Databricks Pricing 101: A Comprehensive Guide (2024)

Databricks—the Unified Analytics Platform built on the foundation of open-source giants—opens up a collaborative playground for data teams to come together, turning raw data into powerful insights. Its toolkit ranges from ETL to machine learning, making joint data exploration, modeling, and bringing ideas to life a breeze, all within one single platform. As its adoption skyrockets, understanding its pricing model is crucial. Databricks pricing model follows a pay-as-you-go approach, charging users only for the resources they use, with Databricks Units (DBUs) at the core representing computational usage.

In this article, we will cover how the Databricks pricing model works, including key concepts like what Databricks DBU cost (Databricks Units) are and the factors affecting the Databricks cost, Databricks pricing challenges and a whole lot more!!

Databricks Lakehouse Vision—Understanding Databricks Architecture

Before we dive into the core Databricks pricing, let's take a moment to understand its architecture first.

Databricks provides a unified data analytics platform built on top of Apache Spark to enable data engineering, data science, machine learning, and analytics workloads. The core components of the Databricks architecture include:

• Delta Lake: Delta Lake acts as an optimized storage layer that brings reliability to data lakes. It provides ACID transactions, scalable metadata handling, and unified streaming/batch processing. Delta Lake extends Parquet data files with a transaction log to provide ACID capabilities on top of cloud object stores like S3.  Metadata operations are made highly scalable through log-structured metadata handling. Delta Lake is fully compatible with Apache Spark APIs. It tightly integrates with Structured Streaming to enable using the same data copy for both batch and streaming workloads.
• Delta Engine: Delta Engine is an optimized query engine designed for efficient SQL query execution on data stored in Delta Lake. It uses techniques like caching, indexing, and query optimization to enable fast data retrieval and analysis on large data sets.
• Built-in Tools: Databricks includes ready-to-use tools for data engineering, data science, business intelligence, and machine learning operations. These tools integrate with Delta Lake and Delta Engine to provide a comprehensive environment for working with data.
• Unified Workspace: All the above components are accessed through a single workspace user interface hosted on the cloud, which allows data engineers, data scientists, and business analysts to collaborate on the same data in one platform.

Databricks Pricing Model Explained—What Drives Your Databricks Costs

Databricks pricing model is based on a pay-as-you-go model where users are charged only for what they use based on usage. The core billing unit is the Databricks Unit (DBU) which represents the computational resources used to run workloads. DBU usage is measured based on factors like cluster size, runtime, and features enabled.

Databricks DBU Cost

Databricks Units or DBUs encapsulate the total use of compute resources like CPU, memory, and I/O to run workloads on Databricks.

To calculate overall cost:

Databricks DBU consumed x Databricks DBU Rate = Total Cost

Databricks DBU cost start as low as $0.08 and go up to $0.50 based on few key factors:

• Cloud Provider and Region: Databricks DBU cost vary across cloud providers like AWS, Azure, and GCP as well as regions within a cloud provider based on localized data center costs.
• Databricks Edition: Databricks offers three editions: Standard, Premium and Enterprise.
• Compute Type ((Jobs, SQL, ML, etc.)): Further specialization of Databricks DBU pricing is available by compute type. Jobs Compute optimizes costs for ETL tasks, SQL Compute for analytics and dashboards, ML Compute for machine learning, etc. Each compute type has different Databricks DBU rates.
• Committed Use : Companies can take advantage of discounts on Databricks DBU cost via committed use contracts reserving a certain capacity for a term. The more capacity reserved, the higher the discount compared to pay-as-you-go rates.

TL;DR; Databricks pricing for DBUs varies by workload type (interactive, jobs, SQL), cloud provider and region. In addition to DBUs, you pay the cloud provider directly for associated resources like VMs, storage and networking. For the ML model serving with MLflow, Databricks pricing is based on concurrency/requests per hour instead of DBUs. You pay per request based on the number of requests and compute time per request.

Databricks provides fine-grained Databricks pricing aligned to how different users leverage the Lakehouse platform. Data engineers, data analysts, data scientists, and business analysts can optimize Databricks costs based on their specific use cases.

This consumption-based approach eliminates heavy upfront Databricks costs and provides the flexibility to scale up and down based on dynamic business requirements. Users can only pay for what they use with the Databricks pricing model.

Key Databricks Products—and Databricks Pricing

Databricks provides multiple products on its Lakehouse Platform for different data workloads. Each product has usage measured in Databricks DBUs consumed, which when multiplied by the Databricks DBU rate provides the final Databricks cost.

Here is a breakdown of major Databricks products and how they are priced:

1. Databricks Pricing —Jobs (Starting at $0.07  /  DBU)

Databricks Jobs provide a fully managed platform for running production ETL Databricks workflows at scale. Jobs auto-scale clusters up and down to match data processing needs. Databricks Jobs lets you easily ingest and transform batch and streaming data on the Databricks Lakehouse Platform using optimized Databricks clusters that are auto-scaled based on workload.

Key capabilities and benefits of Databricks Jobs include:

• Auto-scaling clusters to match workload needs—Jobs can automatically spin up and down clusters to provide exactly the right amount of compute resources needed to process each job. This optimize costs by not having idle resources.
• Support for different cluster types like Standard, High Concurrency and Optimized—Jobs can use various cluster types tuned for specific workloads like data engineering, data science and analytics.
• Integration with Delta Lake for reliability—Jobs natively integrates with Delta Lake for data pipeline reliability, data quality and lineage.
• Scheduling recurrence and dependencies—Jobs makes it easy to schedule data pipelines on fixed intervals, set start and end times, and configure pipeline dependencies.
• Integration with monitoring tools—Jobs provides metrics and integrates with tools like Grafana for monitoring workloads. Alerts can be set up to detect issues.

Databricks Jobs usage is billed based on Jobs Compute DBU rates which start at:

2. Databricks Pricing — Delta Live Tables (Starting at $0.20 / DBU)

Delta Live Tables makes building reliable, scalable data pipelines using SQL or Python easy via auto-scaling Apache Spark. It streams data continuously from sources into Databricks.

Delta Live Tables consumes Databricks DBU from Jobs Compute for running streaming and batch data pipelines. The base Databricks pricing rate starts at $0.20 per Databricks DBU based on the Standard edition.

Advanced features like pipeline branching, debugging and autoloader are available in higher editions and have correspondingly higher DBU cost.

3. Databricks Pricing — Databricks SQL (Starting at $0.22 / DBU)

Databricks SQL provides lightning fast interactive SQL analytics directly on massive datasets in data lakes. It scales to trillions of rows with ANSI-compliant syntax and BI integration.

Key aspects of Databricks SQL include:

• Massive scale - It leverages the Spark SQL engine to query trillions of rows of data in the lakehouse. No data movement needed.
• High performance - Optimized engine provides blazing fast query performance and sub-second responses on massive datasets.
• ANSI SQL syntax - Standard ANSI SQL makes it easy for anyone familiar with SQL to query. Also supports TSQL syntax.
• BI integrations - Integrates with BI tools like Tableau, Power BI and Looker to visualize and share insights.
• Scalable workloads - Workloads auto-scale by automatically provisioning resources to match query needs.
• Workgroup isolation - SQL Workgroups provide isolated SQL Endpoint clusters for more consistent performance and control.

For Databricks SQL, usage pricing is based on SQL Compute DBU rates which start at:

Tldr; Databricks SQL brings the simplicity and familiarity of SQL analytics to data lakes and provides a performant and collaborative analytics platform for data teams.

4. Databricks Pricing — Data Science & ML(Starting at $0.40 / DBU)

Databricks provides a complete platform for data science and machine learning powered by Spark, MLflow and Delta Lake. This enables data teams to collaborate on the full ML lifecycle.

ML workloads run on All Purpose clusters with Databricks DBU rates starting at $0.40 per DBU on AWS under the Standard plan. ML Compute optimized clusters, GPUs and advanced MLOps capabilities come at higher Databricks pricing points.

Usage scales based on factors like model complexity, size of training data and frequency of retraining. Jobs provide metrics on training times and DBU consumption for ML workloads.

5. Databricks Pricing — Serverless Inference (Starting at $0.07 / DBU)

In addition to training ML models, Databricks enables directly deploying models for low latency and auto-scaling inference via its serverless offering.

Key aspects of Databricks Serverless Inference include:

• Real-time predictions—Achieve sub-second latency for model predictions on new data.
• Auto-scaling—Serverless automatically scales up and down to handle usage spikes and troughs.
• Pay-per-use pricing—Only pay for predictions served, not idle model instances.
• Integrates with MLflow—Deploy models tracked in MLflow Model Registry.
• Multiple frameworks—Supports major frameworks like TensorFlow, PyTorch, and scikit-learn.
• Monitoring—Detailed metrics on prediction throughput, data drift, costs and more.

For real-time model scoring, the Serverless ML plans start at $0.07 per DBU on AWS. More optimized model serving with higher throughput has rates of $0.14 per DBU.

Databricks Serverless Inference provides an efficient and flexible way to integrate ML predictions with applications in a cost-effective pay-per-use pricing model.

Check out this article to learn more in-depth about Databricks pricing.

Databricks Costs — Key Factors Affecting It

While DBUs provide a standardized usage measure, total DBUs depend on data and workload specifics. The key factors that drive DBU usage are:

1. Data Volume: Higher volumes require more processing, increasing DBUs.
2. Data Complexity: More complex data and algorithms lead to higher DBU consumption.
3. Data Velocity: For streaming, higher throughput increases DBU usage.

Overall Databricks Pricing Breakdown:

Now that we've covered most of the Databricks cost and pricing aspects, it's worth mentioning that there's actually a tool for calculating Databricks cost—Databricks pricing calculator.

Using DBU Calculator to Estimate Databricks Costs

To simplify estimating Databricks costs Databricks provides the Databricks DBU Calculator which is a very handy tool. It lets you model hypothetical workloads based on parameters like:

• Databricks Editions: (Standard, Premium and Enterprises)
• Compute type
• AWS instance type
• Cloud platform and region

You can tweak these parameters to reflect your actual data and pipelines. The Databricks calculator will help you estimate the:

• Databricks DBUs likely to be consumed by the workload
• Applicable Databricks DBU cost based on choices
• Total daily + monthly cost

This provides a projected Databricks spend based on your unique situation. The calculator also lets you experiment with different scenarios and see how the Databricks pricing changes. So by modeling your existing and projected future workloads through the DBU Calculator, you can arrive at a reasonable estimate of your overall Databricks costs.

Databricks Pricing and Billing Challenges

While Databricks provides extensive flexibility, some key Databricks pricing and billing challenges to note are:

• Manual Billing Data Integration: Integrating Databricks costs into overall cloud spend reporting requires substantial amounts of manual effort without automated processes. This is time-consuming and riskier for Databricks billing accuracy.
• Double charges: Databricks has two main charges—licensing and infrastructure costs. This duality makes assessing total Databricks costs extremely difficult.
• Lack of Spending Controls: Databricks lacks robust spending limit controls or cost alerting to prevent unexpected overages and budget overruns.
• Granular Cost Tracking Issues: It is hard to differentiate Databricks costs by specific capabilities like data exploration vs insights. And determining which business units drive Databricks costs  is very challenging to figure out.

Conclusion

Databricks pricing model operates on a pay-as-you-go model where users are charged  solely for the resources utilized, which is a transparent and user-friendly approach. The core billing unit is the Databricks Unit (DBU) which represents the computational resources used to run workloads. DBU usage is measured based on factors like cluster size, runtime, and features enabled. In this article, we covered:

• What is Databricks Unit (DBU) and its impact on overall cost
• How total cost is calculated based on DBU consumption and DBU rate
• Variable DBU cost across cloud providers, Databricks editions, and compute types.
• Pricing breakdown for key Databricks products like Jobs, Delta Live Tables, Databricks SQL, Data Science & ML, and Serverless Inference
• Primary cost drivers including data volume, complexity, and velocity affecting DBU consumption
• Databricks DBU Calculator for better cost estimation and budgeting
• Challenges related to Databricks pricing and billing.

At first glance, navigating Databricks pricing might seem daunting at first, given its range of products and cloud options. But, getting a handle on its cost structure, especially the Databricks Units (DBU) cost, is crucial to avoid surprises and keep your analytics operations within budget.

FAQs

What is Databricks pricing based on?

Databricks uses a pay-as-you-go pricing model based on usage measured in Databricks Units (DBUs). You are charged for the computational resources used to run workloads.

How are Databricks DBUs calculated?

Databricks DBUs encapsulate the total use of compute resources like CPU, memory, and I/O to run workloads. DBU usage depends on factors like cluster size, runtime, and enabled features.

What drives Databricks costs?

The main factors affecting Databricks costs are data volume, data complexity, and data velocity. Higher volumes, more complex processing, and increased throughput all increase DBU usage.

How do you calculate overall Databricks cost?

Total Databricks cost = DBUs consumed x DBU hourly rate. The DBU rate varies based on cloud provider, region, features enabled.

How can you estimate Databricks costs?

Databricks provides a DBU Calculator tool to model hypothetical workloads and estimate likely DBU usage. This projected DBU usage can be multiplied by DBU rates to estimate overall costs.

What is the Databricks Community Edition?

It's a free version of Databricks with limited features, suitable for training or limited usage.

What does the Databricks free trial offer?

The free trial provides user-interactive notebooks to work with various tools like Apache Spark and Python, without any charge for Databricks services, though underlying cloud infrastructure charges apply.

How are DBU cost billed?

DBUs are billed on a per-second basis, making the pricing flexible based on usage.

What factors affect the number of DBUs consumed?

Factors include the amount of data processed, memory used, vCPU power, region, and the pricing tier selected.

How does Serverless Compute affect Databricks cost?

Serverless Compute simplifies cluster management but the pricing may vary based on the resources consumed.

Are there any commitment-based discounts available?

Yes, committing to a certain amount of usage can earn discounts off the standard rate.

How do Spot Instances contribute to cost-saving?

Spot Instances can provide up to 90% savings on Databricks costs compared to on-demand pricing.

How is pricing affected by the Databricks Compute type chosen?

Pricing varies based on the compute type chosen as each caters to different processing needs and resource consumption.

What additional costs should be considered?

Additional costs may include Enhanced Security & Compliance Add-ons, especially for regulated data processing.

How does Databricks pricing compare to traditional data analytics platforms?

Databricks claims to offer better price-performance, especially on AWS using Graviton2 instances.

How does the region affect Databricks pricing?

Pricing may vary based on the region due to different operational costs and cloud resource pricing.