Where the spend went, and what it bought
Cut the Databricks bill without cutting what it does
We find the Databricks spend that is not buying anything: all-purpose compute running jobs that should be serverless, clusters sized for a load that never arrived, schedules nobody owns, and warehouses left running overnight. You get findings ranked by severity with the estimated saving and the reasoning behind each one.
The bill grows faster than the work and nobody can say which part is the problem, because a cost dashboard tells you what you spent rather than which of it bought nothing. That judgement is the deliverable.
- Who it is for
- Data platform leads whose Databricks bill is outgrowing the work
- Bench
- 115+ engineers, 80 Databricks-certified
Where the money usually is
- All-purpose compute running scheduled work that belongs on jobs or serverless
- Cluster and warehouse sizing against what the workload actually needs
- Job failures and retries, which are paid for twice and rarely counted
- Schedules and compute that outlived whoever created them
- Warehouses with no auto-stop, which is the quietest line on the bill
- Model serving and AI Gateway spend, where they are in use
What you get
- Findings ranked by severity, each with an estimated saving and the reasoning shown
- A thirty, sixty and ninety day order, written so your own team can act on it
- The changes that are safe to make immediately, separated from the ones needing a change window
- A fixed-scope proposal for the remediation, if you would rather we did it
What makes it faster
The audit is the scorecard. Where you want us to stay and hold the savings, Radar is what we use: declared SLOs per workload, and every restart or reroute written down as a governed action rather than vanishing into somebody's terminal history.
About Fabric RadarWhere this goes next
FAQ
Databricks cost optimization: common questions
How do you reduce Databricks costs?
By finding the spend that bought nothing rather than by turning things down. Usually that is scheduled work on all-purpose compute, clusters sized for a peak that never came, warehouses without auto-stop, retries nobody counted, and schedules whose owner left. Each finding comes with an estimated saving and the reasoning, so you can disagree with it.
Will this slow our workloads down?
That is the constraint the work runs under. Findings are split into what is safe immediately and what needs a change window, and anything with a performance trade-off is written up as a trade-off rather than a saving. A recommendation that makes the platform worse is not a saving.
What access do you need?
Read access to the workspace and to system tables where you have them. No production data and no write access. If your security process wants a named scope written before anything is granted, we will write one.
How is this different from our cost dashboard?
A dashboard reports spend. This says which of it bought nothing, why, and what to change, which is judgement rather than a billing export. It comes from engineers who have run these platforms and recognise the patterns.