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.
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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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Dimension | Cost/unit |
|---|---|
Databricks Consumption Units | $1.00 |
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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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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
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
Collaborative Development Environment
Native collaboration capabilities enabling data teams to collaborate across entire data and AI workflow
Open Source Foundation
Built on open source data projects and open standards to maximize flexibility and interoperability with existing data ecosystems
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 and Amazon Comprehend for accelerated AI development.
Large Language Model Connectivity
LLM Mesh capability enabling connections to Amazon Bedrock for Chat, Retrieval-Augmented Generation (RAG), and Agentic workflows.
Visual Analytics and ML 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
Ikhlab H.
Streamlined Data Pipelines, Few Setup Hiccups
Reviewed on Jul 10, 2026
Review provided by G2
What do you like best about the product?
I really love how well Databricks integrates with our existing cloud infrastructure. Connecting to Kafka, cloud storage, and our orchestration tools was set up without needing a lot of custom code, which I find incredibly convenient. Performance-wise, it handles spikes in event volumes during streaming workloads without falling behind, which was initially a concern for us. The MLflow experiment tracking has been a genuine win for our team, making it simple to cooperate on model versions and roll back if needed. It's made our model development much more organized compared to the spreadsheet tracking approach we used before.
What do you dislike about the product?
What I don't like is that governance through Unity Catalog, while powerful in theory, took a lot of trial and error to configure correctly for our permission structure. And the documentation didn't always match what we saw in practice. Pricing for continuous streaming workloads also adds up fast compared to batch jobs, since clusters need to stay running constantly, rather than spinning up for scheduled tasks.
What problems is the product solving and how is that benefiting you?
I use Databricks to solve our latency issue. We now process events as they arrive, making our data only minutes old instead of hours. This real-time capability has greatly benefited our team.
Rachit S.
Premium Notebook Experience That Unifies ML and Data Engineering
Reviewed on Jul 09, 2026
Review provided by G2
What do you like best about the product?
The notebook experience is one of the premium features I use very often. It saves me time, and I spend less time on operational tasks compared to actually working productively with data because the user flow is so simplified. The biggest advantage is having ML and data engineering under one roof, so I don’t have to switch between different tools. The support and onboarding are very smooth as well, and the product’s performance is better than its competitors.
What do you dislike about the product?
Nothing as of yet, since it has been a very short time so far. That said, having more product videos would be helpful, especially for users who haven’t used multiple tools before. A bigger community around the product could also be useful in the long run.
What problems is the product solving and how is that benefiting you?
As mentioned, the biggest advantage of this product is having everything in one place, which makes work simpler and more productive. Also, the easy integrations with multiple platforms make it even more usable.
Muaaz H.
Scalable and Efficient, But Cost Management Needs Improvement
Reviewed on Jul 09, 2026
Review provided by G2
What do you like best about the product?
I really like the Databricks workspace because its layout makes it easy to organize notebooks, jobs, and dashboards without things getting cluttered, even as more teams use it. The support and onboarding exceeded my expectations, with our account team helping us set up best practices for cluster configuration early on, saving us a lot of trial and error that most companies face in the first few months. The AI assistant integrated into the workspace is genuinely useful, especially for less experienced SQL users on our team, helping them write and troubleshoot queries much faster than they could on their own.
What do you dislike about the product?
What I don't like is the cost checking is not as transparent as I like since it is easy for a team to spin up a cluster and leave it running without anyone noticing until the bill arrives. There is not a great way to see spend by team or project, without setting up your own tagging and monitoring. Which feels like something that should be simpler out of the box. I also like a clearer default alert when usage spikes unexpectedly.
What problems is the product solving and how is that benefiting you?
Databricks solved our performance issues by running heavy queries faster, making our reporting more reliable for stakeholders who need timely updates.
Aditi S.
Powerful Tool with Intuitive Interface, Demands Organizational Effort
Reviewed on Jul 09, 2026
Review provided by G2
What do you like best about the product?
I appreciate Databricks for its intuitive interface, which is very user-friendly and flexible, making work comfortable. I use it extensively for collaborative work; my colleagues can view, comment on, and improve analyses all in one place, which is really handy. I can also develop production models, train, test, and deploy them efficiently. It offers excellent data control by allowing me to manage who accesses which data and track changes, reducing technical issues and data inconsistency problems while improving regulatory compliance. I also found the initial setup simple, which is a big plus for us.
What do you dislike about the product?
I don't like the idea that scalability can process large amounts of data and generate resources as needed. Furthermore, it requires better governance at scale and demands a significant organizational effort. What I mean is that it requires much more effort than normal, and that's something that should be changed and optimized to avoid so much effort.
What problems is the product solving and how is that benefiting you?
I use Databricks for collaborative work with colleagues, ensuring data consistency and better control. It allows me to focus on models, improve compliance, and manage permissions easily. The user-friendly interface enhances workflow efficiency.
Anonymous
Seamless Web-Based Analytics with Some Learning Curve
Reviewed on Jul 09, 2026
Review provided by G2
What do you like best about the product?
I really like that Databricks is integrated in a web browser, which makes it extremely easy to access. Anyone with access to a web browser and authentication to our data system can do self-service analytics. This is a big improvement from when we used Teradata, which was extremely slow and required downloading an application. Now, with Databricks, everything's in the web browser and that's a major plus.
What do you dislike about the product?
I believe that Databricks can be improved by maybe having a more useful path for beginners. Sometimes it could be a steep learning curve when learning Databricks, understanding what queries do, understanding what's the difference between notebooks, jobs, workspaces, etc.
What problems is the product solving and how is that benefiting you?
Databricks solves our problems by providing everything needed for data transformation, processing, analytics, SQL queries, and machine learning models, all in one place.