best graph database for large scale data
AI Search Visibility Analysis
Analyze how brands appear across multiple AI search platforms for a specific prompt
Total Mentions
Total number of times a brand appears
across all AI platforms for this prompt
Platform Presence
Number of AI platforms where the brand
was mentioned for this prompt
Linkbacks
Number of times brand website was
linked in AI responses
Sentiment
Overall emotional tone when brand is
mentioned (Positive/Neutral/Negative)
Brand Performance Across AI Platforms
| BRAND | TOTAL MENTIONS | PLATFORM PRESENCE | LINKBACKS | SENTIMENT | SCORE |
|---|---|---|---|---|---|
1Neo4j | 11 | 4 | 95 | ||
2JanusGraph | 11 | 0 | 86 | ||
3TigerGraph | 9 | 0 | 83 | ||
4Amazon Neptune | 7 | 1 | 79 | ||
5ArangoDB | 7 | 0 | 75 | ||
6NebulaGraph | 7 | 2 | 70 | ||
7Dgraph | 4 | 0 | 56 | ||
8Memgraph | 1 | 2 | 55 |
Strategic Insights & Recommendations
Dominant Brand
Neo4j emerges as the most consistently recommended brand across all platforms, praised for its maturity, strong community support, and proven scalability.
Platform Gap
ChatGPT provides broader coverage including ArangoDB, while Perplexity offers the most detailed technical comparison, and Google AIO includes unique mentions of Dgraph and Memgraph.
Link Opportunity
All platforms reference official documentation and comparison resources, creating opportunities for linking to vendor websites, technical benchmarks, and implementation guides.
Key Takeaways for This Prompt
Neo4j, TigerGraph, JanusGraph, and NebulaGraph are consistently identified as top choices for large-scale graph data across all platforms.
Scalability approaches vary significantly: some databases offer horizontal scaling while others focus on vertical optimization and performance tuning.
Query language support differs substantially, with Cypher, GSQL, Gremlin, and nGQL each offering unique advantages for different use cases.
Managed cloud services like Amazon Neptune provide operational simplicity, while open-source options like JanusGraph offer greater customization flexibility.
AI Search Engine Responses
Compare how different AI search engines respond to this query
ChatGPT
BRAND (5)
SUMMARY
ChatGPT provides a comprehensive overview of five leading graph databases for large-scale data: Neo4j (with Cypher query language and ACID transactions), Amazon Neptune (fully managed AWS service supporting both property graph and RDF models), ArangoDB (multi-model database combining graph, document, and key/value), TigerGraph (designed for real-time analytics with parallel processing), and JanusGraph (open-source distributed database supporting billions of vertices and edges). The response emphasizes considering specific use cases, existing infrastructure, and compatibility requirements when making a selection.
REFERENCES (5)
Perplexity
BRAND (7)
SUMMARY
Perplexity offers a detailed comparison focusing on four top contenders: TigerGraph (excelling in real-time processing and deep link analysis), Neo4j (widely popular with strong community support and unbounded scale up to 100+ TB), JanusGraph (distributed with pluggable storage backends like Cassandra and HBase), and NebulaGraph (open-source, cloud-native, designed for trillions of edges with millisecond latency). The response includes a helpful comparison table highlighting scalability, query languages, and deployment considerations for each database.
REFERENCES (14)
Google AIO
BRAND (8)
SUMMARY
Google AIO presents six strong candidates for large-scale graph databases: NebulaGraph (cloud-native with shared-nothing architecture), JanusGraph (distributed with pluggable storage backends), Amazon Neptune (fully managed AWS service), Neo4j (battle-tested with multiple cloud options), Dgraph (fast and scalable with native GraphQL support), and TigerGraph (enterprise-focused for real-time deep link analytics). The response also covers key selection factors including managed vs. self-hosted options, data models, scalability requirements, community support, and specific use case optimization.
REFERENCES (15)
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