Use Case

Databricks Cost Optimization

Job clusters are typically the largest line on a Databricks bill. Altimate's Auto Tune agents handle Databricks cost optimization by right-sizing them automatically, along with your SQL warehouses and compute. Every change is checked, approved by you, and rolls back automatically if anything slows down.

A co-pilot identifies savings opportunities and routes them to the right owner to approve.

20–35%
typical cost reduction
100+
saving opportunities surfaced
<30
days to proven value
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01 · Auto Tune

Autonomous Compute Savings, SLA Safe

Auto Tune reads how your job clusters, SQL warehouses, and all-purpose clusters actually run, then moves each to a cheaper setup on its own. The waste creeps in through default cluster templates that nobody revisits, and at hundreds or thousands of jobs, nobody can revisit them by hand. Auto Tune revisits them continuously. Approve it once; it applies between runs, monitors continuously, and rolls back the moment anything slows down.

Jobs, warehouses, and all-purpose compute
Saves both DBU and cloud costs
SLA-safe, approval-gated, auto-reverts

Every automated change runs the same loop. No exceptions, no surprises.

ROLLBACKANALYZERECOMMENDGATEAPPLYWATCH
01

Approval-gated — you choose the jobs, warehouses, and clusters it may touch.

02

Policy-checked — changes clear your SLA rules before they apply.

03

Atomic — one snapshot, one change, applied only between runs.

04

Watched — continuous monitoring against the prior baseline.

05

Reversible — failure or degradation triggers automatic rollback.

Watch a real pass: analysis, gate checks, the applied change, the watch window that follows, and the audit trail every event lands in.

Get Serverless Efficiency at Classic Compute Pricing.

Serverless is often pitched as a better solution where compute requirements are unpredictable, but it trades over-provisioning for a premium rate under Databricks pricing. Altimate's Auto Tune for Databricks fixes the sizing directly, on the classic compute you already own, and shows you which workloads are genuinely ad hoc enough to run on serverless. Already running serverless SQL warehouses? Auto Tune tunes those too.

Right-size job clusters, SQL warehouses, and all-purpose compute in place. Typical result: 20–35% lower spend.
See which workloads are stable enough to stay on classic compute and which are ad hoc enough to justify serverless.
Serverless SQL warehouses get auto-tuned as well, so the savings follow you either way.
02 · Spark & job intelligence

Spark Waste, Found and Fixed

Slow shuffles, oversized executors, steps that run one after another when they could run together. Altimate reads each run, prices it, and recommends the fix — so a job creeping up on cost is caught weeks before month-end, with a concrete change already in hand.

Finds hidden Spark waste
Alerts when cost or runtime jumps
Recommends the fix, not just the flag
A Spark run flagged with a 1.0 GB spill and a 6.4x skew ratio, above three recommendations: eliminate the disk spill in the sort-merge join, reduce data skew on the join key, and cut GC pressure in the hash aggregate — each with its cost and time savings
Altimate reads each Spark run — here flagging a 1.0 GB spill and a 6.4× skew — then hands back concrete fixes: eliminate the disk spill, rebalance the skewed join key, ease GC pressure. Each with the cost and time it saves.
03 · AI/ML spend intelligence

The Fastest-Growing Line on the Bill Is the Least Watched.

Model serving, vector search, AI gateway, and Genie — the fastest-growing spend on the platform. Altimate attributes every dollar by service, team, and day, and flags endpoints serving nobody.

AI/ML Services cost dashboard showing Model Serving, Vector Search, and AI Gateway spend over 28 days
Every dollar of AI/ML spend attributed by service and by day across all workspaces. Teams finally know where the budget is going, instead of stitching it together from billing exports after the fact.

3 idle model-serving endpoints. 30 days. Zero requests. $19,800/month in invisible waste.

We flagged it. They fixed it. $240K saved annually. Zero disruption.

Opportunity card recommending decommissioning three idle model serving endpoints for roughly twenty thousand dollars per month
$20K/month. Low effort. First detected 5 days ago. Altimate doesn't wait for a quarterly review to find invisible spend. It surfaces the opportunity, sizes the saving, and hands it to the right engineer to action.
04 · Discover

Every Opportunity Sized, Owned, and Tracked to Done.

Spot-instance misconfigurations, wrong instance families, notebooks parked on all-purpose clusters, resources idling for days. A co-pilot does the heavy lifting, turning each into an opportunity with a dollar value, an effort score, and a name next to it.

Links straight to the resource
Filter, group, and export
Tracked until it's done
Discover page listing Databricks cost opportunities with money savings, time savings, effort, status, and owner columns
The Discover queue: each row carries money savings, time savings, effort, status, and an owner.

From finding to fix: how an opportunity gets explained, assigned, and closed.

05 · Chargeback & forecast

Know Which Team Spent What. See Next Quarter Early.

Spend split by team first, then by workspace, SKU, product, and user, and projected forward. Each SKU carries its own rate in the Databricks pricing model, so the split shows which rates drive the bill. Every dollar rolls up to an owning team, so budget conversations happen before the invoice arrives, not after.

Spend by team and user
Broken down by SKU and product
See this quarter and next
Breakdown dashboard: total cost of $603K across six workspaces, with cost broken down by product — SQL, Jobs, all-purpose, model serving, DLT, and vector search — each with DBUs and percent of total
Total spend split by workspace and by product — SQL, Jobs, all-purpose, model serving, and the rest — each ranked by share of the bill.
Users dashboard ranking the top Databricks users and service principals by query cost, with DBUs, query counts, success rate, and average duration per user
Top users and service principals ranked by query cost, with DBUs, query volume, and success rate. Chargeback stops being a spreadsheet exercise.
Team sport

Cost Optimization Is a Team Sport.

Autonomous savings and the opportunities your team closes land on one Summary page — measured in dollars and engineer-hours, each with an owner. Cost optimization stops being only the platform team's job.

AUTONOMOUS

Auto Tune agents apply the change themselves. Gated, monitored, reversible.

ASSISTED

A co-pilot does the heavy lifting — sizing each opportunity in dollars, scoring the effort, and naming an owner. Your team gives the final go-ahead.

Altimate Summary page showing autonomous and assisted savings totals with time savings for a Databricks account
The Summary page ledger: autonomous and assisted savings tracked side by side, with time savings in engineer-hours.
FAQ

Questions, Answered.

SOC 2 Type II certifiedMetadata-only analysisScoped service principalNo training on your dataPer-account pricing, no per-seat fee

"Significant money savings on warehouses that were already optimized by us."

Aniket, VP of Data, ThredUp

Ready to Cut Your Databricks Bill?

Run a 30-day proof of value. Watch the savings land on your own Summary page.

Also running Snowflake? See our Snowflake cost optimization overview.