Graph databases are trendy again
Thank you, AI!
Graph DBs are the fastest-growing segment in the $137B database market (25%+ annual growth), and here's why that matters for your AI strategy.
Traditional relational databases store data in rows and tables. Graph databases store relationships. Think connections between customers, products, and behaviors. This makes them perfect for AI systems that need context, not just data points.
AI workloads (LLMs, recommendation engines, knowledge graphs) are becoming board-level priorities. Your database choice directly impacts AI model accuracy, speed, and scalability. Graph databases sell themselves as the "AI-ready data fabric" these systems need.
The catch? Vendors like Neo4j, TigerGraph, and cloud giants (AWS Neptune, Google Spanner Graph etc.) are racing to lock in multi-year enterprise deals while AI demand is hot. Expect aggressive bundling and new pricing models that scale with usage, not storage.
The risk is getting locked into immature ecosystems before standards settle. Vendor consolidation will come at some point.
What to do? Audit current AI projects for graph dependencies. If you're building knowledge graphs or doing entity resolution, start conversations with both incumbent vendors and specialists. Don't let vendor FOMO drive your strategy, but don't sleep on this shift either.