How Databricks Pricing Works: A 2026 Cost Breakdown

For data professionals interested in data modeling, artificial intelligence, and machine learning, Databricks offers a robust cloud data platform. The Databricks Lakehouse combines data lake and data warehouse capabilities in one architecture.

This makes Databricks particularly suitable for building sophisticated data analytics, artificial intelligence (AI), and Machine Learning (ML) models.

Databricks has published benchmarks suggesting its Lakehouse delivers up to 12X better price-performance than traditional data warehouses under specific conditions. Keep in mind that this figure reflects controlled benchmarks—actual savings depend on your workload mix, cloud provider, and whether you factor in the infrastructure costs your CSP charges separately.

Databricks has also expanded its compute options with support for AWS Graviton3 and Graviton4 instances, building on the up to 3X price-performance improvement first introduced with Graviton2 in 2022.

So how does Databricks pricing actually work? Let’s break it down.

How Does Databricks Charge?

Databricks charges you based on the compute resources you consume. Databricks uses per-second billing for this pay-as-you-go model. Using Databricks doesn’t require upfront costs or recurring contracts. You just pay for what you need when you need it (on-demand rate).

If you want to use Databricks for free but with limited features, such as to train your data team, you can use the Databricks Community Edition (fully open-source). Databricks offers a free 14-day trial if you want to try it out fully.

You can earn discounts off the on-demand rate in two ways:

Databricks bills per Databricks Unit (DBU).

What is a DBU in Databricks?

DBU units measure the amount of processing power you use on Databricks’ Lakehouse Data Platform per hour. Billing is based on per-second usage.

To determine the cost of Databricks, multiply the number of DBUs you used by the dollar rate for each DBU.

Several factors determine how many DBUs a specific workload consumes, including:

Your Cloud Service Platform (CSP) plays a crucial role in how much you actually spend on Databricks (Total Cost of Ownership). Here’s why.

Understanding Databricks Pricing: What You Need To Know 

Pricing for Databricks clusters depends on the following factors:

Your cloud provider provides the compute instances, storage, and networking capacity you use with Databricks — and you pay the CSP a separate fee for those.

One important detail that catches many teams off guard: Databricks uses a dual-billing structure. You receive one bill from Databricks for DBU consumption and a separate bill from your cloud provider for the underlying compute and storage infrastructure. Teams often underestimate total Databricks costs by 50–200% when they only budget for the DBU charges and overlook the CSP infrastructure underneath. When forecasting spend, plan for the DBU cost plus an equivalent or greater amount for compute and storage.

Databricks consumes DBUs as long as the compute cluster is active. The number of DBUs you use depends on various factors, such as your data volume, processing time, and the complexity of your data transformation.

Example: Let’s say you used an i3.xlarge cluster for a two-hour data pipeline that consumed three DBUs to complete your task. You’d need to pay your cloud service provider directly for the compute cluster you used during the two hours and then pay an additional charge in DBUs to Databricks.

Behind the scenes, when the job starts, Databricks automatically turns on your CSP-provided compute instances (such as Amazon EC2 instances), executes the task, and then switches the instances off after a predefined idle period to save costs (Databricks’ auto-terminate feature).

Credit: Databricks auto-terminate feature

In a minute, we’ll examine each approach. Meanwhile, you can test out Databricks for 14 days free to see if it’s right for your workload.

What does the Databricks free trial provide?

You get user-interactive notebooks to work with Apache Spark, Delta Lake, Python, TensorFlow, SQL, Keras, Scala, MLFlow, and scikit-learn, among other tools. If you want to deploy Databricks on a private cloud, you’ll need to contact them for custom configuration.

During the trial, Databricks won’t charge you for using its service, but the underlying cloud infrastructure will (such as Amazon EC2 instance or Azure VMs).

You can cancel the free trial at any time before it expires. Otherwise, you’ll automatically be subscribed to your current plan.

Here’s how your AWS, Azure, or Google Cloud infrastructure affects your Databricks costs.

1. Databricks pricing on AWS

This pay-as-you-go method means you only pay for what you use (on-demand rate billed per second). If you commit to a certain level of consumption through DCUs, you can get discounts. As of 2026, Databricks on AWS offers two active pricing tiers—Premium and Enterprise—across multiple compute types:

Databricks on AWS pricing

This is what your dollar rate per DBU for one hour using a m5d instance type would look like if you chose the Standard plan and an All-Purpose Compute (with Photon) Databricks Compute type:

Databricks on AWS pricing for m5d instance types

Each tier includes different capabilities. Premium includes Unity Catalog, Databricks SQL Workspace, and enhanced security features, while Enterprise adds compliance, audit, and advanced governance tools.

Credit: Databricks

Databricks’ Serverless Compute service will run on Databricks’ AWS account, so you won’t have to pay Databricks and AWS separately.

Now, if you want to simplify that calculation, Databricks offers a calculator with filters to make it easier:

Credit: Databricks pricing on AWS calculator

Then, with CloudZero Advisor, you can also get recommendations on AWS services such as EC2, S3, and ElastiCache based on pricing, performance, and instance configuration to optimize your CSP costs. Here’s an idea of what to expect:

Discover the best AWS tools based on pricing, AWS services, regions, instance types, sizes, and more using CloudZero Advisor.

2. Databricks pricing on Azure

Azure Databricks primarily offers a Premium tier as its main plan. The Standard tier is being retired—new Standard workspace creation was blocked after April 1, 2026, and remaining Standard workspaces will be automatically upgraded to Premium by October 2026. Azure supports multiple Databricks compute workload types:

Microsoft Azure Databricks pricing

On top of the payment for Azure virtual machine usage, Azure Databricks will also charge for managed storage, disks, and blobs.

Nonetheless, Azure offers discounts of up to 33% and 37% off the on-demand DBU rate per hour when you commit to a certain usage level for one and three years, respectively. If you want the best deal, you can also use spot instances.

Here’s an example of prices if you used Azure’s DV3 series instances:

Microsoft Azure Databricks pricing for General Purpose – DV3 Series instances

3. Databricks pricing on Google Cloud

Databricks on Google Cloud also uses on-demand billing, like AWS and Azure, without upfront costs or contracts. As with AWS, the Standard tier was sunset in October 2025, leaving Premium as the primary tier with multiple compute types:

Databricks on Google Cloud pricing

Here’s a more detailed example, with various pricing factors considered:

Pricing for Databricks on Google Cloud

SQL Warehouse Pricing

If you run BI workloads or ad hoc SQL queries on Databricks, the SQL warehouse type you choose has a significant impact on cost. Databricks offers three options:

SQL Classic (~$0.22/DBU on AWS) runs compute in your cloud account and is the lowest-cost option per DBU, though it lacks newer features like Predictive I/O. SQL Pro (~$0.55/DBU) adds Predictive I/O and Intelligent Workload Management while still running in your account. Serverless SQL (~$0.70/DBU in US regions, ~$0.91/DBU in EU regions) is fully managed by Databricks with infrastructure costs bundled into the DBU rate—making it simpler to budget and often cheaper for intermittent or spiky workloads since it scales to zero when idle.

Databricks is increasingly steering organizations toward Serverless SQL, and for many teams the lower total cost of ownership outweighs the higher per-DBU rate.

Model Serving and AI Workload Pricing

For teams deploying ML models into production, Databricks now offers structured model serving pricing. Two modes are available: pay-per-token, which is ideal for prototyping and variable-volume inference (billed per token processed and converted to DBUs), and provisioned throughput, designed for production workloads requiring guaranteed capacity at approximately $0.07/DBU for foundation model serving. Organizations using Databricks for AI and ML should factor model serving costs into their total spend alongside compute and storage.

What Affects Your Databricks Costs?

Below are a few key takeaways to keep in mind:

The type of compute workload also has an outsized impact on cost. All-Purpose Compute clusters, which are used for interactive notebook development, can cost 2–3X more per DBU than Jobs Compute clusters used for automated pipelines. Switching production workloads from interactive All-Purpose Compute to scheduled Jobs Compute is one of the fastest ways to cut Databricks spend by 40–60%.

Accurately Collect, Understand, And Optimize Your Databricks Costs With CloudZero

Databricks integrates data lake and data warehouse capabilities for modern data professionals. The tool is handy for training and analyzing AI and machine learning models.

Even so, many teams find that Databricks costs add up faster than expected. To reduce your costs, you can:

However, if you aren’t sure how much capacity you’ll need, you can use a robust enough tool to aggregate, collect, normalize, analyze, and present your Databricks costs in a way that’s valuable to you.

Once you’ve tracked the cost insights for a while, you’ll have enough accurate, granular intelligence to make a decision.

You can achieve all of this and more with CloudZero.

CloudZero AnyCost aggregates, enriches, visualizes, analyzes, and presents your Databricks costs across AWS, Azure, Kubernetes, and GCP. For organizations using Databricks for AI and ML workloads, CloudZero’s AI cost management framework provides the additional layer of visibility needed to track costs by model, training run, and inference endpoint.

The future is multi-cloud, and CloudZero allows you to get a holistic view of all your costs, including Databricks, MongoDB, New Relic, and Snowflake costs.

With CloudZero AnyCost, you can zoom in on your costs and view costs by customer, product, feature, team, project, environment, service, and more.

Real-time anomaly detection sends smart alerts to a specific team or individual via Slack, email, or text so they can prevent overspending.

to see the power of CloudZero for yourself!

Software and pricing information last verified May 2026. Features, pricing, and availability may have changed. Please verify current details with vendors before making decisions.

FAQs

We’ve answered some of the most common questions about Databricks pricing below:

What is the Databricks pricing model?

It’s pay-as-you-go. There are no contracts or upfront payments required. You can use this on-demand rate (as needed) if you’re unsure how many resources you’ll require to complete a particular task. Committed usage offers discounts if you know how much you’ll use.

How is Databricks billed?

Databricks charges you based on the amount of Databricks Units (DBUs) you consume per hour, billed per second of usage. You also receive a separate bill from your cloud provider (AWS, Azure, or GCP) for the underlying compute and storage infrastructure.

Does Databricks have a free plan?

Yes. It offers a 14-day free trial, after which you’ll be automatically billed at the on-demand rate if you don’t cancel.

Is Snowflake more expensive than Databricks?

It can be tough to compare Snowflake vs. Databricks pricing because both offer unique data cloud services with different pricing considerations.

Is Databricks an alternative to Snowflake?

Many organizations use Databricks (data science, analytics, and modeling for AI and ML) and Snowflake (Data analytics and Business Intelligence) together. For a detailed comparison of their performance, pricing, and use cases, check out Snowflake vs. Databricks.