Compare Keebo vs Unravel Data
Both Keebo and Unravel Data help optimize data workloads, but they take different approaches. Unravel Data focuses on observability and workload performance management, giving teams deep visibility into pipelines, resource usage, and bottlenecks so they can troubleshoot and optimize Spark, SQL, and data platform performance. Keebo goes further with autonomous, real-time optimization that continuously tunes warehouses, queries, and workloads to reduce cost and improve performance without manual effort. In short, Unravel helps you see and diagnose what’s happening, while Keebo actively optimizes your environment for you.
Business Overview
| Capability | Keebo | Unravel Data |
| Founded | 2019 | 2016 |
| Supported Data Clouds | Snowflake Databricks | Snowflake Databricks BigQuery |
| FinOps Foundation Alignment | General Member | – |
| Security and Compliance | SOC 2 | SOC 2 |
| Pricing | Pay-as-you-go or enterprise subscription | Unravel Data charges a monthly or annual subscription fee that is a percentage of spend monitored and optimized by Unravel Data |
Warehouse Optimization
| Capability | Keebo | Unravel Data |
| Autonomous warehouse size adjustments | yes | yes |
| Autonomous warehouse cluster-count adjustments | yes | yes |
| Autonomous warehouse autosuspend adjustments | yes | no |
| Proactive suspension algorithm | yes | no |
| Independently verifiable savings calculation | yes | no |
| Algorithm aggressiveness tuning | yes | no |
| Performance Guardrails (e.g. max latency, queue size, etc.) | yes | no |
| Outside change detection | yes | yes |
| Automated upsizing | yes | no |
| Governance and audit logs for user and system actions | yes | yes |
Observability and Workload Intelligence
| Capability | Keebo | Unravel Data |
| Analyze query performance by warehouse | yes | yes |
| Analyze costs by user or warehouse | yes | yes |
| Projected savings | yes | yes |
| Analyze data spillage by warehouse | yes | no |
| Warehouse utilization analysis | yes | yes |
| Wasteful query detection | yes | yes |
| Under-provisioned warehouse recommendations | yes | yes |
| Query migration recommendations and analysis | yes | yes |
| Memory inefficient recommendations | yes | no |
| Unused and unread data tables recommendations | yes | yes |
| Most expensive queries breakdown | yes | yes |
Commitment to Transparency
Product names, logos, and trademarks are the property of their respective owners. Information is based on public sources and internal analysis as of April 28, 2026 and may change over time. If you identify any errors, please contact us with supporting evidence and we will update the page.