The Hidden Cost of Volatility Drag: Unifying Databricks and Cloud Infrastructure Costs
That said, integrating unified cost dashboards with FinOps and Platform teams has become a crucial step in achieving accurate TCO visibility. To drill into full costs by workspace, workload, and business unit to align usage with budgets, eliminating manual reconciliation is key.
Understanding Total Cost of Ownership (TCO) on Databricks is crucial for making informed decisions about AI and data investments. On multicloud data platforms like Databricks, TCO consists of two core components: Platform costs and Cloud infrastructure costs.
On Databricks, platform costs include compute and managed storage, which are costs incurred through direct usage of Databricks products. Cloud infrastructure costs, such as virtual machines, storage, and networking charges, are costs incurred through the underlying usage of cloud services needed to support Databricks.
Understanding TCO for classic compute products is more complex. Here, customers manage compute directly with the cloud provider, meaning both platform costs and cloud infrastructure costs need to be reconciled. In these cases, there are two distinct data sources to be resolved: System tables (AWS | AZURE | GCP) in Databricks will provide operational workload-level metadata and Databricks usage.
Cost reports from the cloud provider will detail costs on cloud infrastructure, including discounts. Together, these sources form the full TCO view. As your environment grows across many clusters, jobs, and cloud accounts, understanding these datasets becomes a critical part of cost observability and financial governance.
The Complexity of TCO The complexity of measuring your Databricks TCO is compounded by the disparate ways cloud providers expose and report cost data. Understanding how to join these datasets with system tables requires deep knowledge of cloud billing mechanics β knowledge many Databricks-focused platform admins may not have.
That said, leveraging the Cloud Infra Cost Field Solution can simplify this process. This open-source solution automates ingestion and unified analysis of cloud infrastructure and Databricks usage data, inside the Databricks Platform. By providing a unified foundation for TCO analysis across Databricks serverless and classic compute environments, the Field Solution helps organizations gain clearer cost visibility.
To deploy this solution, customers must have the following permissions: Azure Permissions to create an Azure Cost Export, Permissions to create the necessary resources within a Resource Group (Storage Account, External Location), and Databricks Permission to create the necessary resources. Additionally, admins must be assigned the Role Assignment to allow for data export and resource access.
The Aggent AI Playbook for the Enterprise
The Cloud Infra Cost Field Solution is particularly relevant for organizations operating within a single cloud but can also be combined for multicloud Databricks customers using Delta Sharing. Azure Databricks Field Solution
To deploy the Azure Databricks Field Solution, admins must have the following permissions: Azure Permissions to create an Azure Cost Export, Permissions to create the necessary resources within a Resource Group (Storage Account), and Databricks Permission to create the necessary resources. Additionally, admins must be assigned the Role Assignment to allow for data export and resource access.
The GitHub repository provides more detailed setup instructions; however, at a high level, the solution consists of deploying Terraform to configure dependent components, including a Storage Account, External Location, and Volume. This step is optional if there is a preexisting Volume since the Azure Cost Management Export location can be configured in the next step.
Once the Azure Cost Management Export location is configured, it exports Azure Billing data to the Storage Account and confirms data export. The purpose of this step is to use the Azure Cost Managementβs Export functionality to make the Azure Billing data available in an easy-to-consume format (e.g., Parquet).
Storage Account with Azure Cost Management Export Configured Azure Cost Management Export automatically delivers cost files to this location.
Delta Sharing
Databricks on AWS Solution
The Databricks solution for multicloud environments, known as Delta Sharing, allows customers to align Marketplace and infrastructure costs. On AWS, while Databricks costs do appear in the Cost and Usage Report (CUR) and in AWS Cost Explorer, costs are represented at a more aggregated, SKU-level.
Moreover, Databricks costs appear only in CUR when Databricks is purchased through the AWS Marketplace; otherwise, CUR will reflect only AWS infrastructure costs. In this case, understanding how to co-analyze AWS CUR alongside system tables is even more critical for customers with AWS environments. This allows teams to analyze infrastructure spend, DBU usage and discounts together with cluster-and workload-level context, creating a more complete TCO view across AWS accounts and regions.
The GitHub repository provides more detailed setup instructions; however, at a high level, the solution consists of deploying Terraform to configure dependent components, including a Storage Account and External Location. This step is optional if there is a preexisting Volume since the AWS Cost Management Export location can be configured in the next step.
The Cloud Infra Cost Field Solution for Azure Databricks
The Cloud Infra Cost Field Solution for Azure Databricks consists of the following architecture components: Azure Databricks Solution Architecture Numbered steps align to high-level steps listed below To deploy this solution, admins must have the following permissions across Azure and Databricks: Azure Permissions to create an Azure Cost Export, Permissions to create the necessary resources within a Resource Group (Storage Account), Container Access Connector Role Assignment.
Databricks Permission to create the necessary resources : Storage Credential External Location The GitHub repository provides more detailed setup instructions; however, at a high level, the solution for Azure Databricks has the following steps: Terraform Deploy Terraform to configure dependent components, including a Storage Account and External Location, then use the Azure Cost Management Export functionality to make the Azure Billing data available in an easy-to-consume format (e.g., Parquet). Storage Account with Azure Cost Management Export Configured Azure Cost Management Export automatically delivers cost files to this location Databricks Asset Bundle (DAB) Configuration to deploy a Lakeflow Job, Spark Declarative Pipeline and AI/BI Dashboard The purpose of this step is to ingest and model Azure billing data for visualization using an AI/BI dashboard.
Validation
The Cloud Infra Cost Field Solution provides an automated process that enables customers to view the TCO of their Lakehouse architecture. Customers now have an automated process that enables them to view the TCO of their Lakehouse architecture!