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Elastic Announces AI Assistant for Observability and General Availability of Universal Profiling

Elastic announced the launch of Elastic AI Assistant for Observability and general availability of Universal Profiling™, providing site reliability engineers (SREs), at all levels of expertise, with context-aware, relevant, and actionable operational insights that are specific to their IT environment.

Today's IT operations teams face an ever-evolving roster of systems challenges and issues unique to their IT environment and are under pressure to address them urgently.

While Artificial Intelligence for IT Operations (AIOps) has helped to translate, streamline, and automate problem resolution, Elastic AI Assistant for Observability, powered by the advances of generative AI, makes it faster and easier. The AI Assistant does this by leveraging generative AI and proprietary data to deliver context-aware and more accurate remediation for SREs which reduces the learning curve and eliminates the need for manual data chasing across silos.

Powered by the Elasticsearch Relevance Engine™ (ESRE™), the Elastic AI Assistant accelerates time to resolution by democratizing understanding of application errors, log message interpretation, alert analysis, and suggestions for optimal code efficiency.

Additionally, Elastic's AI Assistant interface improves speed and collaboration across teams by allowing users to interactively chat and visualize all relevant telemetry cohesively in one place while also leveraging proprietary data and runbooks for remediation.

"With the Elastic AI Assistant, SREs can quickly and easily turn what might look like machine gibberish into understandable problems that have actionable steps to resolution," said Ken Exner, chief product officer, Elastic. "Since the Elastic AI Assistant uses the Elasticsearch Relevance Engine on the user's unique IT environment and proprietary data sets, the responses it generates are relevant and provide richer and more contextualized insight, helping to elevate the expertise of the entire SRE team as they look to drive problem resolution faster in IT environments that will only grow more complex over time."

"The impact and value of generative AI dramatically increase when it has access to an enterprise's proprietary data," said Torsten Volk, analyst at Enterprise Management Associates. "It's exciting to see how Elastic's AI Assistant for Observability may help customers achieve a state where generative AI provides role and situation-specific recommendations, problem resolutions, and suggested efficiency enhancements, all based on the customer's own data sources. At the same time, helping keep that information private from the generic AI model that lives in the public cloud."

Elastic Announces General Availability of Universal Profiling

Complex cloud-native environments often create blind spots for SRE teams since many components cannot be instrumented. Instrumentation overhead and deployment complexity of traditional monitoring systems are also limiting factors for modern application teams. To address these challenges, Elastic has launched Universal Profiling, with always-on zero instrumentation and low overhead, to pinpoint performance bottlenecks with visibility into third-party libraries, allowing expedited issue resolution while enabling organizations to reduce cloud costs and track and lower the carbon footprint of their infrastructure.

"Elastic Universal Profiling has been a game-changer in optimizing our operations, ensuring that AppOmni consistently delivers exceptional experiences and cost efficiency," said Drew Gatchell, Director, Detection Engineering at AppOmni. "With its end-to-end visibility and data-driven insights, we can proactively identify and tackle performance bottlenecks to mitigate potential issues, enabling our teams to uphold peak performance and security for our customers."

Read the blog for more information about Elastic AI Assistant for Observability and how Universal Profiling provides visibility into how application code and infrastructure are performing at all times.

The Latest

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...

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 ...

Elastic Announces AI Assistant for Observability and General Availability of Universal Profiling

Elastic announced the launch of Elastic AI Assistant for Observability and general availability of Universal Profiling™, providing site reliability engineers (SREs), at all levels of expertise, with context-aware, relevant, and actionable operational insights that are specific to their IT environment.

Today's IT operations teams face an ever-evolving roster of systems challenges and issues unique to their IT environment and are under pressure to address them urgently.

While Artificial Intelligence for IT Operations (AIOps) has helped to translate, streamline, and automate problem resolution, Elastic AI Assistant for Observability, powered by the advances of generative AI, makes it faster and easier. The AI Assistant does this by leveraging generative AI and proprietary data to deliver context-aware and more accurate remediation for SREs which reduces the learning curve and eliminates the need for manual data chasing across silos.

Powered by the Elasticsearch Relevance Engine™ (ESRE™), the Elastic AI Assistant accelerates time to resolution by democratizing understanding of application errors, log message interpretation, alert analysis, and suggestions for optimal code efficiency.

Additionally, Elastic's AI Assistant interface improves speed and collaboration across teams by allowing users to interactively chat and visualize all relevant telemetry cohesively in one place while also leveraging proprietary data and runbooks for remediation.

"With the Elastic AI Assistant, SREs can quickly and easily turn what might look like machine gibberish into understandable problems that have actionable steps to resolution," said Ken Exner, chief product officer, Elastic. "Since the Elastic AI Assistant uses the Elasticsearch Relevance Engine on the user's unique IT environment and proprietary data sets, the responses it generates are relevant and provide richer and more contextualized insight, helping to elevate the expertise of the entire SRE team as they look to drive problem resolution faster in IT environments that will only grow more complex over time."

"The impact and value of generative AI dramatically increase when it has access to an enterprise's proprietary data," said Torsten Volk, analyst at Enterprise Management Associates. "It's exciting to see how Elastic's AI Assistant for Observability may help customers achieve a state where generative AI provides role and situation-specific recommendations, problem resolutions, and suggested efficiency enhancements, all based on the customer's own data sources. At the same time, helping keep that information private from the generic AI model that lives in the public cloud."

Elastic Announces General Availability of Universal Profiling

Complex cloud-native environments often create blind spots for SRE teams since many components cannot be instrumented. Instrumentation overhead and deployment complexity of traditional monitoring systems are also limiting factors for modern application teams. To address these challenges, Elastic has launched Universal Profiling, with always-on zero instrumentation and low overhead, to pinpoint performance bottlenecks with visibility into third-party libraries, allowing expedited issue resolution while enabling organizations to reduce cloud costs and track and lower the carbon footprint of their infrastructure.

"Elastic Universal Profiling has been a game-changer in optimizing our operations, ensuring that AppOmni consistently delivers exceptional experiences and cost efficiency," said Drew Gatchell, Director, Detection Engineering at AppOmni. "With its end-to-end visibility and data-driven insights, we can proactively identify and tackle performance bottlenecks to mitigate potential issues, enabling our teams to uphold peak performance and security for our customers."

Read the blog for more information about Elastic AI Assistant for Observability and how Universal Profiling provides visibility into how application code and infrastructure are performing at all times.

The Latest

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...

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 ...