DatabricksUnified data, analytics and AI platform on an open lakehouse
vs
DVCManage data the way code is managed.
Comparison · MLOps Platforms · weekly run Nov 2
Databricks vs DVC
Which one do AI engines recommend, and how do they differ on price, fit and features?
The short answer
Databricks is the stronger AI pick in MLOps Platforms: it has a visibility score of 46.2 versus 3.7 for DVC, and ranks ahead in 75% of the answers that name both.
Choose Databricks if…
- You want the brand AI engines rank higher (46.2 vs 3.7)
- You need ISO 27001 or ISO 27017 or ISO 27018 or ISO 27701 or SOC 1 or SOC 2 Type II or SOC 3 coverage
- Lakehouse architecture combining data warehousing with data lake flexibility
- Strong for machine learning, AI, and data engineering workloads
Choose DVC if…
- AI answers describe it more positively (56% vs 35% 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 | Databricks | DVC |
|---|---|---|
| Visibility score | 46.2% | 3.7% |
| Change vs last week | New | New |
| Rank in MLOps Platforms | #2 | #21 |
| Share of voice | 7.6% | 1.4% |
| Mentions this run | 51 | 9 |
| Positive in AI answers | 35% | 56% |
The stronger value in each row is marked with a ✓.
Visibility score trend
Weekly runs over the last 60 days
Databricks 0pp · DVC 0pp over 0 weekly runs.
Engine by engine
Databricks leads on 3 of 3 engines
ChatGPT
AI Mode
Perplexity
DatabricksDVC
Head to head in AI answers
4 engine answers name both brands
3Databricks ranked higher
1DVC ranked higher
| Prompt | Engine | Databricks | DVC |
|---|---|---|---|
| What are the best MLOps Platforms tools in 2026? | ChatGPT | #1 | #12 |
| Alternatives to the most popular MLOps platform | Perplexity | #12 | #6 |
| Where can I sign up for an MLOps platform? | AI Mode | #5 | #10 |
| Official website of the top MLOps platform vendors | Perplexity | #1 | #15 |
Features, pricing & fit
| Dimension | Databricks | DVC |
|---|---|---|
| Deployment | Available across all major cloud providers | 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, ISO 27017, ISO 27018, ISO 27701, SOC 1, SOC 2 Type II, SOC 3 | — |
| Support | Technical support on request, Technical documentation | — |
| Founded | 2013 | 2017 |
| Headquarters | San Francisco, CA | — |
| Employees | 5,000+ | — |
| Funding | Round at $60B valuation, including secondary sale | — |
| Best for | Data engineering, data science, analytics and AI teams at startups and global enterprises | 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
Databricks
- Lakehouse architecture combining data warehousing with data lake flexibility
- Strong for machine learning, AI, and data engineering workloads
- Top customer satisfaction ratings alongside Snowflake
- Free trial and free Edition for learning
- Discounts for annual prepaid commitments
- Complex consumption-based pricing with no single fixed price
- Best only when ML, Spark, or Python skills are central to the team
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, “Databricks vs DVC”, https://slateindex.ai/compare/databricks-vs-dvc, data of Nov 2, 2026. Licence: CC BY 4.0.