Sold by: Dataiku
Deployed on AWS
Accelerate Enterprise AI with Dataiku on AWS
4.4
Overview
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Dataiku 3-Minute Demo
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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
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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.
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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
Pavan B.
Easy No-Code Visual Flows That Connect Seamlessly to Native Servers
Reviewed on Sep 01, 2026
Review provided by G2
What do you like best about the product?
No-code or low-code visual flows that are easy to connect to native servers.
What do you dislike about the product?
The license cost is expensive, and it also requires heavy infrastructure to handle heavy flows.
What problems is the product solving and how is that benefiting you?
Operational tracking and clinical trial analytics.
Rythm G.
All-in-One Data Science Platform That Streamlines Workflows and Collaboration
Reviewed on Aug 31, 2026
Review provided by G2
What do you like best about the product?
What I like most about Dataiku is that it combines data preparation, analysis, visualization, and machine learning in a single platform. The visual workflow makes it easy to build and follow data pipelines without needing to write code for every step, while still offering flexibility for people who prefer working in Python or SQL. I also find its integrations with different data sources helpful, and the interface makes it straightforward to collaborate with others and share workflows. Overall, it streamlines the data science process and reduces the need to switch between multiple tools.
What do you dislike about the product?
One area that could be improved is the learning curve for some of the more advanced features. Although the visual interface is helpful, it can still take new users a while to understand how the different components and workflows fit together. Some of the advanced integrations and AI features would also benefit from clearer documentation, along with more beginner-friendly examples that show how to use them in practice. Performance can occasionally vary depending on the complexity and size of a workflow, so more guidance on optimizing larger projects would be useful as well.
What problems is the product solving and how is that benefiting you?
Dataiku simplifies the end-to-end data science workflow by bringing data preparation, analysis, visualization, and machine learning into a single environment. Rather than switching between multiple tools at different stages of a project, I can keep workflows and experiments organized in one place. The visual interface is especially helpful for quickly exploring data and building workflows, and the option to use Python or SQL adds flexibility when I need more advanced analysis. Overall, it’s easier to experiment, reproduce workflows, and collaborate on data-driven projects.
dnyaneshwar g.
Amazing Data Ingestion, Analysis, and Interactive Dashboards
Reviewed on Aug 29, 2026
Review provided by G2
What do you like best about the product?
The data ingestion, analysis, and interactive dashboard for visualising the data and presenting it to stakeholders are amazing. The value for money is great, so many data analysis capabilities made Dataiku a perfect solution fit for our problem.
What do you dislike about the product?
I tried integrating the Dataiku APIs into my Python project. It works flawlessly, but it still needs some improvements, and it isn’t that flexible.
What problems is the product solving and how is that benefiting you?
For my organization, Dataiku has helped us analyze well log data, and our decision-making has become much quicker. Reservoir pressure data analysis also helps onsite engineers make faster decisions.
INDRAYUDH B.
Straightforward Visual ML Workflows with Flexible Python and SQL Options
Reviewed on Aug 28, 2026
Review provided by G2
What do you like best about the product?
What I like most about Dataiku is how straightforward it makes working with data and putting together machine learning workflows. After using it for a week, I found the visual interface particularly helpful because it let me explore, clean, and transform data without needing to write code at every step. At the same time, the option to switch to Python and SQL when needed adds a lot of flexibility. Overall, it feels like a solid platform that brings data preparation, analysis, and machine learning together in one place.
What do you dislike about the product?
What I like least about Dataiku is that it can feel a bit overwhelming at first. It offers a lot of features and options, so it takes time to get comfortable with the interface and to figure out the workflow that makes the most sense. For beginners, some tasks can also seem more complicated than they need to be. After using it for a week, I still think it has a lot of potential, but I wish the initial learning curve were smoother.
What problems is the product solving and how is that benefiting you?
Dataiku helps simplify the process of preparing, analyzing, and working with data by bringing everything into one platform. Instead of switching between different tools, I can manage data, create workflows, and experiment with machine learning in one place. This saves time and makes the overall data workflow more organized and easier to manage.
samira Y.
Everything in One Platform
Reviewed on Aug 25, 2026
Review provided by G2
What do you like best about the product?
I like the fact that everything is in one platform.
What do you dislike about the product?
Advanced development requires a lot of learning, and there’s quite a bit to pick up before you feel comfortable with it.
What problems is the product solving and how is that benefiting you?
It solved the problem of fragmented data science workflows by bringing data preparation, analytics, machine learning, deployment, and governance together in one place.