Cost & Performance Optimization intro

Most enterprises face these problems:

• Lack of contextual visibility: Raw DBU counts don’t explain why workloads consumed resources.

• Wasted compute: Orphan clusters, misconfigured autoscaling, idle jobs, inefficient code.

• Unpredictable monthly bills: No forecasting or early warnings based on historical patterns.

• Siloed teams: Data engineers, product teams, and finance lack a single version of the truth.

• Manual investigations: Teams spend days analyzing logs to understand spikes or anomalies.

The result is: Higher cloud bills, inconsistent performance, and no shared accountability.

Open

Lakehouse Optimizer (LHO) is a cost governance and optimization platform purpose-built for Databricks. It captures compute usage at a high frequency, models workload behavior, and provides actionable recommendations with business context.

Open

Core LHO Capabilities

• High-resolution compute telemetry (every 5–10 seconds)

• Contextual KPIs (cost per TB, cost per task, data IO, CPU/Memory saturation, etc.)

• Optimization insights (“why did this cost change?”, “what changed in job behavior?”)

• Anomaly detection for cost, performance, and usage patterns

• Forecast modeling for consumption planning (commit utilization, spike prediction)

• Usage distribution analysis (percentile-based CPU & memory patterns, not time-sorted noise)

• Cross-workspace visibility (multi-region, multi-team)

• Governance automation (policies, alerts, incident rules with overrides)

LHO turns Databricks telemetry into meaningful intelligence and governance automation.

Open

Forecast Spend and Commit Utilization

With real consumption patterns and workload behavior, LHO provides:

• Predictive monthly cost modeling

• Workspace-level and workload-level forecast curves

• Commit vs. actual consumption tracking

• Alerts for early signs of commit overrun or under-utilization

Finance and platform teams gain predictable, stable cost planning.

Open