DataikuThe Platform for AI Success
vs
DVCManage data the way code is managed.
Comparison · MLOps Platforms · weekly run Nov 2
Dataiku vs DVC
Which one do AI engines recommend, and how do they differ on price, fit and features?
The short answer
Dataiku is the stronger AI pick in MLOps Platforms: it has a visibility score of 8.5 versus 3.7 for DVC, and ranks ahead in 100% of the answers that name both.
Choose Dataiku if…
- You want the brand AI engines rank higher (8.5 vs 3.7)
- You need ISO 27001:2022 or ISO 27701:2019 or SOC 1 Type II or SOC 2 Type II or HIPAA or GxP or GDPR coverage
- Bridges data scientists and business teams for collaboration
- End-to-end governance and operationalization
Choose DVC if…
- AI answers describe it more positively (56% vs 48% positive)
- Git-like versioning of data and experiments that integrates with code repositories
- Versions massive datasets and models alongside code
- Free, open-source tool often paired with MLflow
AI visibility side by side
Latest weekly run · Nov 2
| Metric | Dataiku | DVC |
|---|---|---|
| Visibility score | 8.5% | 3.7% |
| Change vs last week | New | New |
| Rank in MLOps Platforms | #12 | #21 |
| Share of voice | 2.9% | 1.4% |
| Mentions this run | 20 | 9 |
| Positive in AI answers | 48% | 56% |
The stronger value in each row is marked with a ✓.
Visibility score trend
Weekly runs over the last 60 days
Dataiku 0pp · DVC 0pp over 0 weekly runs.
Engine by engine
Dataiku leads on 2 of 3 engines
ChatGPT
AI Mode
Perplexity
DataikuDVC
Head to head in AI answers
3 engine answers name both brands
3Dataiku ranked higher
0DVC ranked higher
| Prompt | Engine | Dataiku | DVC |
|---|---|---|---|
| What are the best MLOps Platforms tools in 2026? | ChatGPT | #10 | #12 |
| Most affordable MLOps platform that is still reliable | AI Mode | #2 | #4 |
| Official website of the top MLOps platform vendors | Perplexity | #7 | #15 |
Features, pricing & fit
| Dimension | Dataiku | DVC |
|---|---|---|
| Deployment | Runs on the clouds, data platforms and models a customer already has | Runs locally on the user's machine alongside Git, with data kept in the customer's own cloud storage (S3, Azure, Google Cloud). |
| Compliance | ISO 27001:2022, ISO 27701:2019, SOC 1 Type II, SOC 2 Type II, HIPAA, GxP, GDPR | — |
| Founded | 2013 | 2017 |
| Headquarters | New York City, NY | — |
| Employees | 1,001–5,000 | — |
| Funding | Series E · $600M raised | — |
| Best for | Large enterprises, including highly regulated ones, where business experts and IT need to build AI together under central governance | Individual data scientists and small data science projects that want data version control with minimal overhead. |
Pros and cons
Themes from the AI answers that mention each brand
Dataiku
- Bridges data scientists and business teams for collaboration
- End-to-end governance and operationalization
- Broad enterprise MLOps platform
- Best for enterprise collaboration and governance
- Steep platform fees that scale aggressively
DVC
- Git-like versioning of data and experiments that integrates with code repositories
- Versions massive datasets and models alongside code
- Free, open-source tool often paired with MLflow
Data collected ·ChatGPT, Perplexity, Google AI Mode·coverage 100%·next run
Cite as: SlateIndex, “Dataiku vs DVC”, https://slateindex.ai/compare/dataiku-vs-dvc, data of Nov 2, 2026. Licence: CC BY 4.0.