Skip to main content

5-Minute Tutorial

This tutorial walks you through creating a real cost estimate from scratch. By the end, you will have a complete estimate for a data platform with two workloads on AWS, ready to export.

Getting Started tutorial page The 5-Minute Tutorial — step-by-step guide to creating your first cost estimate with two workloads.

Video walkthrough​

End-to-end tutorial: create an estimate, add Jobs and DBSQL workloads, review costs, ask the AI assistant, and export to Excel.

What we are building​

A cost estimate for a mid-size data platform running on AWS us-east-1 with the Premium tier:

WorkloadPurposeConfiguration
ETL PipelineNightly batch ingestionJobs (Classic), 4 workers, runs 2x/day for 45 min
Analytics WarehouseBusiness intelligence queriesDBSQL Serverless, Small size, 10 hrs/day

Step 1: Create the estimate​

  1. Open Lakemeter in your browser. You are automatically signed in via Databricks SSO.
  2. On the home page, click New Estimate.
  3. Fill in the form:
    • Estimate Name: Q4 Data Platform - AWS
    • Cloud: AWS
    • Region: us-east-1
    • Pricing Tier: Premium
  4. Click Create.

You land on the Calculator page -- an empty estimate ready for workloads.

Creating an estimate in Lakemeter — click New Estimate, fill the form, and submit Animated: creating a new estimate — fill in name, cloud, region, and tier, then click Create.

Lakemeter calculator page with workloads and cost summary The Calculator page showing configured workloads with live cost summary and AI assistant panel on the right.

Step 2: Add the ETL Pipeline workload (Jobs)​

  1. Click Add Workload.

  2. Set Workload Type to Jobs.

  3. Set Workload Name to ETL Pipeline.

  4. Leave Serverless toggled off (we want classic compute for this example).

  5. Configure compute (in the Driver Node and Worker Nodes cards):

    • Driver Instance Type: m5d.xlarge
    • Worker Instance Type: m5d.xlarge
    • Worker Count: 4
    • Photon: Off (leave unchecked)

  6. Configure pricing (within each card):

    • Driver Pricing Tier: On-Demand (the default)
    • Worker Pricing Tier: Spot Instances (the default)

  7. Configure usage:

    • Runs Per Day: 2
    • Avg Runtime (minutes): 45
    • Days Per Month: 30 (the default is 22 business days — change it to 30 since this ETL runs every day including weekends)

  8. Click Save.

What the numbers mean: This job runs twice daily for 45 minutes, so it uses 1.5 hours/day x 30 days = 45 compute-hours/month. Each hour consumes DBUs based on the instance type (driver + 4 workers), which Lakemeter multiplies by the $/DBU rate for Jobs on AWS Premium to calculate the monthly cost. You also see VM infrastructure costs for the driver (on-demand) and workers (spot pricing).

Step 3: Add the Analytics Warehouse workload (DBSQL)​

  1. Click Add Workload again.

  2. Set Workload Type to DBSQL.

  3. Set Workload Name to Analytics Warehouse.

  4. Leave the Serverless checkbox checked (this is the default).

  5. Set Size to Small (12 DBU/hr).

  6. Set Number of Clusters to 1.

  7. Configure usage:

    • Hours Per Month: 220 (roughly 10 hrs/day x 22 business days)

  8. Click Save.

What the numbers mean: A Small Serverless warehouse consumes 12 DBU per hour. At 220 hours/month, that is 2,640 DBUs/month. Lakemeter multiplies this by the Serverless SQL $/DBU rate for your cloud, region, and tier. Serverless workloads have no separate VM costs -- infrastructure is included in the DBU price.

Step 4: Review costs​

Back on the Calculator page, you can see:

  • Each workload's monthly cost displayed on its card or row
  • Total estimate cost summed at the top
  • DBU breakdown showing how many Databricks Units each workload consumes
tip

Click on a workload to expand or edit it. Costs recalculate instantly when you change any parameter -- try adjusting the number of workers or warehouse size to see the impact.

Estimate with multiple workloads configured An estimate with multiple workloads showing individual costs and the total cost summary.

Step 5: Export to Excel​

  1. Click the Export button (download icon) at the top of the calculator page.
  2. An Excel file downloads named something like Databricks_Estimate_Q4_Data_Platform_AWS_20260404.xlsx.

The spreadsheet includes:

  • Header section with your estimate details (cloud, region, tier)
  • Workload table with every configuration field, DBU rates, and costs per workload
  • Summary section with total monthly cost and DBU breakdown by SKU type
  • Assumptions and notes explaining the pricing basis

This file is ready to attach to an RFP response, share in a planning meeting, or use for internal budgeting.

What to try next​

  • Add more workloads -- try Model Serving or FMAPI to estimate AI/ML costs. See the Quick Reference for a summary of all 14 workload types.
  • Use the AI Assistant -- click the chat icon on the right side and describe what you need. For example: "Add a DLT pipeline for real-time streaming with Pro edition." The assistant proposes workload configurations you can accept or modify. See the AI Assistant guide.
  • Duplicate and compare -- duplicate your estimate, change the region or tier, and compare costs side by side.
  • Follow the full workflow -- see the End-to-End Workflow guide for a complete walkthrough from creation through export interpretation.