Workload Sizing Guides
Use this catalog to choose the Lakemeter form that matches the consumption you need to estimate. Each linked guide is the canonical source for that workload's sizing inputs, calculation behavior, and Excel export.
For current Databricks product capabilities and availability, refer to the official Databricks documentation. These guides intentionally focus on how Lakemeter models cost.
Choose a workload type, enter its sizing assumptions, and save it to the estimate.
Compute and SQL
| What you need to size | Lakemeter guide | Main sizing inputs |
|---|---|---|
| Scheduled or triggered processing | Lakeflow Jobs | Compute shape, workers, runs, runtime |
| Interactive notebook compute | All-Purpose Compute | Compute shape, workers, active hours |
| Declarative data pipelines | Lakeflow Spark Declarative Pipelines | Compute mode, edition, workers, usage |
| Managed connector pipelines through the API | Lakeflow Connect API | Pipeline usage, edition, optional gateway |
| SQL warehouses | Databricks SQL | Warehouse type, size, clusters, hours |
AI, ML, and data services
| What you need to size | Lakemeter guide | Main sizing inputs |
|---|---|---|
| Model inference endpoints | Model Serving | Endpoint type, scale-out concurrency, hours |
| Vector indexing and search | AI Search | Endpoint mode, vector capacity, storage, reranker requests, hours |
| Databricks-hosted foundation models | Foundation Models — Databricks | Model, rate type, token volume or hours |
| Proprietary foundation models | Foundation Models — Proprietary | Provider, model, geography, context, token volume |
| Transactional database capacity | Lakebase | Compute range, usage, nodes, storage protection |
| Databricks-hosted applications | Databricks Apps | App size, app count, active hours |
| Document parsing | AI Parse | Estimation mode, document complexity, page volume |
| Structured field extraction | AI Extract | Document type, monthly input volume |
| Document classification | AI Classify | Document type, monthly document volume |
| Gateway inference tables and usage tracking | Unity AI Gateway | Components enabled, input method, request or payload volume |
| Agent evaluation service usage | Agent Evaluation | Features enabled, evaluation token volume, synthetic questions |
| Serverless GPU model training | AI Runtime | Accelerator, monthly runtime |
| Image generation | Shutterstock ImageAI | Monthly image volume |
Ingestion, storage, and platform
| What you need to size | Lakemeter guide | Main sizing inputs |
|---|---|---|
| Direct or OpenTelemetry ingestion | Zerobus Ingest | Mode, monthly ingested GB |
| Databricks-managed default storage | Databricks Default Storage | Stored data, Tier 1 operations, Tier 2 operations |
| Security and mission-critical packages | Platform Add-ons | Add-on selection, cloud, tier, Product Spend at List |
Shared sizing principles
Name the assumption
Use a workload name that identifies the scenario being modeled, not only the product. For example, Nightly customer ingestion is easier to review than Jobs workload.
Model expected usage
Lakemeter may ask for run frequency and duration, active hours, capacity, storage, tokens, pages, or images. Use representative usage rather than maximum technical limits unless the estimate is intentionally modeling a peak case.
Use the options shown in the app
Available clouds, regions, tiers, sizes, SKUs, and models can change. The current Lakemeter controls and pricing data determine what can be estimated. Check the Databricks pricing page and your commercial terms before using an estimate for a final purchasing decision.
Review the breakdown
Expand each workload after saving it. Confirm the usage quantity, billing unit, selected SKU, rate, and any separate VM or storage components before exporting.
Related tools
- SKU Explorer — inspect SKU list rates and model simple volume-based costs
- FMAPI Tokens — compare proprietary model token-rate combinations
- Calculation Reference — understand the calculation structure shared across workloads
- Exporting to Excel — understand how sizing assumptions appear in the exported workbook