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.
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.
Details
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Deployed on AWS
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Pricing
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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 |
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No refunds
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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.
Resources
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.com
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
|
218 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.
Computer Software
one of leaders in data
Reviewed on Oct 02, 2025
Review provided by G2
What do you like best about the product?
What I really like about the Databricks Data Intelligence Platform is how it brings everything together in one place. Instead of juggling different tools for data engineering, analytics, machine learning, and governance, you can do it all in a single environment.
What do you dislike about the product?
Honestly, what I find a bit frustrating about the Databricks Data Intelligence Platform is that while it’s incredibly powerful, it can also feel overwhelming at times. There’s a steep learning curve, especially for teams who are just getting started and don’t have much experience with Spark or distributed systems.
What problems is the product solving and how is that benefiting you?
The big problem it solves is breaking down data silos. Instead of having separate systems for raw data, analytics, and machine learning, Databricks gives you one platform where everything connects. That means less time moving data around and more time actually using it.
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.
Lokesh L.
Brings together data engineering, analytics & machine learning into a single integrated platform.
Reviewed on Aug 19, 2025
Review provided by G2
What do you like best about the product?
Databricks brings together data engineering, analytics, and machine learning into a single, integrated platform, reducing the need for separate tools and simplifying workflows.
What do you dislike about the product?
Some users find the platform challenging to learn, especially for those unfamiliar with distributed computing or specific Databricks features.
What problems is the product solving and how is that benefiting you?
Databricks reviews mention its Delta Lake architecture and governance features help ensure data reliability and security.
prateek k.
Best Collaborative platform for data engineer, analyst and scientists
Reviewed on Aug 05, 2025
Review provided by G2
What do you like best about the product?
Easy to use, it provides one under umbrella platfrom where different teams collborate their work together, which is very helpful for development and data sharing.
What do you dislike about the product?
as of now i dont find any issues, but we can improve on unity catalog side.
What problems is the product solving and how is that benefiting you?
We have different pipelines in databricks, we are utlisinf it for getting spark benifts and colloborative developement and data sharing between teams.
Abhi J.
Unlocking Scalable Data Insights with Databricks
Reviewed on Jul 24, 2025
Review provided by G2
What do you like best about the product?
Databricks excels in unifying data engineering, analytics, and machine learning in a collaborative, cloud-based environment. Its support for multiple programming languages (Python, SQL, Scala, R) makes it incredibly flexible. The Lakehouse architecture simplifies data management by combining the best of data lakes and data warehouses. The auto-scaling compute clusters, tight integration with tools like MLflow, and powerful notebooks streamline experimentation and production deployment. I also appreciate the frequent product updates and commitment to open-source technologies like Apache Spark and Delta Lake.
What do you dislike about the product?
While powerful, Databricks has a learning curve—especially for non-technical users or those new to Spark-based architectures. Pricing can escalate quickly if not closely monitored, particularly with always-on clusters. The UI, although improving, still feels unintuitive in certain areas (like managing jobs or cluster permissions). Some integrations, especially with on-premise systems, require additional effort or custom workarounds.
What problems is the product solving and how is that benefiting you?
Databricks addresses the fragmentation between data engineering, data science, and analytics by offering a unified platform. Previously, we struggled with maintaining multiple disconnected tools for ETL, machine learning, and BI. Databricks' Lakehouse architecture allows us to manage structured and unstructured data in a single place, simplifying our data pipelines and reducing operational overhead.
It also improves collaboration across teams—data engineers, analysts, and data scientists can work together in shared notebooks with version control and built-in visualizations. With Delta Lake, we now have ACID-compliant data reliability and time-travel capabilities, which help ensure data quality and reproducibility.
As a result, project delivery times have decreased, and our ability to iterate quickly on models and reports has improved significantly—leading to faster business insights and better data-driven decision-making.
It also improves collaboration across teams—data engineers, analysts, and data scientists can work together in shared notebooks with version control and built-in visualizations. With Delta Lake, we now have ACID-compliant data reliability and time-travel capabilities, which help ensure data quality and reproducibility.
As a result, project delivery times have decreased, and our ability to iterate quickly on models and reports has improved significantly—leading to faster business insights and better data-driven decision-making.