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LakeSentry icon

LakeSentry

Databricks cost intelligence platform that catches cost anomalies, flags waste and surfaces optimization insights with safe, auditable actions.

Cost intelligence dashboard: total spend, forecast and top cost drivers

Cost / License

  • Freemium (Subscription)
  • Proprietary

Platforms

  • Online
  • Software as a Service (SaaS)
Insights: anomalies, waste and optimizations ranked by estimated savings
Cost Explorer: spend breakdown by resource type, DBU and cloud cost
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LakeSentry information

  • Developed by

    Dark Lake

  • Licensing

    Proprietary and Freemium product.

  • Pricing

    Subscription ranging between $499 and $849 per month + free version with limited functionality.

  • Alternatives

    3 alternatives listed

  • Supported Languages

    • English
LakeSentry was added to AlternativeTo by oxana-astapova on and this page was last updated .
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What is LakeSentry?

LakeSentry is a cost intelligence platform for Databricks. It connects through a read-only service principal, ingests Databricks system tables and API metadata, and builds a normalized cost ledger across all workspaces.

On top of that ledger it detects cost anomalies with statistical evidence, identifies waste such as idle SQL warehouses and orphaned clusters, and ranks optimization opportunities by estimated savings. Every insight carries evidence and a one-click path to action: teams approve or dismiss changes with a full audit trail, or enable autopilot for selected safe actions with guardrails.

The platform also includes a cost explorer with breakdowns by jobs, SQL warehouses, compute types and teams, plus budgets, commitment tracking, 30-day forecasts, and cost attribution with tag governance. LakeSentry never accesses business data, notebooks or query results, only usage and cost metadata.

There is a free plan with unlimited workspaces; paid plans add more users, longer history and full insight details. Built for platform engineering, FinOps and data leadership teams running Databricks at scale.

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