Apache Spark vs Databricks
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 Data Warehouse: it has a visibility score of 37.9 versus 0.9 for Apache Spark, and ranks ahead in 100% of the answers that name both.
Choose Apache Spark if…
- Strong distributed processing capabilities for massive workloads
- Distributes query execution across a cluster of commodity machines
Choose Databricks if…
- You want the brand AI engines rank higher (37.9 vs 0.9)
- 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
AI visibility side by side
Latest weekly run · Nov 3
| Metric | Apache Spark | Databricks |
|---|---|---|
| Visibility score | 0.9% | 37.9% |
| Change vs last week | New | New |
| Rank in Data Warehouse | #26 | #4 |
| Share of voice | 0.5% | 9.3% |
| Mentions this run | 3 | 52 |
| Positive in AI answers | — | 35% |
The stronger value in each row is marked with a ✓.
Visibility score trend
Weekly runs over the last 60 days
Apache Spark 0pp · Databricks 0pp over 0 weekly runs.
Engine by engine
Databricks leads on 3 of 3 engines
Head to head in AI answers
1 engine answers name both brands
| Prompt | Engine | Apache Spark | Databricks |
|---|---|---|---|
| Compare the leading data warehouse platforms | ChatGPT | #8 | #2 |
Features, pricing & fit
| Dimension | Apache Spark | Databricks |
|---|---|---|
| Deployment | Runs on single machines or on clusters: standalone, Hadoop YARN, Apache Mesos or Kubernetes. | Available across all major cloud providers |
| 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 | 2010 | 2013 |
| Headquarters | Wilmington, Delaware | San Francisco, CA |
| Employees | 5,000+ | 5,000+ |
| Funding | — | Round at $60B valuation, including secondary sale |
| Best for | Data engineering, data science and machine learning work on single machines or clusters. | Data engineering, data science, analytics and AI teams at startups and global enterprises |
Pros and cons
Themes from the AI answers that mention each brand
Apache Spark
- Strong distributed processing capabilities for massive workloads
- Distributes query execution across a cluster of commodity machines
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
Data collected ·ChatGPT, Perplexity, Google AI Mode·coverage 100%·next run
Cite as: SlateIndex, “Apache Spark vs Databricks”, https://slateindex.ai/compare/apache-spark-vs-databricks, data of Nov 3, 2026. Licence: CC BY 4.0.