Performance. Reliability. Cost-effectiveness.
Unravel's automated, AI-powered data observability + FinOps platform for Databricks on AWS and other modern data stacks provides 360-degree visibility to allocate costs with granular precision, accurately predict spend, run 50% more workloads at the same budget, launch new apps 3x faster, and reliably hit greater than 99% of SLAs.
Purpose-built AI for supercharging data teams.
AI isn't new to Unravel. We've worked on automating tasks for data teams over the last 10 years after observing more than 50 million data pipelines and queries. Working closely with data teams, we have learned that observability solutions cannot stop at simply showing "what" happened. They also need to break down "why" something happened and "how" it can be remediated. Only then can data teams stop wasting time on firefighting issues and spend more time innovating.
Unravel's AI helps automate critical tasks such as ensuring data pipelines and AI models run reliably, the data platform costs less and scales efficiently, and data applications generate correct results. Data engineering, product, business, and finance teams finally have a single platform to deliver the AI goals of their companies.
3x faster time to launch new apps - End-to-end observability of data-native applications and pipelines. Automatic improvement of performance, cost efficiency, and reliability.
50% more workloads for the same budget and +/- 10% budget forecast accuracy - Break down spend and forecast accurately. Optimize apps and platforms by eliminating inefficiencies. Set guardrails and automate governance. Unravel's AI helps you implement observability and FinOps to ensure you achieve efficiency goals.
99% less firefighting time using AI-enabled troubleshooting - Detect anomalies, drift, skew, missing and incomplete data end-to-end. Integrate with multiple data quality solutions. All in one place.
AWS Marketplace now accepts line of credit payments through the PNC Vendor Finance program. This program is available to select AWS customers in the US, excluding NV, NC, ND, TN, & VT.
Pricing is based on the duration and terms of your contract with the vendor, and additional usage. You pay upfront or in installments according to your contract terms with the vendor. This entitles you to a specified quantity of use for the contract duration. Usage-based pricing is in effect for overages or additional usage not covered in the contract. These charges are applied on top of the contract price. If you choose not to renew or replace your contract before the contract end date, access to your entitlements will expire.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
You pay for Unravel on a usage basis, tied to your Databricks consumption measured in Units. The listing prices four Databricks workload types separately: Interactive/All purpose, Jobs/Workflows, SQL, and DLT. Each covers both Classic and Serverless compute. The Unravel Pay as You Go dimension means you only pay for what you use, with no upfront commitment. The core unravel_payg_aws line reflects list pricing that already includes volume discounts. Your total scales with how much of each monitored workload type you run each month.
Top-of-mind questions for buyers
What does one Unit map to for billing across the workload types?
Units track your Databricks consumption, measured in Databricks Units (DBUs). Unravel meters usage based on the cluster or warehouse type it monitors. Each workload type — Interactive/All purpose, Jobs/Workflows, SQL, and DLT — counts the DBUs consumed by that workload category, whether you run Classic or Serverless compute.
Do underlying cloud compute and storage costs count toward the Units I pay for?
No. Units reflect Databricks Unit (DBU) consumption only. Databricks Units are reference capacity units used to price data workloads. Cloud resources such as compute instances and cloud storage are billed separately, outside this listing. Your Unravel charges track the monitored DBU usage, not your raw AWS infrastructure bill.
Which workload dimension drives most of my monthly cost?
The four workload dimensions bill independently and add together on one invoice. The dimension driving the largest share depends on your usage mix. Heavy interactive cluster use raises the Interactive/All purpose line. Scheduled pipelines raise Jobs/Workflows. Query-heavy warehouses raise SQL. Streaming pipelines raise DLT. Your total scales with each type's monthly consumption.
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