Sold by: Databricks, Inc.
Deployed on AWS
Free Trial
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
Video
Databricks at AWS re:Invent 2024
Video
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/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
Sold by
Delivery method
Deployed on AWS
New
Introducing multi-product solutions
You can now purchase comprehensive solutions tailored to use cases and industries.
Features and programs
Buyer guide
Gain valuable insights from real users who purchased this product, powered by PeerSpot.
Financing for AWS Marketplace purchases
AWS Marketplace now accepts line of credit payments through the PNC Vendor Finance program. This program is available to select AWS customers in the US, excluding NV, NC, ND, TN, & VT.
Pricing
Free trial
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 |
Vendor refund policy
No refunds
Custom pricing options
Request a private offer to receive a custom quote.
Legal
Vendor terms and conditions
Upon subscribing to this product, you must acknowledge and agree to the terms and conditions outlined in the vendor's End User License Agreement (EULA) .
Content disclaimer
Vendors are responsible for their product descriptions and other product content. AWS does not warrant that vendors' product descriptions or other product content are accurate, complete, reliable, current, or error-free.
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:
AWS infrastructure support
AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.
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
Michael A.
End-to-End Data Management with Databricks
Reviewed on Apr 02, 2026
Review provided by G2
What do you like best about the product?
I like the fact that Databricks helps me manage data end to end, from ingestion to analytics to reporting and even governance. Within the platform, I'm able to build my pipelines to integrate and adjust data. I can also build dashboards, create reports, share them with my stakeholders, and ensure that the right people have access to the correct datasets and reports. The initial setup was pretty easy, and taking some training on the Databricks Academy was really helpful.
What do you dislike about the product?
The layout of the view of the portal could be nicer if it was a bit more colorful.
What problems is the product solving and how is that benefiting you?
Databricks solves a lot of problems by helping me build data pipelines, create a central source of truth, and maintain data security.
Thoufeeq A.
All-in-One Powerhouse with Room for Pricing Clarity
Reviewed on Apr 02, 2026
Review provided by G2
What do you like best about the product?
I like that Databricks is an all-in-one powerhouse where I can do multiple works in one place. It's powerful to manage data from multiple sources and have it in a single UC to manage permissions with row-level security. I also appreciate that I can create experiments, run multiple models, and select the best one from logs, which was difficult on other platforms. Once I learned the setup, it's been easy and comfy to work with.
What do you dislike about the product?
I find it difficult to use the calculator to determine CPU serving endpoint prices because the documentation doesn't explicitly explain this. It only mentions 1 concurrency equals 1 DBU on the Azure page, which isn't clear. The pricing calculator has a single option for serving endpoints, labeled as medium with four DBU, but lacks separate options for GPU or CPU and their concurrency, making it hard to understand how it works properly. Initially, I also felt it was very tough to learn Databricks and manage deployments of workspaces, although it became easier over time.
What problems is the product solving and how is that benefiting you?
Databricks consolidates multiple tools into one platform, making it powerful and convenient. I can manage permissions with row-level security and easily run experiments to select the best models, all in one place.
Sivabalan A.
Unified Data Engineering, Science, and Analytics in One Collaborative Platform
Reviewed on Apr 02, 2026
Review provided by G2
What do you like best about the product?
What I appreciate most about Databricks is its ability to unify data engineering, data science, and analytics on a single platform. The collaborative environment—especially the notebooks and integrated workflows—makes it much easier for teams with different skill levels to work together without constant context-switching.
Another highlight is the integration with popular tools and cloud services that are widely used in the market today, which makes it easier to move data between them. The performance monitoring and job scheduling features help maintain visibility over pipelines, and the Delta Lake support for reliable data management has also been very useful.
Another highlight is the integration with popular tools and cloud services that are widely used in the market today, which makes it easier to move data between them. The performance monitoring and job scheduling features help maintain visibility over pipelines, and the Delta Lake support for reliable data management has also been very useful.
What do you dislike about the product?
Cost management is one area that could be improved. While Databricks offers autoscaling and flexible cluster options, it’s easy for resource usage to escalate unexpectedly, especially with large datasets and long-running jobs. Keeping costs predictable often requires careful oversight and a solid understanding of the platform’s pricing model.
Additionally, some of the more advanced features—such as fine-grained access controls and more complex job orchestration—can feel less intuitive. The documentation is extensive, but it occasionally leaves gaps that end up requiring trial and error.
Additionally, some of the more advanced features—such as fine-grained access controls and more complex job orchestration—can feel less intuitive. The documentation is extensive, but it occasionally leaves gaps that end up requiring trial and error.
What problems is the product solving and how is that benefiting you?
Databricks addresses several key challenges in modern data workflows, particularly around scalability, data reliability, and collaborative analytics. One major problem it solves is managing and processing large-scale datasets efficiently. By leveraging Apache Spark’s distributed computing framework, Databricks enables parallelized ETL pipelines and large-scale data transformations that would be impractical on traditional infrastructure.
Another challenge is ensuring data consistency and reliability across pipelines. With Delta Lake, Databricks provides ACID-compliant storage, versioned tables, and schema enforcement, which reduces data errors and simplifies data governance. This is especially beneficial when multiple teams are working on different stages of data pipelines at the same time.
Databricks also helps solve the problem of fragmented workflows for data scientists and engineers. Its unified environment supports multiple languages (Python, SQL, R, Scala) and includes integrated machine learning with MLFlow, making it easier to collaborate and move from data preparation to analytics and ML in one place.
Another challenge is ensuring data consistency and reliability across pipelines. With Delta Lake, Databricks provides ACID-compliant storage, versioned tables, and schema enforcement, which reduces data errors and simplifies data governance. This is especially beneficial when multiple teams are working on different stages of data pipelines at the same time.
Databricks also helps solve the problem of fragmented workflows for data scientists and engineers. Its unified environment supports multiple languages (Python, SQL, R, Scala) and includes integrated machine learning with MLFlow, making it easier to collaborate and move from data preparation to analytics and ML in one place.
Janani D.
Scalable Power with Manageable Trade-offs
Reviewed on Apr 02, 2026
Review provided by G2
What do you like best about the product?
The collaborative notebooks are hands-down my favorite part of Databricks. I love being able to jump into a notebook with my team, tweak Spark SQL queries live on those massive shipment datasets, and watch everything sync instantly—without any version-control.
It beats emailing notebooks back and forth or wrestling with merge conflicts; it feels like pair programming, but for data pipelines. And when you pair that with Delta Lake’s reliability for keeping my ETL jobs rock-solid on intermodal lane data, it ends up being a huge workflow saver.
Top notebook perks for me are the real-time editing and sharing that keeps everyone aligned during debugging, the built-in version history that lets me roll back mistakes quickly, and the seamless Spark integration so I’m not constantly context-switching when doing big data transforms.
It beats emailing notebooks back and forth or wrestling with merge conflicts; it feels like pair programming, but for data pipelines. And when you pair that with Delta Lake’s reliability for keeping my ETL jobs rock-solid on intermodal lane data, it ends up being a huge workflow saver.
Top notebook perks for me are the real-time editing and sharing that keeps everyone aligned during debugging, the built-in version history that lets me roll back mistakes quickly, and the seamless Spark integration so I’m not constantly context-switching when doing big data transforms.
What do you dislike about the product?
One key drawback is the cost management—charges can accumulate rapidly if clusters are left running, requiring careful monitoring of DBU usage and auto-termination settings.
Debugging intricate Spark job failures in notebooks often involves sifting through extensive log output, which extends resolution time considerably. Additionally, the UI experiences occasional performance delays under high workloads, impacting efficiency when responsiveness is essential.
Debugging intricate Spark job failures in notebooks often involves sifting through extensive log output, which extends resolution time considerably. Additionally, the UI experiences occasional performance delays under high workloads, impacting efficiency when responsiveness is essential.
What problems is the product solving and how is that benefiting you?
Databricks addresses core challenges in managing large-scale data processing, such as scalability limitations in traditional databases and the complexity of integrating disparate tools for ETL workflows. It enables distributed Spark processing across clusters to handle massive datasets efficiently, while Delta Lake provides ACID-compliant storage to ensure data integrity amid evolving schemas or concurrent updates.
This benefits me by streamlining pipelines that feed BI tools, reducing processing times from days to hours and minimizing manual infrastructure oversight. Collaborative notebooks further enhance team productivity through real-time editing, eliminating version control issues and accelerating development cycles.
This benefits me by streamlining pipelines that feed BI tools, reducing processing times from days to hours and minimizing manual infrastructure oversight. Collaborative notebooks further enhance team productivity through real-time editing, eliminating version control issues and accelerating development cycles.
Information Technology and Services
Databricks Unifies Data and AI for Effortless ML at Scale
Reviewed on Apr 02, 2026
Review provided by G2
What do you like best about the product?
What I like most about Databricks is how it brings data and AI into one place, so you’re not jumping between tools.
It makes building and scaling ML models feel much more straightforward, especially with built-in experiment tracking.
The integration with Apache Spark helps handle large datasets without extra setup.
Overall, it just reduces the friction between raw data and actually getting useful AI outcomes.
It makes building and scaling ML models feel much more straightforward, especially with built-in experiment tracking.
The integration with Apache Spark helps handle large datasets without extra setup.
Overall, it just reduces the friction between raw data and actually getting useful AI outcomes.
What do you dislike about the product?
One thing I find challenging with Databricks is cost visibility-it can scale quickly, and predicting spend isn’t always straightforward.
There’s also a bit of a learning curve, especially when working across notebooks, jobs, and cluster configs.
And for simpler use cases, it can feel like overkill compared to lighter-weight solutions.
There’s also a bit of a learning curve, especially when working across notebooks, jobs, and cluster configs.
And for simpler use cases, it can feel like overkill compared to lighter-weight solutions.
What problems is the product solving and how is that benefiting you?
Databricks solves the problem of fragmented data and AI workflows by bringing everything-data engineering, analytics, and ML-into one platform.
It eliminates the need to move data across multiple systems, which reduces latency and pipeline complexity.
For me, that means faster experimentation and smoother deployment of AI models without worrying about infrastructure.
Overall, it helps focus more on solving business problems rather than managing tools.
It eliminates the need to move data across multiple systems, which reduces latency and pipeline complexity.
For me, that means faster experimentation and smoother deployment of AI models without worrying about infrastructure.
Overall, it helps focus more on solving business problems rather than managing tools.