November 2025 Summaries

Coralogix's introduction of Dataspaces and Datasets through its DataPrime engine marks a significant advancement in managing observability data at scale by providing a structured approach to organizing, governing, and analyzing telemetry and platform-usage events. The new architecture allows for logical data segmentation, enabling teams to isolate data based on specific boundaries like team, environment, or compliance domain, which enhances data clarity, control, and analytics predictability. This innovation addresses the challenges posed by traditional observability data management, such as schema collisions, governance issues, and performance degradation, by allowing data to be stored in smaller, purpose-built Datasets within Dataspaces, each governed by consistent policies. This structure not only simplifies governance and compliance but also accelerates analytics and provides granular cost visibility, ultimately offering a unified, scalable solution for managing observability data across organizations.

Metrics Usage Analyzer and Query Usage Analyzer are tools designed to help teams better manage and understand the value of their metrics amidst growing telemetry and rising costs. These tools provide transparency by identifying which metrics are driving storage costs and how they are being queried across platforms, thus enabling teams to make informed decisions about which metrics to keep or eliminate. For instance, the Metrics Usage Analyzer offers real-time insights into metric ingestion, highlighting patterns that contribute to high cardinality and cost, while the Query Usage Analyzer tracks the actual usage of metrics in dashboards and alerts. By using these tools, teams can confidently clean up unused data, reduce unnecessary ingestion costs, and streamline their observability setups, ultimately allowing for more efficient and cost-effective management of their metrics stack.

Coralogix, a company specializing in observability solutions, highlights the challenges of understanding system performance through tracing, which allows engineers to follow requests in distributed systems but often lacks contextual analysis. To address this, Coralogix introduces DataPrime, a tool designed to provide comparative span analysis by evaluating spans against historical baselines to detect anomalies and performance changes. DataPrime allows engineers to dynamically analyze spans, correlate them with logs and events, and classify performance deviations, providing insights beyond traditional tracing methods. This approach empowers engineers to quickly identify and investigate performance issues, transforming observability data into actionable insights. DataPrime's flexibility and capabilities in handling real-time, dynamic data make it a powerful tool for modern observability, as demonstrated by its successful implementation in high-traffic API environments.

In the rapidly evolving landscape of artificial intelligence, the development process has shifted dramatically from traditional programming to the use of AI-driven tools, which allow for the creation of complex applications with minimal human input. This shift has made measurement and real user monitoring (RUM) crucial for understanding app performance and user experience, as developers need to ensure that the machine-generated logic results in a positive user experience. RUM provides insights into user behavior, helping developers identify and address issues like inefficiency, poor performance, and user frustration. As code becomes increasingly commoditized and applications are built more by systems than by humans, developers' roles are evolving to focus more on tuning systems and optimizing the user experience. This new era demands real-time observability to match the high velocity of software development, emphasizing the importance of accountability and user-centric design in maintaining a competitive edge in an AI-driven world.

Coralogix has adopted FinOps strategies to optimize cloud infrastructure spending, emphasizing the use of Continuous Profiling, a tool traditionally used for debugging, to gain detailed insights into resource utilization. Despite the widespread increase in cloud expenditures, with companies often exceeding their budgets and wasting significant portions of their spending, Coralogix successfully utilized Continuous Profiling to address inefficiencies in their system. Faced with a high-demand event from a major customer, they identified a bottleneck caused by the io.circe JSON parser, which led to spikes in CPU usage. By switching to a more efficient parsing library, they achieved a 50% reduction in cloud spending and CPU usage for that service. This experience underscored the broader applicability of profiling tools beyond traditional engineering roles, suggesting their potential value for FinOps and DevOps teams to improve resource management and cost efficiency.

In a blog post by Lewis Isaac, the challenges of transforming mobile app telemetry into meaningful alerts are explored, highlighting the importance of context-specific thresholds tailored to an app's unique functions. Isaac describes the creation of a tool, the Mobile Metrics Advisor, which uses Generative AI to recommend metrics and alerts based on an app’s industry and other factors, though it initially struggled with relevance due to a lack of real-time telemetry integration. The introduction of Coralogix's AI-native observability agent, Olly, is presented as a solution that grounds AI recommendations in live data, allowing for adaptive and accurate alerting based on actual user behavior and traffic patterns. The post underscores the necessity of combining AI tools with real telemetry to avoid irrelevant alerts and maintain dynamic thresholds, ultimately leading to smarter, adaptive alert systems.

Coralogix is gearing up for AWS re:Invent 2025 in Las Vegas, where it will showcase its autonomous AI agent, Olly, designed for full-stack observability. Attendees can visit Coralogix at Booth #1739 to see live demonstrations, customize hats at a swag station, and participate in speaking sessions led by company leaders discussing the Observability Maturity Model and AI-native observability. The event will also feature the Coralogix re:Charge Lounge, offering a space to relax and attend workshops on AI-driven observability and security. Additionally, Coralogix will host a networking happy hour in collaboration with Commit, aiming to build the future of observability through engaging talks and interactive sessions.

At AWS re:Invent 2025, the discussion around AI has shifted from its capabilities to securing and trusting it at scale, with Coralogix emphasizing the importance of building AI on a secure foundation. Despite the rapid pace of AI adoption, security measures such as visibility, least privilege, data protection, and continuous monitoring remain crucial, as AI models operate dynamically, challenging traditional security controls. Coralogix addresses these challenges by offering a unified platform that integrates security, observability, evaluation, and posture management for AI applications, providing real-time risk detection and AI Security Posture Management (AI-SPM) to ensure compliance and security. Their AI Center features an evaluation engine analyzing AI behavior, security guardrails to prevent high-risk activities, and a comprehensive dashboard for monitoring AI security posture. By doing so, Coralogix helps enterprises innovate responsibly, ensuring speed and assurance coexist in AI deployment, which can be witnessed at their booth at the event.

In large tech organizations, maintaining IT asset visibility is a significant challenge due to frequent changes in device assignment and the influx of logs from various systems. This complexity can lead to security risks if unmonitored devices fall out of compliance. A global enterprise faced this issue and turned to DataPrime, a tool by Coralogix, to address their IT asset visibility problems. DataPrime effectively unified fragmented data streams from different systems by reshaping and standardizing structures, enabling IT teams to detect inactive devices and compliance violations in real-time. By embedding business logic directly into queries and visualizing data through dashboards, DataPrime allowed the organization to achieve real-time device intelligence, which enhanced proactive compliance and operational efficiency. The solution transformed the cumbersome process of reconciling device data into an agile system that provided dynamic insights, reducing manual checks and improving decision-making based on real data. This case exemplifies how adaptable tools like DataPrime are essential in navigating the complexities of modern data observability and operational intelligence.

Coralogix, a full stack observability platform, has appointed Chetan Chaudhary as Chief Revenue Officer to spearhead its global go-to-market strategy, marking a significant milestone in the company's growth trajectory. With over two decades of experience in scaling revenue organizations from his roles at Scale AI, Twilio, and Cisco, Chaudhary will oversee sales, customer success, marketing, and partnerships, enhancing Coralogix’s position in the competitive observability market. His appointment follows Coralogix's acquisition of Aporia, which bolstered its machine-learning and AI capabilities, particularly through its AI observability product, Olly. This product offers advanced intelligence beyond mere chatbots, enabling enterprises to automate and optimize data monitoring. CEO Ariel Assaraf expressed confidence in Chaudhary's ability to drive enterprise growth and transform the company as it prepares for a public offering. Coralogix continues to expand its influence in AI-driven full-stack observability, with Olly's success leading to increased customer adoption and recognition in the industry.

Logs are increasingly recognized as a valuable data asset in modern systems, capturing detailed narratives of user behavior, requests, errors, and business events, and offering insights into user experience, performance, and security. Their ability to dynamically map fields without rigid schemas and to perform advanced query operations makes them indispensable for quick troubleshooting, product development, and compliance auditing. With the use of object storage like S3, logs can be stored efficiently for long-term analysis without incurring high costs, enabling organizations to maintain a comprehensive history of data. Coralogix exemplifies the potential of treating logs as a primary data source, providing a unified platform that integrates logs with metrics, traces, and security data, enhancing observability and data-driven decision-making across business operations.

Traditional Security Information and Event Management (SIEM) systems, designed for static infrastructures, are becoming obsolete in the face of dynamic, cloud-based environments where data is scattered and attackers are increasingly sophisticated. Modern Security Operations Centers (SOCs) face challenges such as fragmented data, blindspots, alert fatigue, and manual investigations that slow their response to threats. The Coralogix MCP Server addresses these issues by integrating artificial intelligence to provide context, automate investigations, and prioritize threats in real-time, transforming SIEMs from passive data collectors into active investigation partners. By enabling natural language queries and connecting multiple data streams, the MCP Server allows even junior analysts to conduct complex investigations swiftly, reducing operational costs and improving coverage by enabling the analysis of medium- and low-severity alerts. The system empowers SOC teams to stay ahead of threats by providing a comprehensive view of data, thereby enhancing their ability to respond decisively and efficiently.