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Sentry Adds New Performance Monitoring Capabilities

Sentry announced a series of new capabilities to enhance its developer-centric Performance Monitoring, including Dynamic Sampling, Performance Issues, Real User Application Profiling, and Session Replay.

The announcement follows a commitment to address the growing gap for developers, who are using tooling that is not built for their workflow or their problems, despite increasing pressure to solve software issues quickly. With these new investments, Sentry is paving the way for a new approach to application monitoring that is intentionally developer-first, providing more control, transparency, and actionable data to monitor the overall health of applications.

New Performance Monitoring capabilities:

- More control with Dynamic Sampling: Dynamic Sampling puts control into the hands of developers to dial up or down the visibility into their application’s performance based on real-time business changes. This reduces noise, providing a more representative sample that allows teams to zero in on the events that matter most and more tightly control their monitoring costs.

- Quicker time to resolution with Performance Issues: Issues are no longer just bound to errors. Issues is evolving to support multiple kinds of issue types and this launch is the first step towards making that vision a reality. Performance Issues now surface the most critical performance problems in applications, and just like Error Issues, they capture and group unique problems together and provide actionable context so developers can solve them faster.

- More awareness through Real User Application Profiling: Profiling provides an additional layer of awareness by identifying the exact functions and lines that are consuming resources on a user’s device. This helps developers find and solve latency issues faster. Profiling is now available for Android and iOS, with plans to expand to backend languages and frameworks.

- Dive deeper with Session Replay: Session Replay bridges the gap between code and user experience by providing developers a visual replay to surface the cause of an error or latency issue. With the ability to rewind and replay every step of the user journey, before and after they encountered an issue, developers can get to the root cause, faster, decreasing time to resolution.

“We have evolved an old space with a much-needed revamp that expands the concept of monitoring an application to be immediately valuable for developers, by focusing on actionability and real outcomes, instead of just more charts,” said Milin Desai, CEO, Sentry. “In a world that is increasingly end-user driven, the developers building these web and mobile apps need the visibility and code-level control to protect the user experience.”

Dynamic Sampling, Performance Issues, and Real User Application Profiling is available in Open Beta.

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Sentry Adds New Performance Monitoring Capabilities

Sentry announced a series of new capabilities to enhance its developer-centric Performance Monitoring, including Dynamic Sampling, Performance Issues, Real User Application Profiling, and Session Replay.

The announcement follows a commitment to address the growing gap for developers, who are using tooling that is not built for their workflow or their problems, despite increasing pressure to solve software issues quickly. With these new investments, Sentry is paving the way for a new approach to application monitoring that is intentionally developer-first, providing more control, transparency, and actionable data to monitor the overall health of applications.

New Performance Monitoring capabilities:

- More control with Dynamic Sampling: Dynamic Sampling puts control into the hands of developers to dial up or down the visibility into their application’s performance based on real-time business changes. This reduces noise, providing a more representative sample that allows teams to zero in on the events that matter most and more tightly control their monitoring costs.

- Quicker time to resolution with Performance Issues: Issues are no longer just bound to errors. Issues is evolving to support multiple kinds of issue types and this launch is the first step towards making that vision a reality. Performance Issues now surface the most critical performance problems in applications, and just like Error Issues, they capture and group unique problems together and provide actionable context so developers can solve them faster.

- More awareness through Real User Application Profiling: Profiling provides an additional layer of awareness by identifying the exact functions and lines that are consuming resources on a user’s device. This helps developers find and solve latency issues faster. Profiling is now available for Android and iOS, with plans to expand to backend languages and frameworks.

- Dive deeper with Session Replay: Session Replay bridges the gap between code and user experience by providing developers a visual replay to surface the cause of an error or latency issue. With the ability to rewind and replay every step of the user journey, before and after they encountered an issue, developers can get to the root cause, faster, decreasing time to resolution.

“We have evolved an old space with a much-needed revamp that expands the concept of monitoring an application to be immediately valuable for developers, by focusing on actionability and real outcomes, instead of just more charts,” said Milin Desai, CEO, Sentry. “In a world that is increasingly end-user driven, the developers building these web and mobile apps need the visibility and code-level control to protect the user experience.”

Dynamic Sampling, Performance Issues, and Real User Application Profiling is available in Open Beta.

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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 ...