Sold by: Databricks, Inc.Â
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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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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
|
621 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.
Naga Likhita C.
A game changer for handling large data
Reviewed on Sep 10, 2025
Review provided by G2
What do you like best about the product?
Databricks has made working with massive datasets so much easier for our team. The collaborative notebooks help us share ideas and troubleshoot together, and the platform’s ability to scale means we don’t have to worry about hitting limits. It’s sped up our analytics and machine learning projects, and connecting to different data sources is a breeze.
What do you dislike about the product?
The initial setup was a bit confusing, and some of the advanced features could use better documentation. Figuring out the pricing took some time, but once we got going, the benefits were clear.
What problems is the product solving and how is that benefiting you?
Databricks is helping me tackle the challenge of working with huge amounts of data for analytics and machine learning. Before using the platform, processing and distributing big datasets was slow and complicated. Now, I can run large-scale data science experiments and build models much faster. The platform’s tools make it easier to collaborate, share results, and turn data into insights that actually help my team make better decisions.
Marketing and Advertising
Power of lakehouse to support AI
Reviewed on Sep 09, 2025
Review provided by G2
What do you like best about the product?
The Databricks Data Intelligence Platform is a unified, AI-native environment that brings together data engineering, analytics, governance, and machine learning on top of the Lakehouse architecture. Its strength lies in combining open data formats with centralized governance via Unity Catalog and embedding intelligence through DatabricksIQ, which allows enterprises to securely connect their data with large language models. From an evaluation standpoint, the platform’s value is in enabling organizations to not only manage and analyze data at scale but also to operationalize generative AI use cases in a governed and collaborative manner.
What do you dislike about the product?
Current stage of evolving technology may require rebuilding on future if adapted by enterprise
What problems is the product solving and how is that benefiting you?
Across industries, the platform solves problems like fraud detection, personalization, compliance, predictive analytics, and AI-driven customer engagement—all by combining data unification, governance, and machine learning in one environment.
Varun T.
A Game-Changer for Data Teams
Reviewed on Sep 09, 2025
Review provided by G2
What do you like best about the product?
I am using data bricks from around 1 year. In a week I use it approx 3-4 days. Everything is integrated, which means I don’t have to switch between multiple tools to do different tasks. It really improves team collaboration. Sharing notebooks and collaborating on models is super easy. This has been great for our team since we often work together on projects and need to see each other's code and progress.
What do you dislike about the product?
This is probably the biggest downside. Databricks can get pretty expensive, especially if you’re not monitoring your usage carefully. It’s definitely a platform that’s best suited for teams with a larger budget or organizations that need heavy-duty data processing.
What problems is the product solving and how is that benefiting you?
Managing and processing huge volumes of data used to be a huge headache. As our data grew, our previous tools were struggling to keep up. Databricks has solved this by providing a robust, scalable infrastructure that automatically adjusts to handle large datasets. Whether it’s batch processing or streaming data, we no longer worry about performance issues when scaling up.
Kanishq G.
A blend of AI powered platform for data management
Reviewed on Sep 05, 2025
Review provided by G2
What do you like best about the product?
>Leverage AI features like LLM and Generative AI to manage data intelligently and generate actionable insights
> Powerful data management capability like ETL , data processing to generate clean data
> Easy to implement pipelines and a great customer network
> Powerful data management capability like ETL , data processing to generate clean data
> Easy to implement pipelines and a great customer network
What do you dislike about the product?
> Use can be limited to a specific use case
> Limited number of connectors to integrate data to other BI tools for reporting purpose
> Support network can be tedious
> Limited number of connectors to integrate data to other BI tools for reporting purpose
> Support network can be tedious
What problems is the product solving and how is that benefiting you?
> Security of data and systems using AI features
> Scalable solutions for managing data as data lake and AI features can be leveraged
> Scalable solutions for managing data as data lake and AI features can be leveraged
Omar J.
It's super handy for analytics, scales well, and you can easily rely on it.
Reviewed on Aug 26, 2025
Review provided by G2
What do you like best about the product?
The best thing about Databricks is that it very easily consolidates data engineering, data science, and analytics – all in one place Therefore, I can process huge data sets quickly, run very complex machine learning operations, all without switching tools. Collaboration through notebooks with my team in real-time really reduced a lot of back and forth that I used to have.
What do you dislike about the product?
The big issue is pricing can quickly ramp up if I’m not careful with cluster size or if I forget to turn them off. I had to learn how to use some of the more advanced features, like Unity Catalog and MLflow connectors if I was to use them well. The only thing I would add is an easy interface, like in some BI products. It may be overwhelming for the newbie.
What problems is the product solving and how is that benefiting you?
I can construct fraud detection models, run queries over billions of rows, and test proof-of-concepts for clients all on one platform. This cost me days in setting work up. Huge workloads on peak hours will not slow me down as I can scale my computing power instantly. This is why I think Databricks gives me more freedom to play around with data models and machine learning at scale as compared to Snowflake or any other alternative.