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Sentry Releases Application Metrics

Sentry announced the general availability of Application Metrics. 

With this launch, Sentry’s observability suite is now comprehensive, giving engineering teams errors, traces, logs, and metrics in one place. Application Metrics is high-cardinality and trace-connected, built for application debugging.

With Sentry Application Metrics, a developer instruments checkout latency once and attaches whatever context is relevant — region, plan type, browser, product category, user ID. From that point forward, any combination of those dimensions can be queried on demand, with no predefined aggregations and no re-instrumentation required. When a new question emerges mid-incident, the data is already there. This is made possible by Sentry’s decision to store metric events in full rather than pre-aggregating them.

Every metric event retains a direct connection to the trace it was emitted from, meaning a spike in any chart is one click away from exemplar traces, logs, and errors connected by trace ID. With Sentry Metrics, there is no pivoting between tools, no manual correlation by timestamp, and no guesswork.

“Developers don’t just want to know that something broke, they want to know why. For years, the tools haven’t matched that desire. Developers would see a spike in a chart and hit a wall when investigating because that spike didn’t provide the right context to understand what happened. They would spend unnecessary time correlating data from different tools in an effort to fill in the missing context. Application Metrics is Sentry’s answer to that wall. We want every developer to have the ability to go from ‘something is wrong’ to ‘here is exactly what happened and why.’ One tool, full context, no wasted time,” said Alex Jillard, Senior Engineering Manager at Sentry.

Getting started requires no additional infrastructure. If you’re already using Sentry, you can add metrics with your existing Sentry SDK. Instrument the signals you care about and they appear alongside your existing errors, traces, and logs.

Sentry Application Metrics is available now.

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Sentry Releases Application Metrics

Sentry announced the general availability of Application Metrics. 

With this launch, Sentry’s observability suite is now comprehensive, giving engineering teams errors, traces, logs, and metrics in one place. Application Metrics is high-cardinality and trace-connected, built for application debugging.

With Sentry Application Metrics, a developer instruments checkout latency once and attaches whatever context is relevant — region, plan type, browser, product category, user ID. From that point forward, any combination of those dimensions can be queried on demand, with no predefined aggregations and no re-instrumentation required. When a new question emerges mid-incident, the data is already there. This is made possible by Sentry’s decision to store metric events in full rather than pre-aggregating them.

Every metric event retains a direct connection to the trace it was emitted from, meaning a spike in any chart is one click away from exemplar traces, logs, and errors connected by trace ID. With Sentry Metrics, there is no pivoting between tools, no manual correlation by timestamp, and no guesswork.

“Developers don’t just want to know that something broke, they want to know why. For years, the tools haven’t matched that desire. Developers would see a spike in a chart and hit a wall when investigating because that spike didn’t provide the right context to understand what happened. They would spend unnecessary time correlating data from different tools in an effort to fill in the missing context. Application Metrics is Sentry’s answer to that wall. We want every developer to have the ability to go from ‘something is wrong’ to ‘here is exactly what happened and why.’ One tool, full context, no wasted time,” said Alex Jillard, Senior Engineering Manager at Sentry.

Getting started requires no additional infrastructure. If you’re already using Sentry, you can add metrics with your existing Sentry SDK. Instrument the signals you care about and they appear alongside your existing errors, traces, and logs.

Sentry Application Metrics is available now.

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Across the enterprise technology landscape, a quiet crisis is playing out. Organizations have run hundreds, sometimes thousands, of generative AI pilots. Leadership has celebrated the proof of concept (POCs) ... Industry experience points to a sobering reality: only 5-10% of AI POCs that progress to the pilot stage successfully reach scaled production. The remaining 90% fail because the enterprise environment around them was never ready to absorb them, not the AI models ...

Today's modern systems are not what they once were. Organizations now rely on distributed systems, event-driven workflows, hybrid and multi-cloud environments and continuous delivery pipelines. While each adds flexibility, it also introduces new, often invisible failures. Development speed is no longer the primary bottleneck of innovation. Reliability is ...

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Technology leaders across the federal landscape are facing, and will continue to face, an uphill battle when it comes to fortifying their digital environments against hostile and persistent threat actors. On one hand, they are being asked to push digital transformation ... On the other hand, they are facing the fiscal uncertainty of continuing resolutions (CR) and government shutdowns looming near and far. In the face of these challenges, CIOs, CTOs, and CISOs must figure out how to modernize legacy systems and infrastructure while doing more with less and still defending against external and internal threats ...

Reliability is no longer proven by uptime alone, according to the The SRE Report 2026 from LogicMonitor. In the AI era, it is experienced through speed, consistency, and user trust, and increasingly judged by business impact. As digital services grow more complex and AI systems move into production, traditional monitoring approaches are struggling to keep pace, increasing the need for AI-first observability that spans applications, infrastructure, and the Internet ...

If AI is the engine of a modern organization, then data engineering is the road system beneath it. You can build the most powerful engine in the world, but without paved roads, traffic signals, and bridges that can support its weight, it will stall. In many enterprises, the engine is ready. The roads are not ...

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 ...

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