Sold by: Databricks, Inc.
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.
4.6
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 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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Dimension | Cost/unit |
|---|---|
Databricks Consumption Units | $1.00 |
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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 awsmp-onboarding-help@databricks.com
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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
Lakehouse Architecture
Built on a lakehouse foundation providing unified data storage and governance across data engineering, analytics, BI, data science, and machine learning workloads
Open Source Integration
Constructed on open source data projects and open standards to maximize flexibility and interoperability across the data ecosystem
Data Intelligence Engine
Powered by a Data Intelligence Engine that enables organizational access to data and insights across diverse user roles and technical skill levels
Unified Data Platform
Consolidates data, analytics, and AI workloads on a single common platform running on Amazon S3, eliminating traditional data silos
Collaborative Capabilities
Provides native collaboration features enabling data teams to work together across the entire data and AI workflow
AWS Service Integration
Secure connectivity to Amazon S3, Amazon Redshift, and Amazon RDS with push-down computation capabilities
Elastic Compute Scaling
Distributed processing powered by Amazon EKS supporting Python, R, Spark, and other frameworks for data and ML workloads
Pre-built AI Workflows
Integration with AWS AI services including Amazon SageMaker and Amazon Comprehend for accelerated AI development
Large Language Model Integration
LLM Mesh connectivity to Amazon Bedrock enabling Chat, RAG, and Agentic workflow capabilities
Visual Development Interface
Low-code visual platform for data preparation, pipeline creation, and machine learning model development accessible to both technical and non-technical users
Workload Auto-scaling
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 capabilities with unified analytic functions to eliminate data silos.
Shared Data Experience (SDX)
Implements security and governance policies that are set once and applied consistently across all data and workloads, with portability across supported infrastructures.
Data Lifecycle Management
Manages complete data lifecycle functions including ingestion, transformation, querying, optimization, and predictive analytics across multiple cloud environments.
Unified Security and Governance
Ensures all workloads share common security, governance, and metadata with capabilities for data discovery, curation, and self-service access controls.
Standard contract
No
No
No
Customer reviews
Vikash M.
All-in-One Platform with Collaborative Notebooks and Fast, Scalable Processing
Reviewed on May 30, 2026
Review provided by G2
What do you like best about the product?
What I like most about Databricks is how it brings data engineering, analytics, and AI into one platform. The collaborative notebooks, fast processing, and easy scalability save a lot of time, especially when working with large datasets and multiple teams on shared projects.
What do you dislike about the product?
Databricks can become expensive quickly, and cluster management still feels confusing for beginners sometimes.
What problems is the product solving and how is that benefiting you?
Databricks simplifies large-scale data processing, analytics, and AI workflows, helping me save time, collaborate better, and manage massive datasets more efficiently.
Entertainment
An All-in-One Platform for Data, Analytics, and Machine Learning
Reviewed on May 29, 2026
Review provided by G2
What do you like best about the product?
I really value that this platform supports everything from raw data ingestion and SQL analytics to machine learning with notebooks. It’s not just another external tool; it feels like a fully integrated solution for an entire organization. I also appreciate that it’s designed to support both technical and business users.
What do you dislike about the product?
Managing costs and optimizing cluster usage can sometimes be challenging and requires internal knowledge of the underlying architecture, such as CPU and RAM configuration for jobs. This can significantly impact the overall budget, especially for small companies.
What problems is the product solving and how is that benefiting you?
Databricks helps us process millions of records daily in a reasonable amount of time while maintaining scalability for our solutions. It also allows us to build and integrate solutions not only within Databricks itself, but also by deploying external packages. In addition, the command-line tools provide flexibility to integrate with our current CI/CD workflow, helping us reduce deployment times.
reviewer2846955
Web-based SQL workflows have become more secure and have saved significant query time
Reviewed on May 28, 2026
Review provided by PeerSpot
What is our primary use case?
My main use case for Databricks is running SQL queries. I use Databricks in my day-to-day work by doing SQL queries directly in Databricks using the Genius platform to better correct the queries instead of doing queries on another platform.
What is most valuable?
The best features offered by Databricks include the fact that it is on the web, that it does not depend on installing any software, and most importantly, the security that prevents connection to anyone else who is not logged in.
Regarding the security and web access I mentioned, I have noticed concrete benefits related to collaboration and data protection within my team, such as it being very secure and the fact that every time we enter the platform, it does the same credential verification.
The features of Databricks have impacted my organization positively, as it has done so very efficiently since we switched from several platforms to using this one. After implementing Databricks in my organization, I have observed that it has been more efficient with my team.
What needs improvement?
I think the aspects of Databricks that should be improved are that it could be faster and that I would like to be able to run direct queries from the server. I have not seen any other improvements that I think are needed in Databricks.
What do I think about the stability of the solution?
Databricks is stable.
What do I think about the scalability of the solution?
I rate the scalability of Databricks as excellent.
How are customer service and support?
Databricks customer support is very good. I would give Databricks customer support a rating of ten.
Which solution did I use previously and why did I switch?
I did not use any other solution before Databricks.
What was our ROI?
I have seen a return on investment, as time is greatly saved and processes are faster.
What's my experience with pricing, setup cost, and licensing?
My experience with pricing, implementation costs, and licensing is that it is very efficient and very fast.
What other advice do I have?
My advice to others considering using Databricks is that it is the best platform with artificial intelligence. I give this review an overall rating of ten.
Lokesh S.
A Game Changer for Unifying Data Engineering and ML, but Watch Your Compute Costs
Reviewed on May 28, 2026
Review provided by G2
What do you like best about the product?
As a Senior Data Scientist at a mid-sized tech company, I've used Databricks for the last couple of years, and it has really transformed our data teams. The main use case we want to process large amounts of user interaction data to create predictive models, namely customer churn, recommendation engines, and customer lifecycle value (LCV) estimation. Prior to Databricks, our workflows were very disjointed. The data engineers utilized one set of complicated tools for ETL tasks and the data scientific research group utilized completely various neighborhood environments for modelling. Databricks gave everyone a common platform and workspace, a cloud-based experience, which put everyone together under one roof.I like the ability to work with collaborative notebooks together with great computing at the same time the most. It's a significant productivity win to be able to code in Python, SQL and Scala — and, vitally, do so in the same environment as the data engineers who are creating the core pipelines. Additionally, I find the out-of-the-box integration with MLflow to be a game-changer in my workday routine. It eliminates the pain of managing version registries, tuning parameters, and more complicated model experiments. I have to also point out the ease with which they have improved cluster management. As a data scientist, I need to use a heavy machine learning model, one that is not always in use for the duration. I can start a distributed, powerful Spark cluster in a few clicks, train my model on it, and then quickly configure it to automatically kill itself after the job completes - I do not want to waste resources.
What do you dislike about the product?
The platform doesn't have exceptional user-friendliness, though, and there are some drawbacks that you'll need to navigate with care. The greatest disadvantage is the loss of control of spending if you're not careful. With so much of the complicated back-end infrastructure abstracted away, it's quite easy for a newer team member to provision an unnecessarily large compute cluster or forget to switch on auto-termination, with a very unpleasant surprise on the monthly billing statement. One must be careful about creating rigid rules for use of the workplace and tracking how it is used. Moreover, it can be unpredictable to learn the learning curve of a distributed computing paradigm that is different from the one analysts or data scientists already have experience with, such as Apache Spark. They have come a long way in introducing features that are similar to the standard Python library but for complex distributed errors, a lot of knowledge about the inner workings is still needed for debugging. The user interface can also sometimes be a bit slow and cumbersome when working with deep levels of workspace folders in which there are hundreds of notebooks from the legacy version.
What problems is the product solving and how is that benefiting you?
As for real-world problems solved, Databricks did away with that well-known situation of having a predictive model I love on my laptop, but not at all in the production environment. We standardized our runtime environments throughout the entire organization and set up MLflow for deployment, saving us many painful weeks to only a couple of days for our models to go to production. Yet another huge success for our company is that we removed the silos among our departments. We do not now throw a clean data table over a metaphorical wall for me to analyse second thoughts because I am not a data engineer. When I recently had to work with a complex and custom feature that was created for an algorithm that recommends products for users, I wrote this feature with the data engineering lead in a common notebook on Databricks, and we tested and optimized this pipeline together. Historically, we would have deployed it the wrong way the first time, but that wouldn't have worked with our previous infrastructure! It has really made our team a very effective cross functional team!
Base. B.
Databricks Unifies Teams with Strong IaC, Streaming, and Git Integration…!!!
Reviewed on May 27, 2026
Review provided by G2
What do you like best about the product?
I like Databricks since it has improved collaboration between our data science and data engineering teams by bringing their workflows onto one platform.Its also the best since it offers us with a complete Terraform provider for managing infrastructure as code makes streaming data processing straightforward and integrates with multiple Git providers with a built-in merge assistant to simplify version control.
What do you dislike about the product?
I have no complain regarding Databricks.
What problems is the product solving and how is that benefiting you?
Databricks streamlines data processing and analytics by unifying them on a single platform.