Databricks Lakewatch vs Graylog AI-Powered Security: Side-by-Side Comparison (2026)

Databricks Lakewatch

Enterprise SOCs drowning in petabyte-scale security data will find real value in Databricks Lakewatch because it actually stores and analyzes that volume without forcing you into expensive data movement or retention tradeoffs. The platform covers DE.CM and DE.AE strongly through agentic threat detection, though incident response automation (RS.MA and RS.AN) remains lighter than dedicated SOAR platforms. Skip this if your team needs out-of-the-box playbooks and tight third-party tool orchestration; Lakewatch assumes you can architect workflows on an open lakehouse foundation.

Graylog AI-Powered Security

Mid-market and enterprise security teams drowning in log volume will find real value in Graylog AI-Powered Security's AI-driven investigation capabilities, which collapse weeks of manual threat hunting into hours. The platform covers NIST DE.CM and DE.AE strongly, meaning anomaly detection and incident characterization are genuinely mature; hybrid deployment (cloud, on-premises, or mixed) removes the either-or constraint that blocks adoption at organizations with legacy infrastructure. Skip this if you need deep incident response orchestration or recovery automation; Graylog prioritizes detection and analysis over the post-breach remediation workflows that enterprise SOCs increasingly demand.