Unravel Data Platform
Unravel Data Platform is an AI-powered observability and FinOps platform built specifically for modern data stacks, targeting data engineering, platform, and FinOps teams at Fortune 500 enterprises.
Publisher review
Unravel Data Platform is an AI-powered observability and FinOps platform built specifically for modern data stacks, targeting data engineering, platform, and FinOps teams at Fortune 500 enterprises. Founded in 2016 and headquartered in Mountain View, California, the platform provides end-to-end visibility across Databricks, Snowflake, and Google BigQuery environments, automatically detecting and remediating performance problems while optimizing resource allocation and cloud spend. It is used by companies such as Novartis, Mastercard, and Equifax to manage complex, multi-cloud data pipelines.
The platform is trained on millions of workloads from these three platforms, enabling it to understand Spark shuffle behavior, Snowflake micro-partition strategies, and BigQuery slot allocation patterns at a depth that general-purpose FinOps tools cannot match. Unravel delivers AI-powered recommendations and automation for continuous improvement, including predictive spend forecasting, proactive cost anomaly detection, and autonomous performance and infrastructure optimizations. It supports hybrid and multi-cloud configurations across all major cloud providers.
The platform's live feed shows real-time optimizations; for example, a Databricks Delta table ZORDER and vacuum operation compacted 4,200 small files, reducing costs by 41% and improving performance by 2.4x, while a Snowflake warehouse auto-suspend and multi-cluster scale-down change reduced costs by 27%. Unravel competes primarily with Databricks Data Intelligence Platform and Astro, but its narrow focus on data platform FinOps and performance optimization differentiates it from broader observability tools. Honest trade-offs include an overwhelming initial user experience due to the sheer number of views, metrics, and options; potential implementation issues during setup; a subscription pricing model that can become expensive for large clusters; and a learning curve for teams not already deep in data platform operations. The platform is recognized on CRN's Cloud 100 list for 2025 and won the SIIA CODiE Award for Best Data Tool & Platform.
How it works
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End-to-end stack visibility
Monitors data pipelines across Databricks, Snowflake, and BigQuery, correlating workload performance with cost at job and user levels.
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Autonomous performance remediation
Automatically detects and fixes performance problems, such as skewed joins or inefficient table layouts, without manual intervention.
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AI-powered cost optimization
Delivers predictive spend forecasting and proactive anomaly detection, with AI-generated recommendations that have saved customers over $2.34M YTD.
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Multi-cloud and hybrid support
Works across AWS, Azure, and GCP environments, managing data pipelines that span multiple cloud providers and on-premises systems.
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Workload-specific FinOps
Trained on millions of workloads, it understands Databricks DBU consumption, Snowflake credit burn, and BigQuery slot utilization for precise optimization.
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Real-time optimization feed
Live feed shows applied optimizations with specific cost and performance impacts, such as a 61% cost reduction and 2.6x speedup on a finance ETL job.
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Agentic automation
Recent enhancements include agentic automation for 'two-click' insights and real-time cost-performance evaluations down to individual jobs and users.
Strengths and trade-offs
Strengths
- Enables enterprises to more than double their data workloads without increasing costs, as reported by Fortune 500 customers.
- Provides actionable, AI-generated recommendations that have achieved an average speedup of 3.1x across monitored workloads.
- Offers automated troubleshooting and prescriptive cost solutions, reducing the need for manual tuning by data engineering teams.
- Recognized on CRN's Cloud 100 list for 2025 and won the SIIA CODiE Award for Best Data Tool & Platform.
Trade-offs
- The initial user experience can be overwhelming due to the large number of different views, metrics, and configuration options available.
- Initial implementation may encounter integration issues, particularly in complex multi-cloud environments with legacy pipelines.
- Subscription pricing based on nodes or pipelines can become expensive for organizations managing very large clusters or high-volume data flows.
- The platform's deep specialization on Databricks, Snowflake, and BigQuery means it does not support other data platforms like Redshift or Synapse.
Pricing context
Subscription-based pricing, typically charged per node or per cloud data pipeline managed; specific tier pricing is not publicly disclosed.
Getting started with Unravel Data Platform
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Sign up for Unravel
Navigate to the Unravel Data Platform website and click the Get Started button. Fill in your work email, company name, and role to create an account. Verify your email address to activate the account and access the onboarding dashboard.
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Connect your data platforms
In the Unravel console, go to the Integrations section and select your data platform (Databricks, Snowflake, or BigQuery). Provide the required connection details such as workspace URL, API token, or service account credentials. Test the connection to ensure Unravel can ingest workload data.
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Configure monitoring scope
Set up monitoring by selecting the specific warehouses, clusters, or projects you want Unravel to observe. Define cost allocation tags and budget thresholds for FinOps tracking. Adjust alert preferences for performance anomalies and cost spikes to match your team's operational needs.
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Review optimization recommendations
Open the Recommendations dashboard to view AI-generated suggestions for performance and cost improvements. Filter by impact or confidence score. Click on a recommendation to see its estimated savings and performance gain, then apply it directly or schedule it for later execution.
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Schedule automated optimizations
Navigate to the Automation section and create a new policy. Choose the optimization type (e.g., auto-suspend idle warehouses, compact small files) and set the trigger conditions. Define the execution schedule and approval workflow, then enable the policy to run autonomously.
Frequently Asked Questions
What is Unravel Data Platform?
Unravel Data Platform is an AI-powered observability and FinOps platform for modern data stacks. It provides end-to-end visibility across Databricks, Snowflake, and Google BigQuery, automatically detecting and fixing performance problems while optimizing resource allocation and cloud spend for enterprises.
How does Unravel Data Platform optimize cloud costs?
Unravel uses AI trained on millions of workloads to deliver predictive spend forecasting and proactive cost anomaly detection. It provides recommendations that have saved customers over $2.34 million year-to-date, with real-time optimizations like auto-suspend and multi-cluster scaling to reduce costs.
What data platforms does Unravel support?
Unravel supports Databricks, Snowflake, and Google BigQuery environments. It works across AWS, Azure, and GCP for hybrid and multi-cloud configurations, but does not support other platforms like Redshift or Synapse due to its deep specialization on these three.
What are the main features of Unravel Data Platform?
Key features include end-to-end stack visibility, autonomous performance remediation, AI-powered cost optimization, multi-cloud support, workload-specific FinOps, a real-time optimization feed, and agentic automation for two-click insights and real-time cost-performance evaluations.
How much does Unravel Data Platform cost?
Unravel uses a subscription-based pricing model, typically charged per node or per cloud data pipeline managed. Specific tier pricing is not publicly disclosed, and costs can become expensive for organizations with very large clusters or high-volume data flows.
What are the weaknesses of Unravel Data Platform?
Weaknesses include an overwhelming initial user experience due to many views and metrics, potential implementation issues in complex multi-cloud environments, expensive subscription pricing for large clusters, and lack of support for platforms like Redshift or Synapse.
Alternatives
How Unravel Data Platform compares
Direct head-to-head against 3 competitors. Picked by 7wData.
Unravel Data Platform
- Pricing
- Subscription-based pricing, typically charged per node or per cloud data pipeline managed; specific tier pricing is not publicly disclosed.
- Target
- Unravel Data Platform is an AI-powered observability and FinOps platform built specifically for modern data stacks, targeting data engineering, platform, and FinOps teams at Fortune
- Strength
- Enables enterprises to more than double their data workloads without increasing costs, as reported by Fortune 500 customers.
- Watch for
- The initial user experience can be overwhelming due to the large number of different views, metrics, and configuration options available.
Databricks Data Intelligence Platform
- Pricing
- Custom/Contact sales
- Target
- Enterprise data teams
- Deployment
- Cloud, On-prem
- Strength
- Unified analytics and AI platform
- Watch for
- Complex pricing model
Astro
- Pricing
- Custom/Contact sales
- Target
- Data workflow automation
- Deployment
- Cloud
- Strength
- Workflow standardization
- Watch for
- Initial setup complexity
Monte Carlo
- Pricing
- Custom/Contact sales
- Target
- Data observability
- Deployment
- Cloud
- Strength
- Data health monitoring
- Watch for
- Heavy reliance on AI
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Sources
Reporting on this tool draws on these publicly available sources.