January 2026 Summaries

Coralogix has introduced System Datasets, a feature that transforms the observability platform into a more transparent, queryable system, allowing users to gain better insights into platform operations. These datasets expose user and platform activity as first-class data, making it easier to analyze costs, improve alert quality, and audit access and configuration changes. Stored as Parquet files and accessible via DataPrime, System Datasets are managed separately from production telemetry, ensuring governance and security. At launch, these datasets include detailed records of query executions, alert histories, notification deliveries, and authentication events, enabling teams to address operational questions and optimize performance. The introduction of System Datasets marks a shift towards treating observability platforms as governable systems that scale with organizational needs, with future plans to expand data exposure to additional areas.

Coralogix has announced its pursuit of FedRAMP Moderate authorization, supported by the U.S. Department of Education's Federal Student Aid office, marking a significant milestone in becoming the first FedRAMP-authorized AI observability platform. This development highlights Coralogix's commitment to providing secure, scalable, and observable AI solutions tailored to meet the unique demands of government agencies. By addressing challenges such as the "black box" problem in AI models and consolidating various monitoring tools into a single platform, Coralogix aims to streamline federal operations and enhance security while reducing costs. The introduction of Olly, an autonomous observability agent, further emphasizes the company's focus on proactive and efficient management of digital environments in the public sector. As Coralogix progresses toward full FedRAMP certification, it reaffirms its dedication to supporting the U.S. government's modernization efforts through innovative technology solutions.

Coralogix has introduced fair usage limits to enhance transparency, predictability, and platform reliability as its customers scale their observability data usage. These limits, starting with metrics and infrastructure monitoring, aim to provide clear feedback and visibility into system behavior rather than impose restrictions. By defining thresholds for high-impact behaviors like ingestion volume and query complexity, the platform helps users understand and adjust usage patterns before they affect stability or performance. When limits are approached or exceeded, Coralogix provides structured diagnostic logs to explain the situation and suggest actions, ensuring teams maintain control and have the necessary context to respond. Unlike traditional quota systems, these limits focus on exposing and understanding system behavior, thereby supporting confident scaling with guardrails instead of hard stops.