Unlock Databricks cost transparency
Gradient's single-pane solution to slaying the Databricks billing beast
In the world of big data and cloud computing, managing costs effectively is a significant challenge. While Databricks provides powerful tools for data engineers and analysts, understanding the complete cost picture can be complex.
Databricks customers receive two separate bills – one for their Databricks usage and another from their cloud provider where clusters were spun up to run Databricks workloads. Without a unified view of these costs, it’s challenging for users to understand the total cost of ownership of their data infrastructure.
The Databricks billing dilemma
Databricks tracks usage through a metric called Databricks Units (DBUs), which represent the compute resources consumed by your workloads. Unfortunately, Databricks’ own usage reporting leaves much to be desired. The account usage page only shows you aggregated numbers per workspace, making it difficult to pinpoint cost drivers and optimize your spending.
Databricks released billing via system tables, which offers a more granular view into DBUs consumed per job run. But there’s a catch – you need to have Unity Catalog enabled on at least one workspace to access this feature.
Learn more about Databricks pricing with this detailed guide.
Gradient: Your single pane of glass for cloud and Databricks costs
Enter Gradient by Sync, the AI optimization powerhouse that not only saves you money but also brings unprecedented transparency to Databricks workloads. Gradient doesn’t just focus on optimizing your cloud infrastructure – it also meticulously tracks and reports Databricks costs.
In the screenshot below, Gradient’s Projects page shows a breakdown of compute spend by platform, You can see cloud vs Databricks spend, aggregated across all workloads:
Accurate DBU accounting, no unity catalog required
Gradient closely monitors your Databricks clusters, building a detailed timeline of their usage. It then uses Databricks’ own pricing model to accurately calculate the DBUs consumed by each workload run. This means you get a granular, per-run view of your Databricks costs, without the need for Unity Catalog.
Demystifying the Databricks pricing puzzle
Calculating Databricks costs is no trivial task. You need to consider a variety of factors, including:
- Cloud provider (AWS, Azure, or GCP) and region
- VM instance type
- Databricks pricing plan (Standard, Premium, or Enterprise)
- Databricks compute type (Jobs, All-Purpose, etc.)
Gradient takes care of all these complexities, crunching the numbers to give you a clear, unified view of your Databricks expenses.
Total Cost of Ownership in a single pane
But Gradient doesn’t stop there. It also tracks and aggregates your cloud infrastructure costs, providing a single, comprehensive dashboard of your total cost of ownership (TCO) for Databricks workloads.
No more juggling multiple billing portals and spreadsheets. Gradient brings all your Databricks and cloud costs together, empowering you to make data-driven decisions about resource optimization and cost management.
Conclusion
With Gradient, you gain unparalleled visibility into your Databricks spending, down to the individual workload level. This granular cost data, combined with Gradient’s powerful optimization capabilities, gives you the tools you need to take control of your cloud and Databricks budgets.
Whether you’re a data engineer seeking to maximize cluster efficiency or a manager responsible for justifying cloud expenditures, Gradient’s cost transparency features deliver the insights you need to make informed, data-driven decisions.
Say goodbye to the Databricks billing black box and hello to a future where your data processing costs are as clear as crystal. With Gradient, the path to cost optimization and profitability has never been more illuminated.
Interested in learning more? Book a demo to see how Gradient can help you reduce compute costs, meet rutnime SLAs. and regain valuable engineering time.
More from Sync:
Choosing the right Databricks cluster: Spot instances vs. on-demand clusters, All-Purpose Compute vs. Jobs Compute
Choosing the right Databricks cluster: Spot instances vs. on-demand clusters, All-Purpose Compute vs. Jobs Compute
Databricks Compute Comparison: Classic Jobs vs Serverless Jobs vs SQL Warehouses
Databricks Compute Comparison: Classic Jobs vs Serverless Jobs vs SQL Warehouses