Apache SparkOpen-source multi-language engine for data engineering, data science and machine learning.
vs
MaterializeThe live context layer for agents and apps
Comparison · Data Warehouse · weekly run Nov 3
Apache Spark vs Materialize
Which one do AI engines recommend, and how do they differ on price, fit and features?
The short answer
Apache Spark is the stronger AI pick in Data Warehouse: it has a visibility score of 0.9 versus 0 for Materialize.
Choose Apache Spark if…
- You want the brand AI engines rank higher (0.9 vs 0)
- You prefer a larger, established vendor (5,000+ employees)
- Strong distributed processing capabilities for massive workloads
- Distributes query execution across a cluster of commodity machines
Choose Materialize if…
- You need SOC 2 Type II coverage
AI visibility side by side
Latest weekly run · Nov 3
| Metric | Apache Spark | Materialize |
|---|---|---|
| Visibility score | 0.9% | 0% |
| Change vs last week | New | New |
| Rank in Data Warehouse | #26 | #35 |
| Share of voice | 0.5% | 0% |
| Mentions this run | 3 | 0 |
| Positive in AI answers | — | — |
The stronger value in each row is marked with a ✓.
Visibility score trend
Weekly runs over the last 60 days
Apache Spark 0pp · Materialize 0pp over 0 weekly runs.
Engine by engine
Apache Spark leads on 2 of 3 engines
ChatGPT
AI Mode
Perplexity
Apache SparkMaterialize
Head to head in AI answers
0 engine answers name both brands
0Apache Spark ranked higher
0Materialize ranked higher
Features, pricing & fit
| Dimension | Apache Spark | Materialize |
|---|---|---|
| Pricing model | — | Usage-based |
| Entry price | — | Free; paid from $1.5 / mo |
| Free plan | — | Yes |
| Deployment | Runs on single machines or on clusters: standalone, Hadoop YARN, Apache Mesos or Kubernetes. | Fully managed SaaS (Materialize Cloud) or self-managed on your own infrastructure, including air-gapped on-prem |
| Compliance | — | SOC 2 Type II |
| Support | — | Chatbot and helpdesk tickets (Cloud On-Demand), Dedicated account team and guided onboarding (Cloud Capacity), Community Slack (Community license), Priority support from Materialize (Enterprise license) |
| Founded | 2010 | 2019 |
| Headquarters | Wilmington, Delaware | New York, NY, United States |
| Employees | 5,000+ | 51–200 |
| Funding | — | Series C · $100M raised |
| Best for | Data engineering, data science and machine learning work on single machines or clusters. | Engineering teams that want to build live data products and fresh context for AI agents and apps using SQL |
Plans side by side
Apache Spark
Pricing not available.
Materialize
Cloud On-Demand$1.5per compute credit, billed monthly
Cloud Capacity$1.5per compute credit, annual prepaid plan
Self-Managed Community$0free forever
Self-Managed EnterpriseCustomannual license
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
Materialize
Profile in progress.
Data collected ·ChatGPT, Perplexity, Google AI Mode·coverage 100%·next run
Cite as: SlateIndex, “Apache Spark vs Materialize”, https://slateindex.ai/compare/apache-spark-vs-materialize, data of Nov 3, 2026. Licence: CC BY 4.0.