Lakemine is a Databricks cost optimization platform that runs inside your workspace, detects waste across 17+ engines, and ranks findings by estimated savings.
Lakemine is a Databricks cost optimization platform that runs inside a customer's own workspace, analyzes consumption and configuration data every day, and returns a ranked list of fixes, most with an estimated saving attached. It targets the layer where Databricks spend actually accumulates: clusters, jobs, SQL warehouses, pipelines, and runtime configuration, and ties every finding back to the workload that generated it.
Last updated July 2026.
Why Lakemine Exists
Most teams already run some combination of Databricks system tables, cloud cost dashboards, infrastructure monitoring, and internal scripts to track spend. Each of these answers part of the cost question and then hands the rest back to an engineer. Dashboards report what happened. Cost platforms show spend by account and workspace without the workload context that explains it. Infrastructure monitoring shows a healthy host even while spill, idle compute, and runtime drift quietly inflate the bill. Lakemine is built to close that gap: it diagnoses the cause of a cost increase automatically and returns a ranked fix with an estimated saving, rather than a report that still requires an engineer to investigate.
How Lakemine Works
Lakemine deploys inside a customer's Databricks account and reads system tables, configuration, and workload telemetry that Databricks already produces. Nothing is copied out of the environment, and no agent inspects customer data, notebooks, or query results directly. The inputs are operational metadata: how compute was configured, what ran, how long it took, and what it consumed.
The platform runs on a four-stage cycle:
- Detect. Read consumption, configuration, and telemetry from the connected workspace.
- Diagnose. Evaluate the evidence against 17 or more optimization engines, each responsible for one specific failure mode, to determine the cause of a cost pattern rather than just flag that a number moved.
- Prioritize. Rank findings by estimated saving where one applies, so a team with limited hours knows which fix pays the most.
- Apply. Route findings to the team that owns the affected workload, with supporting evidence attached, so the fix lands with the people who can act on it.
This cycle runs daily rather than as a one-time engagement, which matters because Databricks environments change continuously. New teams onboard, pipelines are added, and query patterns shift, so a configuration that is efficient this month can drift out of alignment by next month without ongoing review.
What Lakemine's Engines Cover
Lakemine's optimization engines are organized into two categories, cost optimization and workload efficiency and reliability, with 17 shipped today and more in active development:
| Category | Engine count | Example coverage |
|---|---|---|
| Cost optimization | 14 | Job compute type mismatches, idle and oversized clusters, disabled autoscaling, spot instance eligibility, bursty SQL warehouses, outdated runtimes, Photon eligibility, always-on pipelines |
| Workload efficiency and reliability | 3 | Job failure and retry rates, small-job sprawl, SQL warehouse query spill |
Each engine owns a single failure mode and evaluates it continuously, covering SQL warehouses, all-purpose and job clusters, streaming pipelines, serverless compute, Photon eligibility, autoscaling, and spot instances.
Savings Reconciled Against the Real Bill
Most Lakemine findings carry an estimated saving at the time they're surfaced. A small number of findings, covering budget alerting and resource tagging, surface visibility and accountability gaps rather than a direct dollar saving, and are reported as such rather than assigned a figure they don't have. Once acted on, savings estimates are reconciled against the customer's actual Databricks invoice rather than left as a standalone projection. Organizations without recent cost review commonly see reductions in the 20% to 35% range, though the specific figure depends on how tuned the environment already was going in.
Two Ways to Buy
Lakemine offers two commercial models, built around how a customer's finance team plans spend:
- Pay as You Save. No fee up front. Billing is calculated as a percentage of audited savings, verified against the customer's actual invoice, and invoiced monthly.
- Pay per DBU. A flat licence fee scaled to active DBU consumption, invoiced on a recurring schedule, built for teams that need predictable, budget-based pricing rather than a variable fee.
Where Lakemine Fits in an Enterprise FinOps Stack
Lakemine is built to extend an existing FinOps stack rather than replace it. It connects natively to platforms such as IBM Apptio Cloudability, tagging and allocating metrics before they reach finance, so cost data generated inside the Databricks workspace flows through to the same tools finance already uses for chargeback and reporting.
Frequently Asked Questions
- What does Lakemine do?
- Lakemine is a Databricks cost optimization platform. It reads consumption and configuration data from a customer's Databricks workspace, identifies sources of wasted spend using 17 or more purpose-built engines, and returns a ranked, actionable fix for each finding with an estimated saving attached.
- Does Lakemine require customer data to leave the workspace?
- No. Lakemine's execution stays inside the customer's Databricks account. Customer data, notebooks, SQL, and metadata do not leave the environment, and Private Link is supported where network policy requires it.
- How is Lakemine different from a cloud cost dashboard?
- A cost dashboard typically shows spend by account or workspace without workload-level context. Lakemine ties every dollar to the specific cluster, job, or query that generated it, then supplies a ranked fix rather than a report an engineer still has to interpret.
- How does Lakemine pricing work?
- Lakemine offers two models. Pay as You Save charges no fee up front and bills monthly against audited savings verified on the customer's invoice. Pay per DBU is a flat licence scaled to active DBU consumption for predictable budgeting.
See how Lakemine works inside your own Databricks environment.