End-to-End Workflow
This guide covers the complete Lakemeter workflow from creating an estimate to interpreting the exported Excel report. Follow this when you need to produce a cost estimate for a customer proposal, internal planning exercise, or vendor comparison.
Video walkthrough
Full walkthrough: create an estimate, add workloads, configure compute and usage, review costs, and export the report.
Overview
Create Estimate → Add Workloads → Configure Each Workload → Review Costs → Export Excel → Interpret Report
Start from the Lakemeter home page — click New Estimate to begin building a cost estimate.
1. Plan your estimate
Before opening Lakemeter, decide:
- Cloud provider -- AWS, Azure, or GCP. This determines available regions, instance types, and pricing.
- Region -- Where the Databricks workspace will run. Pricing varies by region.
- Pricing tier -- Standard, Premium, or Enterprise. This affects DBU rates and which workload types are available.
| Tier | Available Workloads | Typical Use |
|---|---|---|
| Standard | Jobs, All-Purpose, DLT, DBSQL (Classic, Pro) | Basic data engineering |
| Premium | All Standard + DBSQL Serverless, Model Serving, Vector Search, FMAPI, Lakebase | Most production deployments |
| Enterprise | Same as Premium with enhanced SLAs | Large-scale, regulated workloads |
Once you add workloads to an estimate, you cannot change its cloud provider. Choose carefully, or create separate estimates for multi-cloud comparisons.
2. Create the estimate
- Click New Estimate from the home page.
- Enter a descriptive name (e.g., "Acme Corp - AWS us-east-1 Premium").
- Select your cloud, region, and tier.
- Click Create.
You are taken to the Calculator page.
3. Add and configure workloads
Click Add Workload for each Databricks service in your architecture. For each workload:
- Choose the workload type. See the Quick Reference if you are unsure which type to use.
- Name it descriptively. Use names like "Nightly ETL Pipeline" or "Analyst SQL Warehouse" -- not "Workload 1".
- Configure compute. For classic workloads (Jobs, All-Purpose, DLT), select instance types and worker count. For serverless or managed services (DBSQL Serverless, Model Serving, Vector Search), choose the size or capacity tier.
- Set usage patterns. Enter how much the workload runs:
- Jobs: runs per day, average runtime in minutes, days per month
- All-Purpose: hours per month
- DBSQL: hours per month
- Always-on services (Model Serving, Vector Search): default is 730 hrs/month (24/7)
- FMAPI: token quantity in millions
- Choose pricing options. For classic compute, pick between on-demand, spot, or reserved (1-year/3-year) pricing for driver and worker nodes.
- Add notes (optional). Use the notes field to document why you chose a particular configuration -- useful when reviewing the estimate later or sharing with others.
Costs update in real-time as you adjust parameters.
Use the AI Assistant to speed up configuration. Open the chat panel and describe your workload: "I need a DLT pipeline processing 500GB daily with Pro edition." The assistant proposes a workload configuration you can accept, modify, or reject.
4. Review the cost breakdown
The Calculator page displays:
- Per-workload costs -- Monthly cost for each workload, broken down into DBU costs and VM infrastructure costs (where applicable).
- Total estimate -- Sum of all workloads displayed at the top.
- DBU consumption -- Total Databricks Units consumed per month by each workload.
Understanding the cost components:
| Component | Applies to | What it is |
|---|---|---|
| DBU cost | All workloads | Databricks Units consumed x $/DBU rate. The rate depends on your cloud, region, tier, and workload type. |
| VM cost | Classic compute only (Jobs, All-Purpose, DLT, DBSQL Classic/Pro) | Cloud infrastructure cost for driver and worker VMs. Not applicable to serverless workloads. |
| Token cost | FMAPI workloads | Cost per million input/output tokens for foundation model API calls. |
| Storage cost | Lakebase, Vector Search | Storage capacity costs beyond the compute/DBU component. |
Quick cost optimization checks
- Spot pricing on workers can reduce VM costs by 60-90% for fault-tolerant Jobs workloads.
- Reserved instances (1yr or 3yr) lower VM costs for always-on workloads like DBSQL warehouses.
- Serverless eliminates VM costs entirely -- the infrastructure cost is bundled into a higher DBU rate. Compare total cost, not just DBU rate.
- Photon doubles the DBU rate but often halves the runtime for compatible workloads. Check whether the faster execution offsets the higher per-hour cost.
5. Export to Excel
- Click the Export button (download icon) at the top of the Calculator page.
- The file downloads as
Databricks_Estimate_{name}_{date}.xlsx.
You can also export all your estimates at once from the home page using the bulk export option.
6. Interpret the Excel report
The exported spreadsheet contains several sections:
Header
The estimate name, cloud provider, region, pricing tier, status, version, and timestamps.
Workload table
One row per workload with columns for:
| Column | Description |
|---|---|
| Workload Name | The name you assigned |
| Type | Workload type (Jobs, DBSQL, etc.) |
| Config | Key configuration summary (instance types, warehouse size, etc.) |
| SKU | The specific Databricks SKU used for pricing |
| Driver / Workers | Instance types and worker count |
| Hours/Month | Total compute hours |
| Token Type / Qty | For FMAPI: input/output tokens in millions |
| DBU/hr | Databricks Units consumed per hour |
| Total DBUs/Month | DBU/hr x hours/month |
| $/DBU | Price per Databricks Unit |
| VM $/hr | Combined VM cost per hour (driver + workers) |
| Total Cost | Monthly cost for this workload |
| Notes | Configuration notes and any pricing warnings |
Summary section
- Total monthly cost across all workloads
- DBU breakdown by SKU type (e.g., JOBS_COMPUTE, SERVERLESS_SQL_COMPUTE)
- Assumptions explaining the pricing basis and disclaimers
How to use the report
| Use case | What to focus on |
|---|---|
| Customer proposal / RFP | Total cost, workload table, assumptions section |
| Internal budget planning | Total cost, DBU breakdown for chargeback allocation |
| Vendor comparison | Duplicate the estimate for each cloud/region, export both, compare total costs |
| Architecture review | Workload table configuration details, notes column |
7. Iterate and refine
Estimates are living documents. Common iteration patterns:
- Duplicate the estimate to create a "what-if" scenario (e.g., "What if we use serverless instead of classic?")
- Adjust usage patterns as you learn more about actual workload behavior
- Add workloads as the project scope grows
- Change pricing options to model the impact of reserved capacity commitments
- Re-export after changes to get an updated report
Each save increments the estimate's version number, so you can track how the estimate evolved over time.