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Graph Database Selection for RAG/AI: What the Benchmarks Won’t Tell You
Article 5 in the Knowledge Graphs for Enterprise AI Memory series
Every graph database vendor publishes benchmarks showing their product crushing the competition. Neo4j claims to be 1,000x faster than MySQL. FalkorDB claims to be 500x faster than Neo4j. Memgraph claims 50x faster writes than Neo4j. TigerGraph demonstrated 534 billion edges on 40 machines.
None of these numbers will predict your production performance.
The graph database market is projected to reach $11.35 billion by 2030, and vendors are fighting for position with marketing benchmarks designed to impress, not inform. Cherry-picked queries, hardware asymmetry (one vendor compared against 13-year-old CPUs), configuration sabotage — the manipulation playbook is well-documented.
This article cuts through the noise. After covering graph architecture, construction patterns, and maintenance realities in previous articles, we now face the selection question directly: which database actually fits your RAG/AI workload? The answer depends on your deployment context, scale requirements, and team expertise — not vendor claims.
