DatabricksDBU burn, flagged before finance sees it.
Workspace and SKU detail without touching system tables, plus the forecast and spike alerts the account console leaves to you.
- 5 min
- setup, per provider
- 90 days
- history, instantly
- Same-day
- anomaly alerts
- Read-only access
- 14-day free trial
- No credit card required
Daily Spend by Provider
$24,321 total
What is StackSpend for Databricks?
StackSpend connects to the Databricks billing system tables (system.billing.usage and list_prices) through a SQL warehouse with a read-only service principal. Track account-wide DBU spend by workspace, SKU, and product — jobs, all-purpose and serverless compute, SQL warehouses, DLT, and model serving — alongside AWS, GCP, Azure, Snowflake, and AI providers. StackSpend connects AWS, GCP (Google Cloud), Azure, Vercel, OpenAI, Anthropic, Claude, Cursor, GitHub, Hugging Face, Twilio, Grok (xAI), Snowflake, and ClickHouse Cloud.
Why is Databricks spend hard to control?
01
Databricks spend moves with jobs, cluster sizing, SQL warehouse uptime, and model serving, and the bill is usage-based in DBUs. Most teams only look after spend has accumulated.
02
DBU totals alone do not say what changed. Usage needs to be broken down by workspace, SKU, and product to see whether a job cluster, an always-on warehouse, or model serving drove the increase.
03
Databricks usually sits beside cloud, data, and AI spend. Reviewing it separately hides the real infrastructure total — especially when the same workloads also drive AWS, Azure, or GCP compute charges.
What does StackSpend show for Databricks?
Read-only access, with nothing to install.
Every integration reads billing and usage APIs with the least privilege the provider allows. No agents, no write scopes, no infrastructure changes, and you can tell your security reviewer exactly what was granted.
How it worksEvery dollar has an owner.
Auto-tagging rules label costs as they are ingested, matching provider, account, service and project patterns in priority order. By the time someone asks who owns the spend, the answer is already on the data — filterable and groupable in the explorer.
Team plan and above
How it worksSee this running against your own Databricks bill by tomorrow morning.
Read-only · 5 minutes per provider
Who should use StackSpend for Databricks?
- Teams that want daily visibility into spend without manually checking billing portals.
- Buyers replacing spreadsheets and fragmented native dashboards with one monitoring workflow.
- Operators who need read-only setup, alerts, and forecasting before overrun becomes month-end reality.
Evaluation checklist
- 01Start a trialOpen a StackSpend workspace with no credit card required.
- 02Connect with read-only accessUse the setup guide to connect the provider or workflow with the minimum permissions needed.
- 03Review the first 90 daysCheck history, alerts, anomalies, and forecast so you can decide whether the workflow is worth adopting.
What's included?
- StackSpend reads account-wide usage from system.billing.usage joined to USD list prices, using a read-only service principal and one small SQL warehouse.
- Jobs, all-purpose and serverless compute, SQL warehouses, DLT pipelines, and model serving appear in the same monitoring workflow as the rest of your providers, per workspace and SKU.
- Daily Slack or email signals, budget thresholds, anomaly detection, and pace-to-forecast make Databricks spend visible before the invoice closes.
Exactly what we track
- Databricks billing system tables
- system.billing.usage and list_prices
- Account-wide DBU usage at USD list price
- Cost by workspace, SKU, and product
- Jobs, all-purpose, and serverless compute
- SQL warehouses, DLT, and model serving
- Budget thresholds and anomaly detection
- Forecasting
What causes Databricks costs to spike?
- A scheduled job runs on an oversized or lingering cluster after a workload change
- A SQL warehouse stays running between queries instead of auto-stopping
- DLT pipelines or streaming workloads process more data after a new source lands
- Model serving or vector search endpoints stay provisioned after an experiment ends
Why do teams move beyond native Databricks billing?
Databricks account console and usage dashboards is built for investigation. StackSpend is built for prevention.
Databricks account console and usage dashboards
- Usage dashboards are strong for investigation but still require someone to check them
- System-table rows need to be queried and interpreted before they become a daily workflow
- No unified view with AWS, GCP, Azure, Snowflake, or AI provider spend
- Budget pacing and anomaly response require a separate monitoring layer
StackSpend
- Daily Databricks cost signal delivered beside the rest of your cloud and AI stack
- DBU usage priced in USD and broken down by workspace, SKU, and product
- Anomaly detection catches job, warehouse, and serving spikes as they happen
- Forecasting and budget thresholds show month-end exposure before invoice time
Native billing shows you last month. StackSpend tells you tomorrow.
Read-only credentials, 90 days of Databricks cost history loaded automatically, and a daily signal from day one.
Read-only access · Flat plans, never a % of your bill · No credit card required
What do you get when you connect Databricks?
- Setup time
- Most teams can connect and validate setup in about 5-10 minutes.
- Access model
- Read-only credentials only. StackSpend does not modify provider resources or billing settings.
- Signals
- Daily Slack or email updates, anomaly alerts, and budget tracking in one workflow.
- History and forecast
- Historical spend context plus pace-to-forecast so overruns are visible before month-end.
Databricks cost monitoring, answered
Why do engineering-led teams use StackSpend for databricks cost monitoring?
Engineering-led teams use StackSpend for databricks cost monitoring to catch cost problems the day they start — not three weeks later when the invoice lands. StackSpend reads account-wide usage from system.billing.usage joined to USD list prices, using a read-only service principal and one small SQL warehouse. Jobs, all-purpose and serverless compute, SQL warehouses, DLT pipelines, and model serving appear in the same monitoring workflow as the rest of your providers, per workspace and SKU.
What Databricks Cost Monitoring data does StackSpend track?
StackSpend tracks: Databricks billing system tables, system.billing.usage and list_prices, Account-wide DBU usage at USD list price, Cost by workspace, SKU, and product, Jobs, all-purpose, and serverless compute, SQL warehouses, DLT, and model serving, Budget thresholds and anomaly detection, Forecasting. All data is pulled using read-only credentials — StackSpend never modifies your account or provider settings.
How do I connect Databricks Cost Monitoring to StackSpend?
Connection takes around 5–10 minutes. You grant read-only access and StackSpend handles the rest. The step-by-step setup guide is at /resources/guides/providers/databricks.
How is StackSpend different from Databricks Cost Monitoring's native billing dashboard?
Databricks Cost Monitoring's native billing dashboard is useful for investigation but requires you to log in to look. StackSpend delivers a daily cost signal to Slack or email, fires anomaly alerts the day a spike starts, and surfaces pace-to-forecast so overruns are visible before month-end.
Does StackSpend support multiple Databricks Cost Monitoring accounts?
Yes. StackSpend supports connecting multiple Databricks Cost Monitoring accounts or workspaces to the same organisation. All accounts roll up into a single combined view alongside your other providers.
Tomorrow morning: your Databricks number, in Slack.
Connect Databricks read-only today. 90 days of history loads automatically, and the first daily signal arrives with breakfast.