
Elastic announced new features and enhancements across its Elastic Observability solution, enabling customers to gain deeper and more frictionless visibility at all levels of applications, services, and infrastructure.
Innovations across the Elastic Observability solution include:
Providing effortless, deep visibility for cloud-native production environments with zero instrumentation and low overhead, with always-on Universal Profiling
Elastic’s new Universal Profiling capability, now in private beta, provides visibility into how application code and infrastructure are performing at all times in production, across a wide range of languages, in both containerized and non-containerized environments.
Modern cloud-native environments are increasingly complex, creating infrastructure and application blind spots for DevOps and SRE teams. Engineering teams typically use profiling to spot performance bottlenecks and troubleshoot issues faster. However, most profiling solutions have significant drawbacks limiting adoption in production environments:
- Significant cost and performance overhead due to code instrumentation
- Disruptive service restarts
- Inability to get visibility into third-party libraries
Universal Profiling is lightweight and requires zero instrumentation. Enabled by eBPF-based technology, it overcomes the limitations of other profiling solutions by requiring no changes to the application code, making it easier to quickly identify performance bottlenecks, improve time to resolve problems, and reduce cloud costs.
The low overhead of Universal Profiling, less than 1% CPU overhead, makes it possible to deploy in production environments to deliver deep and broad visibility into infrastructure and cloud-native application performance at scale.
For a production application running across a few hundred servers, early results show code optimization savings of 10% to 20% of CPU resources, resulting in cost savings and a reduction of CO2 emissions per year.
Introducing new capabilities to cloud- and developer-first synthetic monitoring
Synthetic monitoring enables teams to proactively simulate user interactions in applications to quickly detect user-facing availability and performance issues and optimize the end-user experience.
Designed to reduce manual and repetitive tasks for development and operations teams, Elastic is introducing the beta of the following innovative synthetic monitoring capabilities available within the current Uptime application for Elastic Cloud customers:
- A cloud-based global testing infrastructure that enables the ability to schedule tests from an expanding global network of synthetic monitors for better visibility into regional variances in user experience.
- Automated creation of synthetic monitors during functional testing when code is released to production. Creating, editing, and deleting synthetic monitors entirely in code reduces the inefficiency of duplicating functional tests.
- Deploying monitoring scripts via CI/CD pipelines to ensure tests and applications are aligned.
- Running synthetics agent locally, making it easier to create and debug monitoring scripts.
- A point-and-click script recorder, enabling non-technical users to quickly create a user journey through an application and turn that into a synthetic monitor. The recorder speeds up the process of creating monitoring scripts for developers by providing a framework that can be edited locally.
Additionally, a new and intuitive user interface to simplify workflows and make it easier to identify and quickly troubleshoot problems in production is currently under development and planned for future availability.
“The capabilities announced today provide deep, frictionless observability into application and infrastructure performance that enable customers to gain even greater value from their data,” said Sajai Krishnan, GM, Observability, Elastic. “Elastic’s continued focus on innovation extends to Universal Profiling, which helps customers understand their application’s CPU consumption hotspots, provides opportunities to optimize applications for real savings in production, plus a reduced carbon footprint.”
The Latest
I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...
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 ...
