AI Data Science Platform
Dataiku
Collaborative AI platform across business roles
- SF Score
- 8.6/10
- Pricing
- Contact sales
Compare tools
2 of 4 tools selected
AI Data Science Platform
SAS Viya
Statistical AI platform for regulated teams
- SF Score
- 8.3/10
- Pricing
- Contact sales
Add third toolCompare up to 4 side by side
Compare features, strengths, trade-offs, and best-fit use cases.
Comparing
Where each one leads
Read straight from the published figures below — not a ranking, and not a recommendation.
Highest SF Score
Dataiku8.6/10
2 of 2 are scored
At a glance
The short version
The facts each tool publishes, lined up so you can scan down a column. Anything the catalog does not establish is marked as such rather than counted against the tool.
Dataiku
- SF Score
- 8.6 / 10
- Free plan
- Not offered
- Category
- AI Analytics Software › AI Data Science Platform
SAS Viya
- SF Score
- 8.3 / 10
- Free plan
- Not offered
- Category
- AI Analytics Software › AI Data Science Platform
Key differences
Where these tools actually diverge
The decision dimensions that separate this set, with each tool's own position stated plainly. No winner is declared — fit depends on your workflow.
Positioned for
Dataiku
AI Data Science Platform
SAS Viya
AI Data Science Platform
Pricing model
Dataiku
Contact sales
SAS Viya
Contact sales
SoftFinders Score
Dataiku
8.6 / 10
SAS Viya
8.3 / 10
Feature comparison
Capability matrix
Grouped by area. Every state carries a word as well as a colour, and anything the catalog has not established stays neutral rather than counting as a missing feature.
1 row differs 3 identical
| Capability | | |
|---|---|---|
| Overview | ||
| SF Score | 8.6 / 10Value | 8.3 / 10Value |
| Pricing model | CustomValue | CustomValue |
| Free plan | No | No |
| Category | AI Analytics Software › AI Data Science PlatformValue | AI Analytics Software › AI Data Science PlatformValue |
Scroll the matrix sideways to see every column.
Listed by one tool only
10 capabilities are published by a single tool in this set. That does not mean the others lack them — only that the catalog has not established an answer either way.
- Visual FlowsBuild data and ML workflows visually
- Code RecipesUse Python, R, SQL, and notebooks
- AutoML ModelingTrain predictive models with guided automation
- LLM MeshGovern access to approved foundation models
- Statistical ModelingRun validated analytics and advanced procedures
- Forecasting ToolsModel time series and demand patterns
- Model ManagerGovern model versions and approvals centrally
- Visual AnalyticsExplore dashboards and business performance views
- Open LanguagesUse Python, R, and SAS together
- Responsible AIReview bias, explainability, and model risk
Use cases
What each tool is built for
The workflows each tool publishes for itself. Cards appear only for tools that list use cases.
Dataiku
- Unify analysts and data scientists
- Govern enterprise generative AI access
- Build predictive models without code
- Operationalize analytics workflows in production
- Standardize reusable data science projects
+1 more on the Dataiku profile
SAS Viya
- Build regulated credit risk models
- Analyze clinical and life sciences data
- Forecast demand and operational performance
- Govern enterprise model approval processes
- Modernize existing SAS analytics programs
+1 more on the SAS Viya profile
Pros & cons
Strengths and compromises
Each tool's published strengths and the compromises that come with them, as supplied — not scored or ranked against each other. Longer lists fold; nothing is left out.
Dataiku
Pros
- Balances visual workflows with code-based flexibility well
- LLM Mesh supports governed generative AI adoption
- Strong collaboration across business and technical teams
- Combines visual and code-based development
- Offers model explainability and fairness analysis
- Connects analytics, ML, and agent workflows
Cons
- Enterprise rollout needs standards and administrators early
- Visual flows can become complex at scale
- Pricing requires custom sales-led scoping from vendor
- Enterprise pricing requires contacting sales
- Advanced governance features require additional licensing
- Cloud trial excludes certain advanced capabilities
- On-premises deployment requires infrastructure management
- Production API deployment requires configured serving infrastructure.
SAS Viya
Pros
- Deep statistical procedures support regulated analytics work
- Model governance fits audit-heavy enterprise environments well
- Supports SAS, Python, and R together effectively
Cons
- Pricing is high for smaller analytics teams
- Migration from legacy SAS can take time
- Python-first teams may prefer open platforms instead
Pricing
What each one costs
Published pricing signals only. Where a tool has not established a price, that is what it says — never a zero and never "free".
Dataiku
- Model
- Custom
Contact Sales
See full pricingSAS Viya
- Model
- Custom
Contact Sales
See full pricingNext step
Ready to try one?
A matrix narrows the field; a free trial settles it. Open whichever tool fits your workflow best, or start a different comparison.
Compare another set of tools