Databricks Cloud Costs: Why Your Bill is Growing Beyond Just DBUs

Databricks has become a key strategic platform for organizations building modern AI and data-driven solutions. As AI reshapes every industry, Databricks positions itself at the core of this transformation, growing rapidly on the back of demand for unified, open, and governed data and AI platforms. The company emphasizes its unique role as a data intelligence engine, enabling organizations to operationalize data and AI at scale. With AI adoption accelerating, many teams are doubling down on Databricks as their cloud-native foundation for everything from data pipelines to advanced analytics.

Organizations run Databricks in the cloud to gain the elasticity, flexibility, and scale needed to accelerate AI and data innovation – without the burden of managing infrastructure. Cloud deployments make it easier to start fast, scale on demand, and integrate seamlessly with a growing ecosystem of services. Yet with these advantages come hidden Databricks cloud costs and infrastructure charges that many teams underestimate until the bill arrives. The cloud makes it easy. Too easy, sometimes.

DBUs: The Starting Point for Databricks Cloud Costs

When teams first dig into Databricks cloud costs, they almost always start with DBUs – and for good reason. A Databricks Unit (DBU) represents the consumption-based charge for the processing power used by workloads, billed per second. It’s the most visible and direct cost metric in Databricks billing. But while DBUs provide an important view into usage, they don’t tell the full story.

The Cloud Infrastructure Behind Databricks – and Its Hidden Costs

While DBUs might dominate the conversation, they’re just the start. A significant share of Databricks cloud costs often comes from the supporting cloud infrastructure that powers these workloads. These costs are easy to overlook without proper visibility, tracking, and preventative policies.

• Compute: The compute resources such as Azure VMs, AWS EC2, and GKE are provisioned on demand. Without proper allocation, optimization, and policy enforcement, these instances can remain unallocated, underutilized, or idle, leading to unnecessary costs.
• Storage: Persistent storage tied to Databricks, including data volumes, logs, and intermediate outputs, results in ongoing charges. Optimizing usage, retention strategies, and managing data transfer fees is essential to control these costs.
• Networking: Data transfers, cross-region traffic, and egress fees accumulate quickly as data moves across services, accounts, and regions. Without the right configurations, teams often end up paying more than necessary for performance that may not be fully utilized.
• Workspaces: On Microsoft Azure, Databricks workspaces are native resources that come with a fixed baseline cost, adding an extra layer of expense unique to that cloud provider. On GCP, each workspace requires a dedicated GKE cluster, which introduces additional infrastructure costs even when the cluster is idle.

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Databricks Cloud Costs, Decoded: How Stacklet Unlocks Visibility, Savings, and Continuous Optimization

Stacklet, built by the creators of the open-source Cloud Custodian project, helps organizations not just gain visibility into cloud costs but govern and optimize them at scale. For teams running Databricks or the FinOps teams tracking those costs, Stacklet makes it possible to finally understand and control the full cloud cost landscape beyond just DBUs – saving a significant portion of the cloud spend associated with Databricks, in up to 6x less time. By improving the organization’s Mean Time to Savings (MTTS), Stacklet helps teams capture Databricks-related savings faster and maintain them through continuous infrastructure optimization and guardrails that prevent waste from returning.

Tagging and Cost Allocation: Revealing the Complete Cost of Databricks

Tagging is the first step in turning cloud cost complexity into clarity. Stacklet automates tagging across cloud resources provisioned by Databricks – ensuring that every compute instance, storage volume, network path, and even Azure workspace is consistently labeled. This includes both cost allocation metadata like cost center, project, and team, as well as tags that explicitly identify the resource as part of your Databricks environment.

With enforced tagging, organizations can accurately allocate Databricks-related costs across projects, teams, or business units. Powered by Stacklet AssetDB, a real-time cloud asset inventory, teams gain a complete, queryable view of all tagged resources across their environment. This makes it easier to report, track, and manage the full cloud costs tied to Databricks. Stacklet also continuously validates tags and remediates missing or incorrect ones, ensuring your cost data stays accurate and actionable.

Usage Optimization and Governance for Compute, Storage, Networking, and Workspaces

Once cost allocation and tracking is in place, Stacklet helps drive AI-driven continuous usage optimization and governance across key infrastructure layers. The platform comes with hundreds of out-of-the-box policies that detect waste and inefficiencies, eliminate unnecessary resources, and automatically prevent them from returning. It also provides an easy on-ramp for creating custom policies and remediation workflows, with generative AI and natural language interfaces that simplify policy creation and adaptation to your unique needs.

Here are some of the key categories and examples of what Stacklet can optimize and govern:

• Compute: Detect and trigger remediation workflows for idle, underutilized, or oversized VMs powering Databricks clusters. Stacklet can flag clusters without autoscaling enabled or recommend shifting workloads to lower-cost options like spot instances.

• Storage: Identify lingering storage volumes, logs, and checkpoints tied to Databricks workloads that haven’t been accessed within a defined period, such as 30 or 60 days. Policies can recommend or trigger multi-step notification and actions for archival, deletion, or moving data to cheaper storage tiers.
• Networking: Uncover inefficiencies in data transfer, inter-region traffic, and egress costs. Stacklet policies detect costly patterns in how Databricks jobs move data across services, accounts, and regions – enabling teams to reconfigure for better cost-performance balance.
• Workspaces (Azure-specific): Monitor Azure Databricks workspaces to ensure they are properly tagged, tracked, and accounted for in cost reports. Even fixed workspace charges can add up and need visibility in FinOps governance.

Databricks is a transformative platform for data and AI, but controlling the full scope of its cloud costs requires more than just monitoring DBUs. Without visibility and guardrails across infrastructure, storage, networking, and workspaces, the true cost of Databricks can remain hidden until the bill arrives. Stacklet provides the visibility teams need while accelerating their Mean Time to Savings (MTTS) through automation, optimization, and governance. The result: Databricks becomes not just a strategic platform, but an efficient and cost-optimized one.