Palantir Technologies vs Dataiku
Updated onCompare Palantir Technologies and Dataiku side-by-side. See how they stack up on features, pricing, and target market.
Palantir Technologies
Provider of enterprise data integration, analytics, and AI software (notably Gotham and Foundry) for government and commercial customers.
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Dataiku
Dataiku is an enterprise AI and analytics platform that enables organizations to collaboratively build, deploy, and govern machine learning, analytics, and generative AI applications at scale.
Starts at $0
Which should you choose?
Palantir Technologies
Choose Palantir if you are a large, highly regulated or government organization that needs a secure, ontology-driven operating system to connect data and AI directly to mission-critical operational decision-making.
Dataiku
Choose Dataiku if you want a more open, collaborative enterprise AI and analytics platform that can start with a free edition and quickly scale across business users and data scientists on your existing cloud and data stack.
Key differences
| Category | Palantir Technologies | Dataiku | Why? |
|---|---|---|---|
| Primary Focus & Ideal Use Cases | Palantir is optimized for mission-critical, operations-centric scenarios in defense, government, and highly regulated industries, while Dataiku focuses on broad enterprise AI and analytics use cases across many commercial sectors. | ||
| Ease of Use & User Personas | Both platforms support no-code and full-code workflows, but reviews consistently describe Dataiku as especially approachable for analysts and “citizen” developers, whereas Palantir is powerful yet more complex and often relies on specialized Palantir engineers for implementation. | ||
| Implementation Model & Services | Palantir frequently embeds Forward Deployed Engineers and runs intensive AIP bootcamps to accelerate time-to-value, whereas Dataiku follows a more traditional software-plus-services model with training and partners but less embedded engineering. | ||
| Pricing & Entry Barrier | Dataiku offers a clearly defined free edition, cloud trials, and tiered paid editions that let smaller teams start quickly, whereas Palantir typically sells larger customized deployments with less transparent, higher-end enterprise pricing. | ||
| Security, Governance & Regulated Environments | Palantir is built for sensitive defense and national-security deployments with stringent security and governance, while Dataiku emphasizes strong governance for commercial and regulated industries but is not primarily positioned for defense-grade environments. |
Feature comparison
| Feature | Palantir Technologies | Dataiku | Notes |
|---|---|---|---|
| Advanced ML & AutoML for data scientists | Each platform supports advanced machine learning workflows and automation, with Palantir positioned as an AI/ML leader and Dataiku recognized as a top data science and ML platform. | ||
| Built-in Generative AI & LLM tooling (agents, RAG, LLM gateway) | Palantir’s AIP and Dataiku’s GenAI & Agents capabilities both provide tooling to run LLMs on private data, build AI agents, and manage GenAI applications with governance. | ||
| Centralized AI governance and MLOps | Foundry and AIP provide built-in security, lineage, and model lifecycle controls, and Dataiku similarly embeds governance, monitoring, and MLOps capabilities across analytics and AI projects. | ||
| On-premises, private cloud, and SaaS deployment options | Palantir’s platforms, supported by Apollo, and Dataiku’s editions can be deployed on customers’ infrastructure, major clouds, or as fully managed SaaS offerings. | ||
| Prebuilt industry use case templates | Palantir offers industry-specific solutions and prebuilt AIP use cases, while Dataiku ships ready-to-use industry scenarios and templates for sectors like manufacturing, banking, life sciences, and retail. | ||
| Visual, no-code data preparation and pipelines | Palantir Foundry and Dataiku both provide visual interfaces and low-code tools for building data pipelines and transformations end-to-end. | ||
| Extensive third-party data platform integrations | Both tools integrate with external data sources, but Dataiku explicitly lists dozens of connectors across clouds, warehouses, and BI tools, whereas Palantir emphasizes integration mainly within its own operating-system stack. | ||
| Free edition / self-service trial | Palantir provides a free Foundry developer tier for eligible users, whereas Dataiku advertises an installable Free Edition and a 14‑day free cloud trial. | ||
| Government/defense-grade security and accreditation | Palantir is widely used in defense and national-security contexts and designed for highly sensitive data, while Dataiku focuses more on commercial governance and compliance than on classified defense environments. | ||
| High-touch professional services / embedded engineers | Palantir is known for Forward Deployed Engineers and intensive engagement models, while Dataiku offers training, support, and a strong partner ecosystem but less embedded engineering by default. |
Review Consensus
Palantir Technologies
"Across review sites, Palantir is seen as a very powerful, secure end-to-end data and AI platform with excellent embedded support, but comes with high cost and some limits to openness and ease of industrialization."
Based on 24 reviews
- ● End-to-end tools for data ingestion, pipeline building, monitoring, and analytics in one integrated platform.
- ● Low-code interfaces allow analysts to work productively while still enabling code-based customization for engineers.
- ● Users often praise overall ease of use once trained and the responsiveness of Palantir support.
- ● Some reviewers feel customization options are limited compared with more open or modular stacks.
- ● Cost is frequently cited as high, especially for smaller teams or experimentation.
- ● Several users mention a meaningful learning curve that requires training to unlock full value.
Data as of 2/2/2026
Based on 61 reviews
- ● Highly capable for building analytics and machine-learning use cases for large enterprises.
- ● Forward Deployed Engineers provide strong, hands-on support and quickly absorb complex business context.
- ● Customers say Foundry lets them build pipelines quickly and expose capabilities to many business users.
- ● Some customers view it primarily as a platform for POCs and MVPs rather than full product industrialization.
- ● Implementation and ongoing operations can be resource-intensive and require dedicated teams.
- ● Aligning closely with Palantir’s platform approach can reduce architectural flexibility for some organizations.
Data as of 1/21/2025
Dataiku
"Reviewers widely see Dataiku as a user-friendly yet powerful end-to-end AI platform with excellent support and governance, with main trade-offs around licensing cost, resource demands, and configuration complexity at large scale."
Based on 188 reviews
- ● Reviewers highlight an intuitive visual interface that still allows full Python, R, and SQL for advanced users.
- ● Strong end-to-end support from data preparation through model deployment and monitoring.
- ● Enables collaboration between data scientists and business analysts on shared projects.
- ● Licensing cost is a recurring concern for large-scale or enterprise deployments.
- ● Some users mention performance or resource-intensity issues on very large workloads.
- ● Initial setup and governance configuration can be complex in big organizations.
Data as of 2/2/2026
Based on 860 reviews
- ● Very high overall satisfaction scores for product capabilities and service & support.
- ● Recognized as a Leader multiple times in the Gartner Magic Quadrant for Data Science & ML Platforms.
- ● Customers value its ability to scale AI and analytics across many teams and use cases.
- ● Feature richness and flexibility contribute to a non-trivial learning curve for some teams.
- ● Enterprise deployments require thoughtful governance and infrastructure planning to get right.
- ● A few reviewers mention UI complexity or clutter for new users.
Data as of 1/30/2026
- ● Described as a one-stop environment covering the full data workflow from ingestion through deployment.
- ● Combines drag-and-drop pipeline building with support for multiple coding languages in the same project.
- ● Emphasizes collaboration and role-based access control to help diverse teams work together securely.
- ● Notes that large workloads can be resource-intensive and demand capable infrastructure.
- ● Highlights the need for training to fully exploit advanced capabilities.
- ● Points out that flexible integration and governance options can add configuration complexity.
Data as of 7/21/2025
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