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Sentry Provides Performance Monitoring for Python and JavaScript

Sentry provides agentless frontend performance monitoring for Python and JavaScript.

With a focus on code and developers, Sentry enables engineering and development teams to more quickly identify performance issues by tracing them to poor-performing API calls along with related errors—all with just five lines of code.

With Performance Monitoring by Sentry, engineering managers and developers now have a solution to resolve performance bottlenecks and deliver fast, reliable, and personalized customer experiences that drive business value. Within minutes, they can trace issues back to a poor-performing API call and surface trends to help them proactively prevent future performance issues, saving time and dramatically reducing costs.

“As more organizations go digital, it is important to know how your code is doing in production and not just if your systems are operational. Developers need a more direct line to the customer experience and related issues,” said Milin Desai, CEO, Sentry. “Sentry is the only platform that enables software teams to easily trace issues related to errors in code, identify performance problems, and surface trends in code quality, all while integrating seamlessly into your development tool stack. This reduces time to resolution from days to minutes, frees up developer cycles, and ensures satisfied, returning customers.”

Sentry Performance arms engineering and development teams with:

- Application Health Insights: Quickly understand customer satisfaction based on your application’s response time to their interactions with live updating latency and throughput data. Compare slow response times, increases in transactions, and error rates to scientifically diagnose and fix all performance issues.

- Transaction Summary: View transactions sorted by slowest duration time, related issues, and the number of users having a slow experience in one consolidated view. Enable release markers for a second layer of context to gauge how your users react to new code pushed to production. Also, track business-critical parts of your application with Key Transactions.

- Root Cause Analysis: Easily identify and understand differences in characteristics between outliers and normal performing transactions with superior drill-down capabilities and user-friendly visualizations.

- Tracing: Leverage end-to-end distributed tracing to reveal the exact DB query that caused an error or performance issue.

- Performance Alerts: See how crashes contribute to performance and set thresholds to get alerted if performance metrics fall past a predefined tolerance band. Drill down into transaction details within tracing waterfalls, which visually highlight API call times in relation to expected operations and device data, to quickly identify which API calls are giving customers poor experiences.

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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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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 ...

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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 ...

Sentry Provides Performance Monitoring for Python and JavaScript

Sentry provides agentless frontend performance monitoring for Python and JavaScript.

With a focus on code and developers, Sentry enables engineering and development teams to more quickly identify performance issues by tracing them to poor-performing API calls along with related errors—all with just five lines of code.

With Performance Monitoring by Sentry, engineering managers and developers now have a solution to resolve performance bottlenecks and deliver fast, reliable, and personalized customer experiences that drive business value. Within minutes, they can trace issues back to a poor-performing API call and surface trends to help them proactively prevent future performance issues, saving time and dramatically reducing costs.

“As more organizations go digital, it is important to know how your code is doing in production and not just if your systems are operational. Developers need a more direct line to the customer experience and related issues,” said Milin Desai, CEO, Sentry. “Sentry is the only platform that enables software teams to easily trace issues related to errors in code, identify performance problems, and surface trends in code quality, all while integrating seamlessly into your development tool stack. This reduces time to resolution from days to minutes, frees up developer cycles, and ensures satisfied, returning customers.”

Sentry Performance arms engineering and development teams with:

- Application Health Insights: Quickly understand customer satisfaction based on your application’s response time to their interactions with live updating latency and throughput data. Compare slow response times, increases in transactions, and error rates to scientifically diagnose and fix all performance issues.

- Transaction Summary: View transactions sorted by slowest duration time, related issues, and the number of users having a slow experience in one consolidated view. Enable release markers for a second layer of context to gauge how your users react to new code pushed to production. Also, track business-critical parts of your application with Key Transactions.

- Root Cause Analysis: Easily identify and understand differences in characteristics between outliers and normal performing transactions with superior drill-down capabilities and user-friendly visualizations.

- Tracing: Leverage end-to-end distributed tracing to reveal the exact DB query that caused an error or performance issue.

- Performance Alerts: See how crashes contribute to performance and set thresholds to get alerted if performance metrics fall past a predefined tolerance band. Drill down into transaction details within tracing waterfalls, which visually highlight API call times in relation to expected operations and device data, to quickly identify which API calls are giving customers poor experiences.

The Latest

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 ...