Sold by: Databricks, Inc.Â
Deployed on AWS
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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.
Overview
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Databricks at AWS re:Invent 2024
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Product video
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/pricingÂ
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 awsmp-onboarding-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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Deployed on AWS
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Pricing
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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 |
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Request a private offer to receive a custom quote.
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Delivery details
Software as a Service (SaaS)
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 awsmp-onboarding-help@databricks.comÂ
For additional training:
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Updated weekly
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
Data Platform Architecture
Unified platform integrating data engineering, analytics, business intelligence, data science, and machine learning on a single architecture
Open Source Foundation
Built on open source data projects with support for open standards and data formats
Lakehouse Infrastructure
Provides a common data management approach using a lakehouse architecture running on Amazon S3
Data Intelligence Engine
Advanced engine capable of interpreting organizational data context and enabling broad data access across teams
Collaborative Workflow
Native collaboration capabilities enabling cross-functional data and AI workflow integration
Data Source Connectivity
Secure connection to multiple AWS data sources including Amazon S3, Amazon Redshift, and Amazon RDS
Elastic Compute Processing
Scalable data and machine learning processing powered by Amazon EKS supporting Python, R, Spark, and multiple programming environments
AI Service Integration
Pre-built workflows integrating with AWS AI services like Amazon SageMaker and Amazon Comprehend
Large Language Model Connectivity
LLM Mesh capability for connecting to Amazon Bedrock to support Chat, Retrieval-Augmented Generation (RAG), and Agentic workflows
Collaborative Analytics Platform
Visual platform enabling distributed creation of advanced analytics with collaboration between technical and non-technical teams
Data Platform Architecture
Enterprise data platform supporting multi-cloud, hybrid cloud, and on-premises data management environments
Security and Governance Framework
Shared Data Experience (SDX) technology providing consistent security and governance policies across data workloads and infrastructures
Multi-Function Analytics
Integrated analytics platform supporting data ingestion, transformation, querying, optimization, and predictive modeling without requiring separate point products
Workload Optimization
Intelligent autoscaling capabilities for dynamically adjusting cloud infrastructure resources based on computational requirements
Data Lifecycle Management
Comprehensive platform supporting data processing across multiple services including Data Warehouse, Machine Learning, and custom analytics environments
Standard contract
No
No
No
Customer reviews
4.1
6 ratings
6 AWS reviews
|
628 external reviews
Star ratings include only reviews from verified AWS customers. External reviews can also include a star rating, but star ratings from external reviews are not averaged in with the AWS customer star ratings.
Shaurya J.
Worth the effort
Reviewed on Aug 01, 2025
Review provided by G2
What do you like best about the product?
Databricks excels at unifying data engineering, analytics, and machine learning into one seamless platform. What I like best is how effortlessly it handles massive data volumes while enabling collaborative development through notebooks. The integration with Apache Spark and the ability to run scalable workloads with ML, SQL, and Python side-by-side makes it a powerhouse for data-driven teams. Its governance and Delta Lake architecture also ensure reliability and security across the data pipeline.
What do you dislike about the product?
While Databricks is incredibly powerful, the learning curve can be steep for non-technical users or teams new to distributed computing. The UI, though functional, can sometimes feel a bit clunky compared to more modern data platforms. Additionally, managing costs in a multi-user environment requires careful governance, especially for teams running large-scale compute-heavy jobs.
What problems is the product solving and how is that benefiting you?
Databricks is helping us break down data silos by centralizing data engineering, analytics, and machine learning into a unified environment. It simplifies handling large datasets, automates ETL processes, and enables real-time analytics and AI-driven insights. As a result, we’ve significantly improved our data pipeline efficiency, reduced time to insights, and empowered both data scientists and analysts to collaborate more effectively using a single platform.
Jay B.
Databricks unifies majority of engineering platforms.
Reviewed on Jul 21, 2025
Review provided by G2
What do you like best about the product?
There are various reasons why I like the Databricks Data Intelligence Platform.
We have been using Databricks as a Unified Platform for All of our Data Workloads (pipelines and models) that encompasses data engineering, data science, analytics and agentic AI.
We love the lakehouse architecture that assists in onboarding traditional data warehousing specialists to Databricks in a fast and scalable way.
We did Unity Catalog upgrade last year that streamlined governance and access control across assets; and did Serverless compute this year that decreased cluster starting/waiting time tremendously.
We have been playing around with Agentic AI space now where we build easy to understand prompts to train agents built on top of datasets. This helps in profiling/slicing/analyzing data by anyone (even a non-technical business person).
BI team has been using Databricks to connect with our Power BI dashboards while Engineering team has been using Databricks to connect with Airflow to create/visualize native DAGs.
Last but not the least, Databricks Notebook concept is awesome.
In the same Databricks notebook, one can have code in multiple languages (Python, Scala, SQL etc.) and each can be flipped at runtime.
Built-in collaborative notebooks with support for multiple languages (Python, Scala, SQL, R) and real-time co-authoring make it easier for teams to iterate together quickly.
We have been using Databricks as a Unified Platform for All of our Data Workloads (pipelines and models) that encompasses data engineering, data science, analytics and agentic AI.
We love the lakehouse architecture that assists in onboarding traditional data warehousing specialists to Databricks in a fast and scalable way.
We did Unity Catalog upgrade last year that streamlined governance and access control across assets; and did Serverless compute this year that decreased cluster starting/waiting time tremendously.
We have been playing around with Agentic AI space now where we build easy to understand prompts to train agents built on top of datasets. This helps in profiling/slicing/analyzing data by anyone (even a non-technical business person).
BI team has been using Databricks to connect with our Power BI dashboards while Engineering team has been using Databricks to connect with Airflow to create/visualize native DAGs.
Last but not the least, Databricks Notebook concept is awesome.
In the same Databricks notebook, one can have code in multiple languages (Python, Scala, SQL etc.) and each can be flipped at runtime.
Built-in collaborative notebooks with support for multiple languages (Python, Scala, SQL, R) and real-time co-authoring make it easier for teams to iterate together quickly.
What do you dislike about the product?
-- Unpredictable costs for small teams. We faced it when one of our agents AI workflow was running for a long time on a very small dataset.
-- Compute + storage based costing means optimization is critical. Every QTR, we have to really keep our Databricks forecasts updated as we don’t want a big deviation between forecast vs. actual.
-- While Databricks supports dashboards (with limitations), it can’t replace BI tools like Power BI, Tableau etc.
-- Compute + storage based costing means optimization is critical. Every QTR, we have to really keep our Databricks forecasts updated as we don’t want a big deviation between forecast vs. actual.
-- While Databricks supports dashboards (with limitations), it can’t replace BI tools like Power BI, Tableau etc.
What problems is the product solving and how is that benefiting you?
We have been playing around with Agentic AI space now where we build easy to understand prompts to train agents built on top of datasets. This helps in profiling/slicing/analyzing data by anyone (even a non-technical business person).
Business teams are also using Power BI dashboards (revenue, corporate accounting, HR, treasury and commerce) fed from Databricks data now.
Business teams are also using Power BI dashboards (revenue, corporate accounting, HR, treasury and commerce) fed from Databricks data now.
aditya k.
Make your Data solid BRICK with Databricks
Reviewed on Jul 03, 2025
Review provided by G2
What do you like best about the product?
What I like the best about Databricks data intelligence platforms is the uninterrupted integration of data engineering, data science and AI workflows on an single platform, which improves cooperation, robustness and performance for large data and ML projects.
What do you dislike about the product?
The lowest useful aspect of Databricks data intelligence platform is its complication for new users and steep learning state, especially for those without a strong background in spark or distributed computing. Additionally, it can be expensive on a scale, and debugging large workflows can sometimes be challenging due to limited transparency in error tracing.
What problems is the product solving and how is that benefiting you?
Databricks solves the problem of silent data and fragmented devices by providing integrated environment for intelligence platform data engineering, analytics and machine learning. It enables rapid data processing, real -time cooperation, and streamlined workflows, eventually improves decision making and intensifies innovation in teams.
Information Technology and Services
Databricks compute user
Reviewed on Jun 12, 2025
Review provided by G2
What do you like best about the product?
We have been using Databricks compute for our observability product. It’s super efficient and helps scale seamlessly with data
What do you dislike about the product?
Starting cost is high and makes the entry cost for our solution a little bigh
What problems is the product solving and how is that benefiting you?
Helps generate quality metrics
Information Technology and Services
All under one hood
Reviewed on Jun 12, 2025
Review provided by G2
What do you like best about the product?
I like that everything you need is in the same platform.
What do you dislike about the product?
It is a bit too easy to overspend, need billing alerts in the platform.
What problems is the product solving and how is that benefiting you?
We are able to easily ingest, transform, display, and train on all our data, in one place.