Guide Maps Database Types for AI Engineering Workflows
Towards AI publishes a comprehensive breakdown of database architectures relevant to agentic AI systems.
Towards AI published a technical guide covering the major database categories now in play across AI engineering — relational, NoSQL, vector, time series, graph, and NewSQL — with a focus on where each fits in modern AI pipelines.
The piece arrives as database selection has become a critical architectural decision for teams building agentic AI systems. Vector databases handle embedding search for RAG, graph databases map entity relationships for knowledge-intensive agents, and time series stores underpin real-time monitoring of model performance. Understanding the trade-offs between these options matters for anyone designing production AI infrastructure, where the wrong storage layer can bottleneck retrieval speed, limit scalability, or complicate multi-step agent workflows. The guide is part of Towards AI's broader educational push, which now reaches over 100,000 AI practitioners.