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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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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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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 ...

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