Databricks vs Neptune.ai
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 2.6 for Neptune.ai, and ranks ahead in 67% of the answers that name both.
Choose Databricks if…
- You want the brand AI engines rank higher (46.2 vs 2.6)
- AI answers describe it more positively (35% vs 25% positive)
- 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
- You prefer a larger, established vendor (5,000+ employees)
Choose Neptune.ai if…
- You need a self-hosted or on-premises option
- Collaboration-first dashboard for tracking experiments
- Lightweight metadata store built for experiment tracking
- Discounts on annual plans
AI visibility side by side
Latest weekly run · Nov 2
| Metric | Databricks | Neptune.ai |
|---|---|---|
| Visibility score | 46.2% | 2.6% |
| Change vs last week | New | New |
| Rank in MLOps Platforms | #2 | #24 |
| Share of voice | 7.6% | 0.6% |
| Mentions this run | 51 | 4 |
| Positive in AI answers | 35% | 25% |
The stronger value in each row is marked with a ✓.
Visibility score trend
Weekly runs over the last 60 days
Databricks 0pp · Neptune.ai 0pp over 0 weekly runs.
Engine by engine
Databricks leads on 3 of 3 engines
Head to head in AI answers
3 engine answers name both brands
| Prompt | Engine | Databricks | Neptune.ai |
|---|---|---|---|
| Which MLOps Platforms tools offer annual discounts? | AI Mode | #4 | #1 |
| Where can I sign up for an MLOps platform? | AI Mode | #5 | #7 |
| Official website of the top MLOps platform vendors | ChatGPT | #1 | #16 |
Features, pricing & fit
| Dimension | Databricks | Neptune.ai |
|---|---|---|
| Deployment | Available across all major cloud providers | Hosted service by default, with a self-hosted option on-premises or on AWS, Google Cloud or Azure as a Kubernetes cluster, deployed as microservices via a Helm chart. |
| Integrations | — | 25+ |
| 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 | Palo Alto, California, United States |
| Employees | 5,000+ | 51–200 |
| Funding | Round at $60B valuation, including secondary sale | Series A · $18M raised |
| Best for | Data engineering, data science, analytics and AI teams at startups and global enterprises | Teams training foundation models and other large-scale machine learning models that need to monitor GPT-scale training runs. |
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
Neptune.ai
- Collaboration-first dashboard for tracking experiments
- Lightweight metadata store built for experiment tracking
- Discounts on annual plans
Data collected ·ChatGPT, Perplexity, Google AI Mode·coverage 100%·next run
Cite as: SlateIndex, “Databricks vs Neptune.ai”, https://slateindex.ai/compare/databricks-vs-neptune-ai, data of Nov 2, 2026. Licence: CC BY 4.0.