, often through analytics and reporting, training pipelines, internal copilots, and AI generated SQL. Only a small fraction reported no interaction at all. That means the database is no longer a downstream system that AI "might" reach later. AI is already there ...

In many organizations, IT still operates as a reactive service provider. Systems are managed through fragmented tools, teams focus heavily on operational metrics, and business leaders often see IT as a necessary cost center rather than a strategic partner. Even well-run ITIL environments can struggle to bridge the gap between operational excellence and business impact. This is where the concept of ITIL+ comes in ...

UK IT leaders are reaching a critical inflection point in how they manage observability, according to research from LogicMonitor. As infrastructure complexity grows and AI adoption accelerates, fragmented monitoring environments are driving organizations to rethink their operational strategies and consolidate tools ...

For years, many infrastructure teams treated the edge as a deployment variation. It was seen as the same cloud model, only stretched outward: more devices, more gateways, more locations and a little more latency. That assumption is proving costly. The edge is not just another place to run workloads. It is a fundamentally different operating condition ...

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Over the past few years, organizations have made enormous strides in enabling remote and hybrid work. But the foundational technologies powering today's digital workplace were never designed for the volume, velocity, and complexity that is coming next. By 2026 and beyond, three forces — 5G, the metaverse, and edge AI — will fundamentally reshape how people connect, collaborate, and access enterprise resources ... The businesses that begin preparing now will gain a competitive head start. Those that wait will find themselves trying to secure environments that have already outgrown their architecture ...

Ask where enterprise AI is making its most decisive impact, and the answer might surprise you: not marketing, not finance, not customer experience. It's IT. Across three years of industry research conducted by Digitate, one constant holds true is that IT is both the testing ground and the proving ground for enterprise AI. Last year, that position only strengthened ...