Databricks vs Qdrant
Which one do AI engines recommend, and how do they differ on price, fit and features?
The short answer
Qdrant is the stronger AI pick in Vector Databases: it has a visibility score of 60.5 versus 1.4 for Databricks, and ranks ahead in 100% of the answers that name both.
Choose Databricks if…
- 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)
- Lakehouse architecture combining data warehousing with data lake flexibility
- Strong for machine learning, AI, and data engineering workloads
Choose Qdrant if…
- You want the brand AI engines rank higher (60.5 vs 1.4)
- AI answers describe it more positively (68% vs 35% positive)
- You need a self-hosted or on-premises option
- You need SOC 2 Type 2 or HIPAA coverage
AI visibility side by side
Latest weekly run · Nov 2
| Metric | Databricks | Qdrant |
|---|---|---|
| Visibility score | 1.4% | 60.5% |
| Change vs last week | New | New |
| Rank in Vector Databases | #22 | #2 |
| Share of voice | 0.8% | 13.4% |
| Mentions this run | 4 | 63 |
| Positive in AI answers | 35% | 68% |
The stronger value in each row is marked with a ✓.
Visibility score trend
Weekly runs over the last 60 days
Databricks 0pp · Qdrant 0pp over 0 weekly runs.
Engine by engine
Qdrant leads on 3 of 3 engines
Head to head in AI answers
5 engine answers name both brands
| Prompt | Engine | Databricks | Qdrant |
|---|---|---|---|
| Best vector database for enterprise | ChatGPT | #9 | #2 |
| Best vector database for enterprise | AI Mode | #6 | #5 |
| Compare the leading Vector Databases platforms | ChatGPT | #10 | #2 |
| Best vector database with AI features | Perplexity | #7 | #3 |
| Is Chroma a good vector database? | ChatGPT | #6 | #2 |
Features, pricing & fit
| Dimension | Databricks | Qdrant |
|---|---|---|
| Pricing model | — | Usage-based |
| Entry price | — | Contact sales |
| Free plan | — | Yes |
| Deployment | Available across all major cloud providers | Managed cloud, hybrid cloud, private cloud (air-gapped), self-hosted on-premises, and edge |
| Compliance | ISO 27001, ISO 27017, ISO 27018, ISO 27701, SOC 1, SOC 2 Type II, SOC 3 | SOC 2 Type 2, HIPAA |
| Support | Technical support on request, Technical documentation | Community support via Discord on the free tier, Standard tier: email and ticket portal, 10x5 business hours, Premium tier: 24/7 support with private Slack, Discord or Teams channels, Enterprise tier: custom response SLA and advisory services |
| Founded | 2013 | 2021 |
| Headquarters | San Francisco, CA | Berlin, Germany |
| Employees | 5,000+ | 51–200 |
| Funding | Round at $60B valuation, including secondary sale | Series B · $88M raised |
| Best for | Data engineering, data science, analytics and AI teams at startups and global enterprises | Developers and teams building RAG, semantic search and recommendation systems that need hybrid search and metadata filtering, either self-hosted or managed |
Plans side by side
Databricks
Pricing not available.
Qdrant
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
Qdrant
- High performance and memory efficiency, written in Rust
- Strong metadata and complex payload filtering
- Free tier for prototyping and testing
- Open-source option with flexible self-hosting
- Strong default choice for startups and production RAG
Data collected ·ChatGPT, Perplexity, Google AI Mode·coverage 100%·next run
Cite as: SlateIndex, “Databricks vs Qdrant”, https://slateindex.ai/compare/databricks-vs-qdrant, data of Nov 2, 2026. Licence: CC BY 4.0.