Cloud data cost optimization
Your platform bill grew faster than the data did.
We baseline spend by workload, remove what nothing needs, and install guardrails so the savings survive the next quarter.
The problem
What we usually find
Spend is visible only as one monthly total
Finance sees the invoice; nobody can say which team, pipeline or query drove the increase.
Compute sized for the worst day, every day
Warehouses and clusters run at peak size around the clock, and idle compute keeps billing between jobs.
Full refreshes where incremental would do
Large tables are rebuilt from scratch every night because incremental logic was never written.
Our approach
Four steps, each with a deliverable
- 01
Cost baseline
We tag and attribute spend to teams, pipelines and queries, and agree a baseline with finance.
- 02
Find the waste
Idle compute, oversized warehouses, unused tables, redundant pipelines and expensive queries are ranked by saving.
- 03
Fix in priority order
Right-sizing, auto-suspend, incremental processing and query rewrites are shipped with before-and-after measurement.
- 04
Guardrails
Budgets, alerts and policies stop new waste. Monthly reports show spend against baseline.
Reference architecture
Deliverables
What you receive
- Cost attribution by team, pipeline and query
- Ranked savings backlog with estimated value
- Implemented fixes with measured before-and-after spend
- Budgets, alerts and cost policies
- Monthly spend report template for finance
Typical timeline
How the work runs
Baseline1–2 weeks
Attribution, baseline and savings backlog.
Quick wins2–3 weeks
Idle compute, auto-suspend and right-sizing.
Structural fixes4–8 weeks
Incremental pipelines and query rewrites.
Guardrails1–2 weeks
Budgets, alerts and reporting handed to your team.
Frequently asked questions
How much can we expect to save?
We won't promise a number before measuring. The baseline phase produces a ranked backlog with estimated savings per item, and you decide what to implement.
Will cost cuts slow our pipelines down?
Every change is measured for runtime and freshness as well as cost. Changes that break an SLA are not shipped.
Can the fee be tied to savings?
For larger programs we can discuss fee structures linked to measured savings against the agreed baseline. We agree the structure in writing before work starts.
Which platforms do you optimize?
Databricks, Snowflake, Microsoft Fabric and the underlying cloud storage and networking on AWS, Azure and Google Cloud.
How do savings stay in place?
Budgets, alerts and policies are configured on the platform, and monthly reports compare spend to baseline so regressions are caught early.
Have 30 minutes? Get a senior architect’s view of your data platform.
No sales deck. You talk to the engineer who would lead the work.
Book an architecture review