Sold by: Databricks, Inc.
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The Databricks Data Intelligence Platform unlocks the power of data and AI for your entire organization. Enjoy up to $400 in usage credits during your 14-day free trial. Cancel anytime. After your trial ends, you will automatically be enrolled into a Databricks pay-as-you-go plan.
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Overview
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Databricks at AWS re:Invent 2024
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Get started today with up to $400 in usage credits during your 14-day free trial. Trial ends the earlier of when credits are consumed or the 14-day period expires. After your trial ends, you will be automatically enrolled into a Databricks pay-as-you-go plan using the payment method associated with your AWS Marketplace account, paying only for what you use and you can cancel anytime. You can view the full per-product rates for Databricks Units (DBUs) at https://www.databricks.com/product/pricing
The Databricks Data Intelligence Platform allows your entire organization to use data and AI. Its built on a lakehouse to provide an open, unified foundation for all your data and governance. And its powered by a Data Intelligence Engine that speaks the language of your organization so anyone can access the data and insights they need.
The Data Intelligence Platform simplifies your modern data stack by eliminating the data silos that traditionally separate and complicate data engineering, analytics, BI, data science and machine learning. Databricks is built on open source and open standards to maximize flexibility. And the platforms common approach to data management, security and governance helps you operate more efficiently and innovate faster across all analytics use cases.
Reach out to sales@databricks.com to get specialized configurations and pricing for Databricks on AWS Marketplace on a contract basis.
** Technical Support: For help setting up your account, connecting to data, or exploring the platform please reach out to help@databricks.com **
Highlights
- Simple: Databricks provides a simplified data architecture by unifying data, analytics and AI workloads on one common platform running on Amazon S3.
- Open: Built on top of the world's most successful open source data projects, the Lakehouse Platform unifies your data ecosystem with open standards and formats.
- Collaborative: With native collaboration capabilities, the Databricks Lakehouse Platform unifies data teams to collaborate across the entire data and AI workflow.
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Financing for AWS Marketplace purchases
AWS Marketplace now accepts line of credit payments through the PNC Vendor Finance program. This program is available to select AWS customers in the US, excluding NV, NC, ND, TN, & VT.
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Try this product free according to the free trial terms set by the vendor.
Pricing is based on actual usage, with charges varying according to how much you consume. Subscriptions have no end date and may be canceled any time.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Cost/unit |
|---|---|
Databricks Consumption Units | $1.00 |
Dimensions summary
This listing bills through a single dimension: Databricks Consumption Units. You pay based on your actual usage, with no upfront commitment. Consumption is measured by compute processing power drawn as you run workloads. The unit rate you consume varies with the type of workload you run, such as data engineering, data warehousing, interactive, or AI processing. Because pricing is usage-based, your cost scales up or down with how much you use. Charges accrue as you consume and are drawn against your Consumption Units.
Top-of-mind questions for buyers
A Databricks Unit is a normalized measure of processing power used for billing. The number consumed reflects the compute resources used and the amount of data processed. Consumption is metered at per-second granularity as you run workloads, so you pay only for what you actually use.
Yes. The unit rate varies by workload type. Data engineering, data warehousing, interactive workloads, and AI or model serving each draw Consumption Units at their own rates. Serverless and classic compute options also consume at different rates. The workload you choose determines how quickly your units are drawn.
Consumption Units are drawn based on compute usage, so charges accrue while workloads run. Pricing covers compute processing only. Storage, networking, and related cloud infrastructure are billed separately and vary by service and region. Non-serverless usage does not include underlying AWS resources such as EC2 instances.
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SaaS delivers cloud-based software applications directly to customers over the internet. You can access these applications through a subscription model. You will pay recurring monthly usage fees through your AWS bill, while AWS handles deployment and infrastructure management, ensuring scalability, reliability, and seamless integration with other AWS services.
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Vendor support
Please reach out to sales@databricks.com with any questions or for options on contract or pricing terms.
Technical Support: For help setting up your account, connecting to data, or exploring the platform please reach out to help@databricks.com
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By Databricks, Inc.
By Cloudera
Top
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In Databases & Analytics Platforms, ML Solutions, Data Analytics
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In ML Solutions
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In Data Analysis
Sentiment is AI generated from actual customer reviews on AWS and G2
Functionality
Ease of use
Customer service
Cost effectiveness
Positive reviews
Mixed reviews
Negative reviews
AI generated from product descriptions
Lakehouse Architecture
Unified data foundation built on lakehouse architecture providing open, unified foundation for data and governance with support for open standards and formats
Data Intelligence Engine
Powered by Data Intelligence Engine that enables organization-wide access to data and insights across all users and roles
Multi-Workload Unification
Consolidates data engineering, analytics, business intelligence, data science and machine learning workloads on a single common platform
Open Source Foundation
Built on open source data projects and open standards to maximize flexibility and interoperability across data ecosystem
Collaborative Capabilities
Native collaboration features enabling unified data teams to collaborate across entire data and AI workflow
AWS Data Source Integration
Secure connectivity to Amazon S3, Amazon Redshift, and Amazon RDS with push-down computation capabilities.
Elastic Compute Scaling
Distributed data and machine learning processing powered by Amazon EKS supporting Python, R, Spark, and additional frameworks.
AWS AI Service Integration
Pre-built workflows integrating AWS AI services including Amazon SageMaker, Amazon Comprehend, and Amazon Bedrock for chat, RAG, and agentic workflows.
Visual Analytics and ML Interface
Visual platform enabling creation of advanced analytics, data pipelines, and machine learning models accessible to both technical and non-technical users.
Governance and Transparency Framework
Built-in governance, transparency, and control mechanisms for managing AI projects, deployments, and team collaboration at scale.
Auto-scaling Capability
Intelligently autoscales workloads up and down across hybrid and public cloud environments for optimized cloud infrastructure utilization.
Multi-function Analytics Platform
Provides integrated data warehouse, machine learning, and custom analytics services with standardized user experience for ingesting, transforming, querying, optimizing, and making predictions from data.
Unified Security and Governance Framework
Implements Shared Data Experience (SDX) technology that sets security and governance policies once and applies them consistently across all data and workloads regardless of infrastructure.
Cross-infrastructure Policy Portability
Maintains security and governance policies with data as it moves across multiple public clouds, private cloud, and hybrid cloud environments.
Centralized Data Management and Discovery
Provides unified metadata management enabling efficient data curation, discovery, and sharing while eliminating data silos and redundant data copies.
Standard contract
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Customer reviews
Rakesh G.
Engineer-Friendly, but Takes Time to Tune Settings for Cost Optimization
Reviewed on Oct 31, 2026
Review provided by G2
What do you like best about the product?
Good tool for engineers even with low coding knowledge
What do you dislike about the product?
Need to spend good amount of time to figure out optimal settings to optimize usage cost
What problems is the product solving and how is that benefiting you?
Helping with connect with internal datalake and run optimization problems
Harshal A.
Flexible Azure Data Engineering with Databricks: Delta Lake, PySpark & Spark SQL
Reviewed on Oct 29, 2026
Review provided by G2
What do you like best about the product?
As an Azure data engineer, I like Databricks because it gives me the flexibility to build data pipelines that can handle both structured and semi-structured data. I can use PySpark and Spark SQL to clean raw files, apply business rules, and prepare curated datasets for analytics. Working with Delta Lake is especially useful when managing incremental loads and maintaining reliable data across different processing stages. My favorite features are Delta Lake, PySpark notebooks, Spark SQL, workflow scheduling, and the medallion architecture. Delta Lake supports reliable table operations, and the workspace provides the best environment for developing and testing data pipelines. Notebooks make it easy to combine code, SQL, and output results in one place.
What do you dislike about the product?
Some configurations require a good understanding of compute resources, access permissions, and workspace settings. Troubleshooting slow jobs can also take time when data is skewed or transformations are not optimized. Monitoring compute usage is important because poorly managed workloads can increase cloud costs.
What problems is the product solving and how is that benefiting you?
It helps me to process data from multiple sources and transform it into a consistent format for downstream reporting. In an Azure environment, I can work with files stored in Azure Data Lake Storage, apply transformations using PySpark, and organize the results into bronze, silver, and gold layers. This makes it easier to implement data quality checks and maintain a repeatable ETL process.
Anand D.
Scalable Data Platform That Covers All Our Needs
Reviewed on Oct 29, 2026
Review provided by G2
What do you like best about the product?
its for all the data needs. can be scaled, room for endless new features, data governance
What do you dislike about the product?
nothing much . its pretty good and i love it
What problems is the product solving and how is that benefiting you?
it can solve any data problem. sky is the limit
Vinay L.
Databricks Delivers Exceptional Efficiency and Reliable ML Pipelines
Reviewed on Oct 28, 2026
Review provided by G2
What do you like best about the product?
The best thing I want to highlight about Databricks is the overall efficiency and data handling capabilities which has been exceptional in our case, we have used databricks for almost 3 years and had not found anything blocking our data and engineering teams.
Talking about its features as an head of quality my primary focus was to ensure the Reliability and the accuracy and performance for our machine learning models since we are using massive data to feed them and databrics has done the job perfectly for our engineering and testing teams as well.
It has allowed us to enable quality control directly into the pipeline which has automated the set rules bad records automatically before the reach our models it has saved 63.5% time in total.
Talking about its features as an head of quality my primary focus was to ensure the Reliability and the accuracy and performance for our machine learning models since we are using massive data to feed them and databrics has done the job perfectly for our engineering and testing teams as well.
It has allowed us to enable quality control directly into the pipeline which has automated the set rules bad records automatically before the reach our models it has saved 63.5% time in total.
What do you dislike about the product?
I don't dislike anything about databrics it has done the job perfectly has have to trigger Ci Cd pipelines through github actions using the data bricks rest API has helped to implement shift left continuous testing forever AI workloads and the AI integrations and clean dashboards has been game changer in this.
What problems is the product solving and how is that benefiting you?
The major problem we had been facing was fragmented test environment and ML models were tested in silos and the test data management was logistical nightmare for us and we lact visibility into the quality of the streaming data feeding via our algorithm hair the databrics has provided us unified Lake house architecture which has solved our test data management issues and gave us escalable version control storage and as a testing leader it has allowed me to enforce strict data governance to automated quality gates across all our Ai pipelines.
It has help to reduce 73.5% production defects as well as the time to market has been reduced by 53.5% due to ML model and ETL testing. It has been a gamechanger with a great pricing and licence support.
It has help to reduce 73.5% production defects as well as the time to market has been reduced by 53.5% due to ML model and ETL testing. It has been a gamechanger with a great pricing and licence support.
Shubhangi J.
Databricks Unifies Data Engineering, Analytics, and ML Seamlessly
Reviewed on Oct 28, 2026
Review provided by G2
What do you like best about the product?
What I like best about Databricks is its ability to bring data engineering, data analytics, and machine learning together in one platform. It makes it easier to process large datasets, build data pipelines, and collaborate using notebooks and SQL. I also like its integration with cloud platforms and its support for scalable data processing, which helps simplify complex data workflows.
What do you dislike about the product?
One thing I dislike about Databricks is that it can be challenging for beginners to understand because of its wide range of features and configuration options. Cluster setup and compute costs can also be difficult to manage if resources are not monitored properly. A simpler onboarding experience, clearer cost tracking, and more beginner-friendly guidance would make the platform easier to use.
What problems is the product solving and how is that benefiting you?
Databricks solves the challenge of processing and managing large volumes of data across different sources. It brings data engineering, analytics, and machine learning into one platform, making it easier to build data pipelines, transform data, and prepare it for analysis. This benefits me by helping me practice real-world data engineering workflows, improve my understanding of cloud-based data processing, and work more efficiently with large datasets.