Keebo

Keebo offers an autonomous optimization platform for cloud data warehouses, focusing on Snowflake and Databricks. It continuously tunes warehouses and workloads to maximize compute efficiency, deliver predictable performance, and accelerate engineering velocity. The tool is designed for data engineering, platform, and FinOps teams responsible for performance and cost, aiming to free teams from manual tuning while maintaining SLA-compliant behavior.

Keebo uses agentic AI and workload intelligence to automate optimization. It emphasizes a self-improving loop where usage is observed, patterns are learned, and optimizations are applied automatically. The platform seeks to go beyond visibility and reporting by turning optimization into an autonomous system with automated decision-making.

Keebo highlights a metadata-only approach that avoids changing data or queries, and it operates without requiring disruption to existing tools. It supports continuous optimization and provides visibility into how workloads interact with data clouds, helping teams align data and FinOps efforts around optimization opportunities.

Its mission centers on enabling data teams to innovate without being limited by cost or complexity, using data learning-based optimization to balance performance and efficiency.

Use cases

• Snowflake optimization to get more from every Snowflake credit.
• Databricks optimization to maximize DBU efficiency.
• FinOps for data clouds to close the gap between finding waste and fixing it.
• Performance and efficiency to rightsize without SLA risk.