Keebo

Keebo is an autonomous data warehouse optimization platform designed specifically for Snowflake and Databricks environments. It continuously monitors and adjusts warehouse configurations, query execution plans, and resource allocations to maintain predictable performance while reducing operational overhead and cloud costs. The system employs agentic AI and workload intelligence to go beyond simple visibility and reporting tools, actively making real-time tuning decisions without requiring manual intervention from data engineers or administrators. One of its core functions is automatic cost optimization. By analyzing historical usage patterns, query complexity, and concurrency demands, Keebo dynamically resizes virtual warehouses, scales clusters, and adjusts caching strategies to match workload needs. This results in an average verified cost reduction of 27%, as measured across customer deployments. The platform also enforces five layers of performance protection, which include guardrails against regressions, automated rollback mechanisms, and preemptive adjustments to prevent spikes in latency or query failures. Keebo reclaims hundreds of hours per month for engineering teams by eliminating the need for manual performance tuning, troubleshooting slow queries, or adjusting warehouse sizes. Instead of spending time on repetitive cloud administration tasks, data engineers can focus on building new data models, improving data quality, and delivering analytics insights. The platform provides a transparent dashboard that shows optimization actions taken, cost savings achieved, and performance trends over time. For organizations using Snowflake, Keebo automatically manages features like auto-suspend, auto-resume, multi-cluster warehouses, and materialized views. For Databrick