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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Buyer guide
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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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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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Support
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
10
In Databases & Analytics Platforms, ML Solutions, Data Analytics
Top
10
In ML Solutions
Top
10
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
No
No
No
Customer reviews
Diego Pablo
Data workflows have become faster and now integrate diverse sources for timely analytics
Reviewed on Oct 24, 2026
Review provided by PeerSpot
What is our primary use case?
Databricks was utilized by our team when we needed to undergo a comprehensive change in the data area and structure the architecture. Day to day, I run queries on the database, fetching data and information using SQL, so that in the end we have what the stakeholders asked us for, and then we productize that, pushing it to Looker or a data visualization tool that the company wants or needs.
The whole change of platforms within a company is quite challenging, and when we made the switch to Databricks, the difference was huge.
How has it helped my organization?
Databricks helps the company as a whole integrate data from different sources, automate processes, and make up-to-date information available for analysis.
The reduction of rework is a huge gain, and data refresh now takes one hour or less with Databricks, representing a reduction of almost seventy-five percent or more in time.
What is most valuable?
The best features that Databricks offers are data integration and transformation, running analyses with SQL and Python, processing large volumes of data with Spark, automating pipelines, collaborating in notebooks, and developing AI and Machine Learning solutions with centralized governance and access control.
Pipeline automation in my day-to-day makes things easier by scheduling and running data tasks, like collecting information, transforming datasets, and updating tables, which greatly reduces manual work.
Databricks' Artificial Intelligence features are useful, helping in areas such as assisting with code creation and speeding up queries, and in terms of governance and security, access control and traceability help a lot.
The AI features are useful for gaining productivity, but the reliability of results depends on data quality, so it is very important to review the generated code and validate the results before using them in company decisions.
What needs improvement?
Databricks could be improved with a more intuitive interface, clearer error messages, and practical guidance that also helps a lot when configuring pipelines.
For how long have I used the solution?
I have been using Databricks for three years.
What do I think about the stability of the solution?
Databricks is stable.
What do I think about the scalability of the solution?
Databricks offers very good scalability, allowing you to increase or reduce processing resources according to the volume of data and demand.
How are customer service and support?
I have not had any problems with customer support, as the few times we needed it, it was resolved in a timely manner.
I would rate customer support a nine.
Which solution did I use previously and why did I switch?
Before, I used Snowflake, and we switched mainly due to costs.
What was our ROI?
While I do not have much information, I can see that the data team has improved significantly with Databricks.
What's my experience with pricing, setup cost, and licensing?
I have had a very good experience with pricing, as there was a drop in costs compared to the previous tool.
Which other solutions did I evaluate?
The idea was only Databricks, already due to the partnership.
What other advice do I have?
I would rate Databricks a nine because it makes integration easier and brings agility and productivity; to reach ten, I see room to make the experience more intuitive.
I would advise others to run a trial first and use the tool because it is reliable, good, scalable, works very well, and has many features built in.
Brian M.
Easy to Use with Agents
Reviewed on Oct 22, 2026
Review provided by G2
What do you like best about the product?
It’s easy to use, and we have good access to support from our Databricks support and account team. The documentation is also clear and straightforward.
What do you dislike about the product?
Onboarding can have more friction than I’d prefer.
What problems is the product solving and how is that benefiting you?
It’s helped me solve data accessibility and infrastructure availability for data analytics and data science pipelines. I honestly wouldn’t have known where to start with building an ML model without Databricks.
Jean M.
Powerful Data Sharing and Scaling, with Cost and UI Trade-Offs
Reviewed on Oct 22, 2026
Review provided by G2
What do you like best about the product?
I like that Databricks lets me apply complex transformations and rely on a single source of truth. I value being able to share data with my team so they understand my business logic. Automatic scaling is valuable at the end of each quarter for in-depth customer churn and upsell analysis, and it helps high-volume processes run quickly without the system crashing. Setup was very easy in our environment.
What do you dislike about the product?
I think Databricks’ interface could be improved because it seems too focused on machine learning and generic analytics. I also have to keep a close eye on costs, especially toward the end of the billing cycle, because usage can increase significantly.
What problems is the product solving and how is that benefiting you?
I use Databricks to consolidate customer data across systems, apply complex transformations, and rely on a single source of truth. I can share data so my team understands my business logic, and automatic scaling helps with quarter-end churn and upsell analyses and keeps high-volume processes running quickly without crashing.
Matthieu D.
One Unified Workspace for Notebooks, SQL, and Delta Lake
Reviewed on Oct 21, 2026
Review provided by G2
What do you like best about the product?
I like having notebooks, SQL, and Delta Lake combined in a single environment. It’s excellent because I can move from exploring data to building a production process without needing to switch tools. I also appreciate the integration with BI and ML tools, which makes it easier to connect analysis and modeling in the same workflow.
What do you dislike about the product?
What I don’t like is that the initial setup—permissions, environments, and conventions—ends up being very complex, more than I expected, especially in companies with many teams and sensitive data.
What problems is the product solving and how is that benefiting you?
Overall, Databricks organizes data much better. It really excels when you’re working with multiple sources, large volumes, and diverse teams that all need to consume the same information. It’s excellent—I hope it stays that way.
Gota N.
Databricks Boosted Delivery Speed and Team Collaboration
Reviewed on Oct 20, 2026
Review provided by G2
What do you like best about the product?
The best thing that I admire about Databricks is the strong data expertise that it has provided us, also they have helped us to bridge the gap between our engineering and. ML along with BI tracks. It's has helped us to speed up the delivery timelines by 39.89% Also With the ETL pipelines has helped us to deploy without upfront infra provisioning. We have seen a change in collaboration since teams are able to collaborate better by 58% since it has vc integrations and multi language support such as (Pyspark, SQL & Scala).
What do you dislike about the product?
There are things which I feel that could be improved in Databricks, first thing is the consumption based model can escalate quickly and it is something not suitable for small and mid businesses. Second is learning curve it is hard to learn and it has complex architecture involved in it. Third, the integrations and dashboards can be better and more intuitive.
What problems is the product solving and how is that benefiting you?
Databricks has helped us to manage the pipelines across independent data warehouses & helped us to separate ML tools and varied our storage buckets. It has also helped us to simplify our sprint road maps and shortened our sprint to production cycles by 67%. And Delivery has been boosted by 56%.