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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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While 87% of manufacturing leaders and technical specialists report that ROI from their AIOps initiatives has met or exceeded expectations, only 37% say they are fully prepared to operationalize AI at scale, according to The Future of IT Operations in the AI Era, a report from Riverbed ...

Many organizations rely on cloud-first architectures to aggregate, analyze, and act on their operational data ... However, not all environments are conducive to cloud-first architectures ... There are limitations to cloud-first architectures that render them ineffective in mission-critical situations where responsiveness, cost control, and data sovereignty are non-negotiable; these limitations include ...

For years, cybersecurity was built around a simple assumption: protect the physical network and trust everything inside it. That model made sense when employees worked in offices, applications lived in data centers, and devices rarely left the building. Today's reality is fluid: people work from everywhere, applications run across multiple clouds, and AI-driven agents are beginning to act on behalf of users. But while the old perimeter dissolved, a new one quietly emerged ...

For years, infrastructure teams have treated compute as a relatively stable input. Capacity was provisioned, costs were forecasted, and performance expectations were set based on the assumption that identical resources behaved identically. That mental model is starting to break down. AI infrastructure is no longer behaving like static cloud capacity. It is increasingly behaving like a market ...

Resilience can no longer be defined by how quickly an organization recovers from an incident or disruption. The effectiveness of any resilience strategy is dependent on its ability to anticipate change, operate under continuous stress, and adapt confidently amid uncertainty ...

Mobile users are less tolerant of app instability than ever before. According to a new report from Luciq, No Margin for Error: What Mobile Users Expect and What Mobile Leaders Must Deliver in 2026, even minor performance issues now result in immediate abandonment, lost purchases, and long-term brand impact ...

Artificial intelligence (AI) has become the dominant force shaping enterprise data strategies. Boards expect progress. Executives expect returns. And data leaders are under pressure to prove that their organizations are "AI-ready" ...

Agentic AI is a major buzzword for 2026. Many tech companies are making bold promises about this technology, but many aren't grounded in reality, at least not yet. This coming year will likely be shaped by reality checks for IT teams, and progress will only come from a focus on strong foundations and disciplined execution ...

AI systems are still prone to hallucinations and misjudgments ... To build the trust needed for adoption, AI must be paired with human-in-the-loop (HITL) oversight, or checkpoints where humans verify, guide, and decide what actions are taken. The balance between autonomy and accountability is what will allow AI to deliver on its promise without sacrificing human trust ...

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