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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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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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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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
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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
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Positive reviews
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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
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.
Nishansha N.
Databricks Bridges Data Engineering and ML with Powerful Scaling and Collaboration
Reviewed on Oct 27, 2026
Review provided by G2
What do you like best about the product?
I like the end to end complexity handling by databricks since it has mature that our gap between data engineering pipelines and machine learning workflows are bridged. The photon and auto scaling spark drastically simplified our large scale computing and also the required custom clusters via kubernetes. Collaborative notebook environment with supports multi language python sequel and scala, and allowed our data engineers to pair program and debug pipeline logic real time which has contributed to reduce total time investigating by 78.2% which is a great thing.
What do you dislike about the product?
There are no major dislikes I love the platform and the integrations it provides us with cloud and our data bases secondly they have great pricing range which is going perfectly with our company vision. Talking about improvements I feel that the cic integration using databrikcs bundles required steep learning which could be difficult that's all which I have as an improvement.
What problems is the product solving and how is that benefiting you?
Databricks helps us to eliminate the unstructured data before using databricks we were using RAW elementary and unstructured training data sets lived in isolated S3 buckets with analytical reported by databricks we were able to unify these layers into single Lake house giving both BI and AI teams a single source of truth which was a great thing for us. Second it has given us AI readiness one hour production servers with deep rooted LLM learning models so these are things which which we have used databricks for.
Anonymous
Secure, User-Friendly Data Platform—But LLM Costs Add Up
Reviewed on Oct 27, 2026
Review provided by G2
What do you like best about the product?
I use Databricks for Unity Catalog and the infrastructure to handle all our data streams and stitch them together. I like its security, user-friendliness, and all-in-one platform.
What do you dislike about the product?
I find the LLM DBU costs can add up quickly and are noticeably more expensive than personal subscriptions, though I understand it's a different business model. I also wish Databricks managed context a little more intuitively.
What problems is the product solving and how is that benefiting you?
I use Unity Catalog and Databricks’ infrastructure to handle all our data streams and stitch them together for managing university data.
Ulrik S.
Unified Data Workflows, Powerful Automation—But Cost Tracking Needs Work
Reviewed on Oct 27, 2026
Review provided by G2
What do you like best about the product?
I use Databricks daily, and it’s integrated into everything—from data ingestion with Auto Loader from S3 and ADLS to transformations with Spark and Delta Live Tables. Now our whole team works on the same notebooks, jobs, and tables with version control. I really like the API and Terraform provider for managing clusters, jobs, and permissions with version-controlled code. Delta Live Tables also lets me create declarative pipelines with built-in expectations and data quality checks instead of programming validations manually. The initial setup was easy.
What do you dislike about the product?
I need better alerts and expense tracking broken down by job, team, and user.
What problems is the product solving and how is that benefiting you?
Databricks replaced our separate environments, versions, and scripts; now my whole team works on the same notebooks, jobs, and tables, all with version control.