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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 businesses increasingly rely on high-performance applications to deliver seamless user experiences, the demand for fast, reliable, and scalable data storage systems has never been greater. Redis — an open-source, in-memory data structure store — has emerged as a popular choice for use cases ranging from caching to real-time analytics. But with great performance comes the need for vigilant monitoring ...

Kubernetes was not initially designed with AI's vast resource variability in mind, and the rapid rise of AI has exposed Kubernetes limitations, particularly when it comes to cost and resource efficiency. Indeed, AI workloads differ from traditional applications in that they require a staggering amount and variety of compute resources, and their consumption is far less consistent than traditional workloads ... Considering the speed of AI innovation, teams cannot afford to be bogged down by these constant infrastructure concerns. A solution is needed ...

AI is the catalyst for significant investment in data teams as enterprises require higher-quality data to power their AI applications, according to the State of Analytics Engineering Report from dbt Labs ...

Misaligned architecture can lead to business consequences, with 93% of respondents reporting negative outcomes such as service disruptions, high operational costs and security challenges ...

A Gartner analyst recently suggested that GenAI tools could create 25% time savings for network operational teams. Where might these time savings come from? How are GenAI tools helping NetOps teams today, and what other tasks might they take on in the future as models continue improving? In general, these savings come from automating or streamlining manual NetOps tasks ...

IT and line-of-business teams are increasingly aligned in their efforts to close the data gap and drive greater collaboration to alleviate IT bottlenecks and offload growing demands on IT teams, according to The 2025 Automation Benchmark Report: Insights from IT Leaders on Enterprise Automation & the Future of AI-Driven Businesses from Jitterbit ...

A large majority (86%) of data management and AI decision makers cite protecting data privacy as a top concern, with 76% of respondents citing ROI on data privacy and AI initiatives across their organization, according to a new Harris Poll from Collibra ...

According to Gartner, Inc. the following six trends will shape the future of cloud over the next four years, ultimately resulting in new ways of working that are digital in nature and transformative in impact ...

2020 was the equivalent of a wedding with a top-shelf open bar. As businesses scrambled to adjust to remote work, digital transformation accelerated at breakneck speed. New software categories emerged overnight. Tech stacks ballooned with all sorts of SaaS apps solving ALL the problems — often with little oversight or long-term integration planning, and yes frequently a lot of duplicated functionality ... But now the music's faded. The lights are on. Everyone from the CIO to the CFO is checking the bill. Welcome to the Great SaaS Hangover ...

Regardless of OpenShift being a scalable and flexible software, it can be a pain to monitor since complete visibility into the underlying operations is not guaranteed ... To effectively monitor an OpenShift environment, IT administrators should focus on these five key elements and their associated metrics ...