Mammoth vs Dataiku
An end-to-end platform for data science, or the reporting job done this week.
Dataiku is built for organisations doing real data science: feature engineering, model training, deployment and monitoring, governed across many teams. If that is the work, the breadth is the point and little else covers as much of it in one place. Most companies asking about Dataiku are not doing that work yet. They have messy data, a reporting cycle that hurts, and no capacity for a platform rollout. Mammoth is deliberately smaller: preparation, automation and dashboards, running in days rather than quarters, without engineering support.
Side by side
The same nine criteria we use on every comparison
Including the two where the answer doesn't favour us.
| Mammoth | Dataiku | |
|---|---|---|
| Time to first trustworthy dashboard | Around 15 minutes from raw data | Fast once the platform is in place; the rollout is the wait |
| Who can build one | Anyone on the team, in plain language | Analysts and data scientists, after training |
| Data preparation | Same product, same canvas, re-runnable | Strong, in a visual recipe model |
| Cost to share with 200 viewers | $0. Viewers are free on every plan | Licensed per user, including lighter read-only roles |
| Keeping numbers current | Scheduled and event-driven refresh, built in | Scenarios and triggers, once configured |
| Machine learning | Not the product. AI is used to build pipelines | Deeper by a wide margin. This is Dataiku's core |
| What it takes to run | Nothing. It is hosted and there is no install | Infrastructure and people to administer it |
| Entry price | Free tier, then $199/mo | Not published. Enterprise quote |
| Compliance | SOC 2 Type II, ISO/IEC 27001:2022, HIPAA, GDPR | Varies by vendor, edition and region — check their trust page |
Dataiku pricing and capabilities as published August 2026. If something here is out of date, tell us and we'll fix it.
Fair's fair
What Dataiku does better
No comparison is worth reading if it pretends the other tool has no advantages. These are real.
- Genuine end-to-end coverage, from prep through to deployed models
- Strong machine learning tooling, including AutoML and monitoring
- Code and visual side by side, so analysts and engineers share a project
- Deep governance and MLOps features for regulated environments
So which should you pick?
Genuinely depends on the job. Here's how we'd decide.
Choose Dataiku if…
- Machine learning in production is the actual goal
- You have data scientists and engineers to run the platform
- You need one governed environment across many teams and projects
Choose Mammoth if…
- The problem is reporting and data preparation, not modelling
- Nobody has months for an implementation
- You want business users self-serving rather than filing tickets
Test it against your own data.
Twenty minutes on the free plan will tell you more about the difference than this page can. Keep Dataiku running while you do.
- 21-day Pro trial
- No credit card
- Viewers always free