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Elastic Announces Beta of New Universal Profiling and Added Synthetic Monitoring Capabilities

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.”

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Elastic Announces Beta of New Universal Profiling and Added Synthetic Monitoring Capabilities

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.”

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In the world of digital-first business, there is no tolerance for service outages. Businesses know that outages are the quickest way to lose money and customers. For smaller organizations, unplanned downtime could even force the business to close ... A new study from PagerDuty, The State of AI-First Operations, reveals that companies actively incorporating AI into operations now view operational resilience as a growth driver rather than a cost center. But how are they achieving it? ...

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

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 ...