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
Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...
For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...
Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...
Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...
For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...
New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...
Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...
In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ...
In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...
When most people think about cybersecurity, they picture firewalls, encryption, and access controls — technical tools designed to protect systems and data. But beneath the technology lies a deeper set of principles about trust, decision-making, and resilience ... The best leaders don't eliminate risk. They manage it intelligently. And in many ways, cybersecurity offers a surprisingly useful playbook for doing exactly that ...