Coralogix secures U.S. Dept of Education sponsorship for FedRAMP Moderate, paving the way for secure AI observability in federal agencies.

Coralogix

Managing reliability in microservices at scale requires moving beyond manual log inspection. When services breach SLOs and enter the 'Red Zone,' exhausting error budgets, engineers face millions of telemetry events that make manual Lucene queries impractical. Coralogix's Loggregation feature uses unsupervised ML (the Streama engine) to cluster logs into structural templates by separating high-cardinality variables (user IDs, trace IDs) from constant error patterns. A practical SLO recovery workflow is demonstrated: starting from a Service Catalog showing 6 breaches, filtering 2.13 million logs down to 31 templates, then 16 error templates, drilling into the highest-frequency pattern, and using DataPrime queries to determine whether failures are pod-specific or systemic. This template-based approach reduces MTTR, prevents cognitive overload, and restores error budgets faster than raw log searching.

Turn Raw Data into Reliability by Changing Performance Perspectives

II. Efficiency Gaps in Traditional Observability

IV. The Technical Workflow: Save the SLOs