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Databricks

Control Data Platform Spend

Databricks is a powerful unified data and AI platfor, but its consumption-based pricing model makes cost governance extremely difficult. DBU rates vary by workload type, cluster configurations change frequently, and job-level cost attribution is rarely available out of the box. If deployed in the cloud, the associated infrastructure and consumption costs can scale rapidly as usage grows.

For FinOps and data engineering leaders, this means rising costs, limited accountability, and reactive optimisation.

Data platform spend is opaque, unpredictable, and growing.

What teams can achieve with Andromeda on Databricks

01

Reduce Databricks waste by identifying idle clusters and over-provisioned jobs

02

Allocate data platform costs accurately across engineering teams and projects

03

Surface the highest-cost Databricks workloads for targeted optimization

04

Give finance teams a reconcilable view of Databricks spend against budgets

05

Enable data teams to make cost-aware decisions without slowing delivery

06

Allocate Databricks costs by workspace, team, or project using tag-based rules

Databricks Use Cases & Value

How AquilaClouds Andromeda™ supports Databricks

Databricks spend visibility and breakdown

Andromeda ingests Databricks billing data and usage logs to provide a granular cost breakdown by cluster, job, user, workspace, and team — going far beyond the native billing UI.

Andromeda FinOps monitors Databricks Unit (DBU) consumption in real time, flagging unexpected spikes, idle clusters, and workloads running outside approved cost windows.

Andromeda FinOps applies tagging-based and rules-based allocation logic to attribute Databricks costs to internal cost centres, enabling accurate chargeback or showback for data platform consumers.

Andromeda FinOps identifies clusters that are running without active jobs, auto-scaling beyond optimal configurations, or using premium DBU tiers where standard tiers would suffice.

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