Ariel Assaraf

Coralogix provides a machine learning-powered log analytics and observability platform that uses AI to reduce the volume of logs requiring full indexing and storage, applying streaming ML analysis to detect patterns, anomalies, and insights in log data without requiring all data to be indexed at query time. The Tel Aviv and San Francisco company stateful streaming approach reduces observability costs significantly compared to traditional log management tools that index and store all data regardless of utility.\n\nThe company raised approximately $270 000 000 including a Series D round from investors including Brighton Park Capital, Greenfield Partners, and Telstra Ventures. Coralogix counts technology companies and enterprises across cloud-native software development, fintech, and SaaS as clients, competing on cost efficiency relative to Datadog, Splunk, and New Relic for engineering teams with high log ingestion volumes and constrained observability budgets.\n\nCoralogix competes in the log management and observability market where Datadog, Elastic, and Grafana hold dominant positions. Its cost optimization positioning resonates with engineering teams managing rapid data growth who face steeply rising observability bills from usage-based pricing models at incumbent vendors. The company TCO reduction messaging is backed by architectural decisions that trade query flexibility at indexing time for streaming ML analysis that surfaces issues without storing all log data, a trade-off that appeals to cost-conscious engineering operations teams at high-growth software companies.