Databricks Pricing: DBU Rates for AWS, Azure and GCP
Databricks pricing is per DBU: most compute runs $0.15 to $0.95 per DBU at Premium list rates. Rates by compute type for AWS, Azure and GCP, side by side.
Databricks pricing is consumption-based: you pay per DBU (Databricks Unit), metered per second, at a rate set by compute type, plan tier, cloud, and region. At September 2026 Premium list rates in US East regions, classic jobs compute on AWS bills $0.15 per DBU and all-purpose compute $0.55; the table below covers every compute type on all three clouds. Classic compute also puts the VMs on your cloud bill, while serverless rates include the compute; storage and networking come from your cloud provider either way. This guide explains the Databricks pricing model — how tiers and clouds change the number, and the costs the pricing calculator won’t show you.
While the model is simple on paper, in practice, it can often become confusing when trying to explain costs of specific workloads — for instance, the difference in cost for the same SQL query can change significantly depending on the type of warehouse used to execute it.
What a DBU Is and How You’re Billed
A DBU is a normalized unit of processing capability that Databricks meters per second of use. It is not a fixed amount of CPU or memory; it’s a billing unit whose consumption depends on multiple factors, such as cluster instance type. Your DBU cost for any workload is DBUs consumed times the per-DBU rate for that workload’s SKU.
The important part, as stated above, is that the full bill has two layers. Databricks meters DBUs. Your cloud provider (AWS, Azure, or GCP) separately bills the VMs, storage, and networking the workload ran on.
A migration that moves workloads from classic compute to serverless can look like “DBUs up, infrastructure down” while total spend barely moves — because serverless SKUs fold the infrastructure into a single, higher DBU rate. For the deeper mechanics of how DBUs meter, see the DBUs explained post.
DBU Rates by Compute Type
Different compute types bill at different per-DBU rates, and the spread is wide. Jobs compute is the cheapest classic SKU on AWS and GCP, while all-purpose (interactive) compute is the most expensive; SQL and serverless sit in between or above. The list rates below are Premium tier, US dollars per DBU, in each cloud’s US East region (AWS N. Virginia, Azure East US, GCP Virginia), as of September 2026 (Databricks pricing, Azure Databricks pricing). On Azure, Premium corresponds to the Enterprise tier on AWS and GCP. Rates change over time and by region, so treat them as the shape of the model rather than a quote.
| Compute type | AWS | Azure | GCP |
|---|---|---|---|
| Jobs Compute (Lakeflow Jobs Classic) | $0.15 | $0.30 | $0.15 |
| Lakeflow Declarative Pipelines (Core) | $0.20 | $0.30 | $0.20 |
| SQL Classic | $0.22 | $0.22 | $0.22 |
| Serverless jobs and pipelines | $0.35 | $0.45 | $0.35 |
| All-Purpose Compute | $0.55 | $0.55 | $0.55 |
| SQL Pro | $0.55 | $0.55 | $0.55 |
| SQL Serverless | $0.70 | $0.70 | $0.70 |
| Serverless notebooks | $0.75 | $0.95 | $0.75 |
On AWS and GCP, all-purpose compute runs at roughly three to four times the rate of jobs compute (about 1.8x on Azure, where jobs compute lists at $0.30), which is why scheduled work that quietly lands on an interactive cluster is such an easy source of cost to miss (all-purpose vs jobs compute). Note the product names: what many still call Databricks Workflows is now Lakeflow Jobs, and Delta Live Tables is now Lakeflow Declarative Pipelines. The classic billing SKUs still carry the older prefixes, which is why both names show up in cost data.
Serverless rates need a caveat when you compare them. SQL Serverless at $0.70 looks far above SQL Classic at $0.22, but the serverless number folds in the cloud VM cost that classic compute bills separately on top of its DBU rate. Comparing a serverless $/DBU against a classic $/DBU is not like-for-like. The honest comparison is total cost, DBUs plus the separate infrastructure line, and on bursty workloads serverless often comes out ahead despite the higher headline rate.
Lakebase, the managed Postgres database, is priced per capacity-unit hour (converted to DBUs on your bill); see Lakebase pricing for its rates on each cloud.
Pricing Tiers: Standard, Premium, Enterprise
Your plan tier multiplies every rate above. Databricks plans run Standard, Premium, and Enterprise, though Azure has now retired its Standard tier and per-cloud availability differs (see the cloud sections below). Higher tiers cost more per DBU and add governance, security, and compliance features.
One tier fact matters for cost tooling specifically: Unity Catalog requires the Premium plan or above, and it is enabled by default on workspaces created since late 2023 (Unity Catalog setup). That’s relevant because the system tables you use to track cost live in Unity Catalog. If you’re on a Standard workspace, the detailed usage data isn’t available until you move up. Azure retired its Standard tier on October 1, 2026, so most new environments start at Premium regardless.
Why Cost Differs by Cloud and Region
The same compute type costs different amounts on AWS, Azure, and GCP — and within a cloud it varies by region. DBU rates are set per cloud, and the underlying VM prices that sit beneath classic compute follow each cloud’s own regional pricing.
Azure Databricks Pricing
Azure Databricks is a first-party Azure service, so DBUs bill through your Azure subscription at rates Microsoft publishes on the Azure Databricks pricing page. The underlying VMs follow Azure’s own regional rate card. Azure’s plan tiers were Standard and Premium until the Standard tier reached end of life on October 1, 2026: new workspaces have had to use Premium since April 1, 2026, and Standard workspaces still running on October 1 are upgraded to Premium automatically (Microsoft Learn).
AWS Databricks Pricing
On AWS, Databricks sets the DBU rates (AWS pricing) while AWS bills the EC2 instances, EBS storage, and data transfer separately underneath. AWS offers the Premium and Enterprise tiers, and per-DBU rates vary by region alongside EC2 pricing.
GCP Databricks Pricing
On Google Cloud, Databricks publishes its own GCP DBU rates while Google bills the Compute Engine VMs and storage. GCP runs on the Premium tier, with the same compute-type rate spread; the difference is the underlying GCE rate card and regional availability.
“How much does Databricks cost?” has no single answer because the rate depends on compute type, tier, cloud, and region all at once.
This is why a number someone quotes from a blog post or a proposal rarely matches your bill. The model is consistent — the inputs are specific to your setup.
The Pricing Calculator and What It Misses
Databricks publishes an official pricing calculator that multiplies an estimated DBU consumption by the rate for a compute type and tier. It’s useful for a first estimate and for comparing SKUs. What it can’t do is predict how your environment behaves.
The calculator assumes a clean consumption number. Real environments add autoscaling that overshoots and holds extra nodes, clusters that idle between runs at the full rate, and jobs that fail and retry, re-paying startup each time. None of those show up in an estimate. The gap between the calculator and the invoice is almost always in the environment behavior.
Our Databricks cost calculator starts from the workloads instead — you describe what you run, and it estimates the bill including the behavior layer.
Hidden Cost Drivers Teams Discover Late
Most pricing surprises come from a handful of drivers that the rate card doesn’t make obvious.
- Photon. Enabling Photon doesn’t raise the price per DBU; it consumes more DBUs per hour because Photon-enabled nodes carry a higher DBU multiplier. On compute-bound work it finishes faster and can lower total cost; on work it can’t accelerate, it just bills more per hour. Whether it pays off is a per-workload question.
- Autoscaling overshoot. Autoscaling adds nodes quickly and releases them slowly, so a brief spike can keep extra capacity billing long after the spike passes.
- Idle compute. An interactive cluster or a warehouse with a long auto-stop window bills at the full rate while doing nothing.
- Storage is separate. DBUs cover compute. Cloud storage and data transfer are billed by the cloud provider on top.
- Commitments, not reserved instances. Databricks discounts come from committed-use agreements (measured in DBCUs, Databricks Commit Units), not the “reserved instances” model you know from cloud VMs, which lower your effective rate below list.
Once a workload is running, the only source of truth is your own usage data. Databricks system tables record actual DBU consumption and the list rate for every line item, so you can reconcile what you were billed against what you estimated. The recurring lesson is that the surprises live in behavior, and behavior is what an estimate can’t capture; for the wider playbook on acting on that data, see Databricks cost optimization.
Databricks pricing is predictable once you separate the model from the behavior. The rate card is straightforward. The surprises come from the gap between the model and how your environment runs day to day — such as shared clusters, autoscaling, idle time, Photon. Closing this gap is a visibility problem more than a pricing one.
FAQ
1. What is a DBU in Databricks?
A DBU (Databricks Unit) is a normalized unit of processing capability, billed per second of use. The DBUs a workload consumes per hour depend on its instance type and node count, and the dollar rate per DBU depends on the compute type, tier, cloud, and region.
2. How much does Databricks cost per DBU?
It depends on the compute type and plan. For instance, a rough AWS Premium guide for September 2026: jobs compute around $0.15, SQL Classic around $0.22, all-purpose around $0.55, and SQL Serverless around $0.70 per DBU.
3. Why is all-purpose compute so much more expensive than jobs compute?
All-purpose (interactive) compute is the priciest classic SKU because it’s built for shared, interactive sessions. It runs roughly three to four times the jobs-compute rate on AWS and GCP (about 1.8x on Azure), so scheduled work placed on it by accident is a frequent, easily-missed cost.
4. Does Photon increase the DBU rate?
No. Photon keeps the same price per DBU but consumes more DBUs per hour. It lowers total cost only when the speedup outweighs the extra consumption, which depends on the workload.
5. Why doesn’t the pricing calculator match my bill?
The calculator estimates rate times a clean DBU number. Your bill reflects real behavior (idle time, autoscaling overshoot, retries), plus separately billed cloud infrastructure and any committed-use discount. Those gaps are where estimates and invoices diverge.
6. Is Databricks cheaper on AWS, Azure, or GCP?
There’s no universal answer. Databricks sets DBU rates per cloud, and Microsoft, AWS, and Google each price the underlying VMs on their own regional rate cards. The cheapest option depends on your compute mix, region, and tier, so compare total cost (DBUs plus infrastructure) on your workloads, not headline rates.
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Related reading
Databricks cost optimization is matching compute and warehouses to actual workload demand. Five steps: see cost, right-size, tune, automate, monitor.
A DBU is the metering unit Databricks uses to bill compute, with rates that differ by compute type. How to tune DBU spend without risking workloads.
When Databricks spend moves, start here. Seven common drivers from DBU multipliers to retry storms, with diagnostic steps and safe first moves for each.
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