What is Lakemine
Lakemine finds where your Databricks spend is being wasted, ranks every finding by dollar impact, and tells you how to fix it.
It installs into your own Databricks account and stays there. It reads the usage and billing information Databricks already keeps about your workspaces, works out where money is going that does not need to, and writes the findings into result tables in a catalog you choose. A dashboard in your workspace reads those tables.
One scheduled job does all of it, by default once a day at 06:00 UTC over a trailing 30-day window. Nobody has to run anything by hand.
The analysis is rule based
Every finding is produced by an explicit rule applied to your usage data, and every savings figure is arithmetic you can check. Seventeen detection engines run on each scan, each one owning a single failure mode.
There is no model in the loop. Nothing is inferred, predicted, or scored by a system you cannot inspect. When Lakemine says a cluster is costing you $2,140 a month more than it needs to, the Engines section shows you the query that found it and the arithmetic that priced it.
What it covers
One install covers every workspace attached to your account’s metastore, not just the one you install into.
Cost and billing coverage is account wide, because Databricks keeps billing data at the account level. Some of the finer telemetry is stored per region, so workspaces outside your install region get cost-only coverage. The installer tells you which ones before you deploy, so this is never a surprise you discover afterwards.
Databricks on AWS, Azure, and GCP are all supported.
What you get out of it
The findings land in two places, and they answer different questions.
The cost dashboard, in your workspace
A Lakeview dashboard named “Databricks Cost Optimization”, deployed into the hub workspace you chose. This is where you read the results. It has two pages:
- Account Overview. Rollups across the account, cost and savings by workspace, the top opportunities ranked by dollar impact, and cost broken down by tag.
- Workspace Detail. Pick one workspace and drill into it.
Access is controlled by one group membership list that you own. Everyone in the viewers group chosen at install can open the dashboard. Nobody else can. See How it works.
The Lakemine portal
app.lakemine.ai tells you whether Lakemine itself is healthy and licensed. Your installs and their last check-in, your plan and subscription, and the aggregated totals each scan reported.
The portal is not where you read your cost findings. Your findings are in the Lakeview dashboard inside your own workspace, and they never leave it. The portal exists for licensing, install health, and metering.
What leaves your environment
Your usage data does not. The scan reads the Databricks system tables in place, and the results stay in your own catalog.
There are exactly two outbound calls, both to the Lakemine portal and nowhere else.
The subscription check, before every scan. It carries no data about you or your Databricks estate. Not your email, not your workspace, not a single number from a scan. The answer is one boolean: is this subscription active and this install still registered. If the portal cannot be reached, the last successful answer stands for 7 days, so an outage on the Lakemine side never interrupts your scans.
The metering payload, after each scan. One aggregated payload, used to bill you for the product. It contains counts and totals only:
| Field | Example | Why it is sent |
|---|---|---|
schema_version | "2" | Contract version |
install_id | UUID | Identifies this deployment |
account_id | Your Lakemine billing account | Attributes usage to your portal account |
run_id | UUID | Correlates one run for support |
engine_version | "0.3.0" | Support and update tracking |
scanned_at | Timestamp | Run cadence and freshness |
workspaces_covered | 3 | Scope of analysis |
total_cost_analyzed_usd | Aggregate | Databricks spend under management |
total_dbus | Aggregate | Total DBUs over the window |
dbus_by_day[] | Per-day totals | Per-day usage |
findings_count | 42 | How much the install is finding |
total_estimated_savings_usd | Aggregate | Value delivered |
cluster_count, warehouse_count, job_count, pipeline_count | Counts | Scope |
sql_query_count | Count | Scope |
detections_run | ["A1","A3", ...] | Which rules ran, so degraded coverage is visible |
What is not in it: no raw usage rows, no query text, no table names, no cluster or job names, no user identities, no resource ids. The contract is aggregates only.
If no metering credential is configured, metering is skipped. It is best effort and never fails a scan.
What Lakemine never has
No standing access. You grant access at install, in your own environment, under your own admin identity. Lakemine holds no credentials to your account at any point. Uninstalling revokes everything.
No readable detection logic in your workspace. The analysis ships as a compiled, sourceless wheel. No detection source is synced into your environment.
No write access to your data. The scan reads the system.* schemas and writes only
to the results schema you chose. Its grants are scoped to one catalog for results and
read-only on system.*.
Next
- How it works covers the permission model and why it is built the way it is.
- Requirements lists what you need before you start.
- Quickstart takes you from download to first findings.