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Coralogix Launches Advanced Continuous Profiling

Coralogix announced Continuous Profiling, an advanced capability that delivers real-time visibility into application performance without any code changes or production impact. 

It seamlessly integrates with Coralogix logs, metrics and traces to help engineering teams pinpoint and resolve bottlenecks in minutes.

Continuous profiling allows developers to connect overall CPU utilization with the underlying application, OS, and kernel processes that are impacting overall performance. By continuously collecting and storing profiling data, developer teams can visualize trends in processes, connect spikes or anomalies with code changes, and highlight troublesome PIDs before they escalate. However, traditional profiling solutions can significantly impact production performance by creating additional workload for the application, consuming extra CPU and memory, and persistently collecting large volumes of data.

Coralogix’s Continuous Profiling eliminates this problem by leveraging vendor-neutral eBPF (extended Berkeley Packet Filter) probes together with the OpenTelemetry standard to deliver kernel-level, always-on performance telemetry with less than 1% overhead. These lightweight in-kernel probes capture high-frequency stack traces, CPU cycles, memory allocations, I/O wait times and thread states, all ingested by Coralogix’s OpenTelemetry-compatible collector. The platform then renders detailed system information—ranging from latency and process IDs (PIDs) to memory allocation and more—within a powerful user interface. This UI enables customers to explore their data visually through flame graphs or investigate the precise impact of every individual process via an interactive table. Within the table, users can traverse each row to understand the role a specific PID has played in contributing to overall process latency, CPU utilization, and additional system metrics.

Later this year, the company will broaden support to include off-CPU profiling (blocked and scheduling delays), GPU utilization metrics, detailed memory-allocation insights and fine-grained I/O profiling—offering end-to-end visibility across every performance dimension.

“Traditional profiling solutions may provide useful insights, but their invasive and resource-intensive nature often degrades performance,” said Ariel Assaraf, CEO of Coralogix. "With Coralogix Continuous Profiling, we’re giving teams an unprecedented lens into production code paths -- automatically and without compromise. By pairing eBPF’s efficiency with Coralogix’s cross-stack observability, we are enabling organizations to accelerate root-cause analysis, optimize resources, and cut costs, all in one unified platform.”

Benefits of the new feature include:

  • Rapid Deployment: Get up and running in minutes without having to modify application code.
  • Performance Optimization: Identify slow function calls within production applications, enabling immediate code optimizations.
  • Cost Efficiency: Quickly detect and mitigate resource-intensive processes, directly lowering infrastructure costs.
  • Enhanced Troubleshooting: Correlate stack traces with logs and metrics, dramatically reducing incident resolution times.

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Coralogix Launches Advanced Continuous Profiling

Coralogix announced Continuous Profiling, an advanced capability that delivers real-time visibility into application performance without any code changes or production impact. 

It seamlessly integrates with Coralogix logs, metrics and traces to help engineering teams pinpoint and resolve bottlenecks in minutes.

Continuous profiling allows developers to connect overall CPU utilization with the underlying application, OS, and kernel processes that are impacting overall performance. By continuously collecting and storing profiling data, developer teams can visualize trends in processes, connect spikes or anomalies with code changes, and highlight troublesome PIDs before they escalate. However, traditional profiling solutions can significantly impact production performance by creating additional workload for the application, consuming extra CPU and memory, and persistently collecting large volumes of data.

Coralogix’s Continuous Profiling eliminates this problem by leveraging vendor-neutral eBPF (extended Berkeley Packet Filter) probes together with the OpenTelemetry standard to deliver kernel-level, always-on performance telemetry with less than 1% overhead. These lightweight in-kernel probes capture high-frequency stack traces, CPU cycles, memory allocations, I/O wait times and thread states, all ingested by Coralogix’s OpenTelemetry-compatible collector. The platform then renders detailed system information—ranging from latency and process IDs (PIDs) to memory allocation and more—within a powerful user interface. This UI enables customers to explore their data visually through flame graphs or investigate the precise impact of every individual process via an interactive table. Within the table, users can traverse each row to understand the role a specific PID has played in contributing to overall process latency, CPU utilization, and additional system metrics.

Later this year, the company will broaden support to include off-CPU profiling (blocked and scheduling delays), GPU utilization metrics, detailed memory-allocation insights and fine-grained I/O profiling—offering end-to-end visibility across every performance dimension.

“Traditional profiling solutions may provide useful insights, but their invasive and resource-intensive nature often degrades performance,” said Ariel Assaraf, CEO of Coralogix. "With Coralogix Continuous Profiling, we’re giving teams an unprecedented lens into production code paths -- automatically and without compromise. By pairing eBPF’s efficiency with Coralogix’s cross-stack observability, we are enabling organizations to accelerate root-cause analysis, optimize resources, and cut costs, all in one unified platform.”

Benefits of the new feature include:

  • Rapid Deployment: Get up and running in minutes without having to modify application code.
  • Performance Optimization: Identify slow function calls within production applications, enabling immediate code optimizations.
  • Cost Efficiency: Quickly detect and mitigate resource-intensive processes, directly lowering infrastructure costs.
  • Enhanced Troubleshooting: Correlate stack traces with logs and metrics, dramatically reducing incident resolution times.

The Latest

As discussions around AI "autonomous coworkers" accelerate, many industry projections assume that agents will soon operate alongside human staff in making decisions, taking actions, and managing tasks with minimal oversight. But a growing number of critics (including some of the developers building these systems) argue that the industry still has a long way to go to be able to treat AI agents like fully trusted teammates ...

Enterprise AI has entered a transformational phase where, according to Digitate's recently released survey, Agentic AI and the Future of Enterprise IT, companies are moving beyond traditional automation toward Agentic AI systems designed to reason, adapt, and collaborate alongside human teams ...

The numbers back this urgency up. A recent Zapier survey shows that 92% of enterprises now treat AI as a top priority. Leaders want it, and teams are clamoring for it. But if you look closer at the operations of these companies, you see a different picture. The rollout is slow. The results are often delayed. There's a disconnect between what leaders want and what their technical infrastructure can handle ...

Kyndryl's 2025 Readiness Report revealed that 61% of global business and technology leaders report increasing pressure from boards and regulators to prove AI's ROI. As the technology evolves and expectations continue to rise, leaders are compelled to generate and prove impact before scaling further. This will lead to a decisive turning point in 2026 ...

Cloudflare's disruption illustrates how quickly a single provider's issue cascades into widespread exposure. Many organizations don't fully realize how tightly their systems are coupled to thirdparty services, or how quickly availability and security concerns align when those services falter ... You can't avoid these dependencies, but you can understand them ...

If you work with AI, you know this story. A model performs during testing, looks great in early reviews, works perfectly in production and then slowly loses relevance after operating for a while. Everything on the surface looks perfect — pipelines are running, predictions or recommendations are error-free, data quality checks show green; yet outcomes don't meet the ground reality. This pattern often repeats across enterprise AI programs. Take for example, a mid-sized retail banking and wealth-management firm with heavy investments in AI-powered risk analytics, fraud detection and personalized credit-decisioning systems. The model worked well for a while, but transactions increased, so did false positives by 18% ...

Basic uptime is no longer the gold standard. By 2026, network monitoring must do more than report status, it must explain performance in a hybrid-first world. Networks are no longer just static support systems; they are agile, distributed architectures that sit at the very heart of the customer experience and the business outcomes ... The following five trends represent the new standard for network health, providing a blueprint for teams to move from reactive troubleshooting to a proactive, integrated future ...

APMdigest's Predictions Series concludes with 2026 AI Predictions — industry experts offer predictions on how AI and related technologies will evolve and impact business in 2026. Part 5, the final installment, covers AI's impacts on IT teams ...

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