Atlan vs Coralogix

Neither company has publicly disclosed a valuation, making a definitive size comparison difficult. Atlan employs 200-500 people, while Coralogix has 100-500 employees.

Coralogix has raised more in total funding at $270M, compared to Atlan's $78M — a gap of $192M.

Both Atlan and Coralogix share the same Awaira Score of 68/100. The Awaira Score is a composite metric that factors in valuation, total funding raised, company stage, employee count, and market category.

Atlan was founded by Prukalpa Sankar in 2018. Coralogix was founded by Ariel Assaraf in 2015. Visit each company's profile on Awaira for a full founder biography.

Atlan: Atlan is an active metadata platform and data workspace that enables data teams to discover, understand, and govern data assets across cloud warehouses, lakes, BI tools, and pipelines through an AI-powered catalog interface. The platform automatically crawls data infrastructure to build lineage graphs, enables natural language search over data assets, and enforces data governance policies through automated tagging and access controls.\n\nThe company raised approximately $78M in Series C funding from investors including Insight Partners and Sequoia India, and has built a global customer base of data-forward technology companies and enterprises. Atlan has emerged as a recognized leader in the data catalog market, competing with Alation, Collibra, and DataHub in the modern data stack ecosystem.\n\nAs cloud data infrastructure has proliferated, data discoverability and governance have become critical operational problems for engineering teams managing hundreds of data sources. Atlan's modern metadata-first approach and deep integrations with the contemporary data stack — Snowflake, dbt, Fivetran, Looker — position it well in a market where legacy catalog vendors have struggled to keep pace with infrastructure evolution. Coralogix: 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.

Coralogix was founded first in 2015, giving it 3 years of additional market experience. Atlan was founded later in 2018. In AI, even a year or two of head start can translate into significantly more training data, customer relationships, and institutional knowledge.

Atlan has approximately 200-500 employees, while Coralogix has approximately 100-500. A larger team often signals higher revenue or venture backing, but in AI, smaller teams are increasingly capable of building at scale.

Yes, Atlan and Coralogix are direct competitors — both operate in the AI Data space and likely target overlapping customer segments. This comparison is especially relevant for buyers evaluating both platforms.