Sold by: Dataiku
Deployed on AWS
Accelerate Enterprise AI with Dataiku on AWS
4.4
Overview
Video
Dataiku 3-Minute Demo
Video
Product video
Dataiku is the Platform for AI Success, the enterprise orchestration layer for building, deploying, and governing AI. In a single environment, teams design and operate analytics, machine learning, and AI agents with the transparency, collaboration, and control enterprises require.
- Data Scientists use familiar tools to focus on high-impact work, with automation and streamlined collaboration.
- Business Analysts get faster insights with intuitive data prep and accessible machine learning.
- Data Teams scale projects with built-in governance and transparency.
Built for AWS
- Connect securely to all data sources, including Amazon S3, Amazon Redshift, and Amazon RDS.
- Scale data and ML processing with Dataiku elastic compute powered by Amazon EKS for Python, R, Spark, and more.
- Accelerate AI development with pre-built workflows integrating AWS AI services, such as Amazon SageMaker and Amazon Comprehend.
- Distributed creation of advanced analytics through its visual platform, fostering greater collaboration between technical and non-technical teams.
- Leverage the Dataiku LLM Mesh to connect to Amazon Bedrock for Chat, RAG, and Agentic workflows.
AI at Scale, Supported Every Step
With expert services and a robust learning platform, Dataiku helps organizations of any size adopt AI at scale - quickly and confidently.
Highlights
- Take full advantage of your investment in the AWS platform with Dataiku's unique push down to Amazon's storage and compute.
- Empower more users to clean and enrich data, build advanced data pipelines and machine learning models in a visual interface.
- Accelerate deployment on AWS, leveraging Sagemaker and Bedrock, with a fully managed service (SaaS) hosted and managed by Dataiku.
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Buyer guide
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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
Pricing is based on the duration and terms of your contract with the vendor. This entitles you to a specified quantity of use for the contract duration. If you choose not to renew or replace your contract before it ends, access to these entitlements will expire.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Description | Cost/12 months |
|---|---|---|
Dataiku | Contact us for pricing | $1.00 |
Dimensions summary
This listing uses a single pricing dimension billed under a contract, measured in Units. Pricing is not published on the pricing table. You contact the vendor to get a quote based on your needs. This structure supports custom scoping rather than fixed tiers or set instance sizes. You negotiate quantity and terms directly. The platform brings analytics, machine learning, and AI agents into one governed system, which the Units dimension covers. Because there is one dimension, all capabilities fall under that single contract arrangement rather than separate add-on charges.
Top-of-mind questions for buyers
The pricing table lists Units as the billing measure but does not define what one Unit maps to, and the exact count is set through a quote. You contact the vendor to confirm how Units are counted for your deployment, since pricing is not published here.
The Units dimension covers the platform that brings analytics, machine learning, and AI agents into one governed system. This includes data preparation, model building, deployment, orchestration, and built-in governance controls like lineage and monitoring. All of these fall under the one contract rather than separate add-on charges.
Yes. The platform connects data, analytics, ML models, and agents across any infrastructure, with support for on-premises, hybrid, and multiple cloud providers. It avoids vendor lock-in and lets you deploy across your enterprise. Discuss on-premises or hybrid setup needs directly with the vendor.
www.dataiku.com+2
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Vendor refund policy
All fees are non-cancellable and non-refundable except as required by law.
Custom pricing options
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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
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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.
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Sentiment is AI generated from actual customer reviews on AWS and G2
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AI generated from product descriptions
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, Amazon Comprehend, and Amazon Bedrock for chat, RAG, and agentic workflows.
Visual Analytics and ML Interface
Visual platform enabling creation of advanced analytics, data pipelines, and machine learning models accessible to both technical and non-technical users.
Governance and Transparency Framework
Built-in governance, transparency, and control mechanisms for managing AI projects, deployments, and team collaboration at scale.
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
Open Source Foundation
Built on open source data projects and open standards to maximize flexibility and interoperability across data ecosystem
Collaborative Capabilities
Native collaboration features enabling unified data teams to collaborate across entire data and AI workflow
Multi-Tool Ecosystem Support
Access to open-source and commercial tools including Jupyter, RStudio, SAS, Anaconda, MATLAB, and distributed compute frameworks like Spark, Ray, Dask, and MPI with one-click integration.
Integrated MLOps Workflows
Built-in workflows and automation for model development, deployment, and monitoring across the entire machine learning lifecycle with enterprise-grade process controls and governance.
Multi-Cloud and Hybrid Deployment
Support for deployment across public cloud, hybrid, and multi-cloud environments with Domino Nexus, enabling workload execution across any compute cluster in any cloud, region, or on-premises.
Model Governance and Reproducibility
Audit-ready platform with turnkey model governance, monitoring, remediation capabilities, and reproducibility controls to satisfy compliance and regulatory requirements.
Seamless Cloud Integration
Native integration with Amazon SageMaker for flexible model deployment and inference, with ability to export models to SageMaker or access SageMaker models within the platform.
Standard contract
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Customer reviews
Michael V.
Highly Informative Keynote—Learned a Lot
Reviewed on Sep 24, 2026
Review provided by G2
What do you like best about the product?
Keynote was very informative and I learned a lot
What do you dislike about the product?
Nothing I thought the whole thing was great
What problems is the product solving and how is that benefiting you?
Too many places to look
Colin U.
Easy Data Sharing, but Another System to Learn
Reviewed on Sep 24, 2026
Review provided by G2
What do you like best about the product?
The ability to easily share and push data to colleagues who are less proficient with data.
What do you dislike about the product?
We use many different systems at work, so this is yet another system we need to train and upskill our people on.
What problems is the product solving and how is that benefiting you?
Faster data acquisition.
Pete W.
Cloud-Based Collaboration That Keeps Data Flowing
Reviewed on Sep 24, 2026
Review provided by G2
What do you like best about the product?
Dataiku is cloud based and collaborative. No longer is work done on one persons laptop but in a collaborative space. This enables continuous data flowing especially when colleagues change roles or go on leave.
What do you dislike about the product?
Prior to cobuild there was a large learning curve for non data focused folks.
What problems is the product solving and how is that benefiting you?
Proactive process monitoring in real time
Sarai A.
Effortless Prototyping That Keeps Us Focused on Data Science
Reviewed on Sep 24, 2026
Review provided by G2
What do you like best about the product?
The ease of prototyping and the ability to focus on the data science rather than the infrastructure
What do you dislike about the product?
It can be buggy and unclear when pipelines fail
What problems is the product solving and how is that benefiting you?
Bringing together business stakeholders and technical into one integrated space
Gerardo E.
Stronger collaboration and less tool fragmentation, but AI needs verification
Reviewed on Sep 15, 2026
Review provided by G2
What do you like best about the product?
I like that it keeps AI systems reliable, scalable, and seamlessly connected to the data they need to perform. It significantly improved collaboration between technical and business users, and it reduced tool fragmentation by bringing more of the AI lifecycle into one platform, making workflows easier to manage and coordinate. The governance capabilities also helped with centralized governance, documentation, and controls to keep AI usage organized and accountable.
What do you dislike about the product?
High costs, complex multi-cloud setups, and overly complicated access controls can make AI expensive, difficult to manage, and harder to secure. Also, the AI sometimes misinterprets the data, so I still need to verify its recommendations before making decisions.
What problems is the product solving and how is that benefiting you?
I’ve used the AI to quickly analyze campaign performance and identify trends. It also made workflows easier to manage and coordinate by reducing tool fragmentation, and it significantly improved collaboration. Governance and documentation provided enough oversight to keep AI usage organized and accountable.