Complete guide to database platforms for AI applications: from vector databases to agentic architectures. Compare platforms, understand architecture patterns, and implement the right solution for your AI workloads.
Updated September 2025: Covering the latest AI database trends, including the explosion of agentic workloads and vector database adoption across enterprises.
Key trends reshaping how databases serve AI applications
80% of databases now created by AI agents, not humans
Sub-second database creation for machine-speed operations
Explosive growth in ephemeral, agent-driven databases
Vector embeddings becoming core to modern applications
Understanding your options in the AI database landscape
Existing applications adding AI features
AI-first applications with heavy vector workloads
Modern applications requiring both traditional and AI capabilities
Comprehensive guides for AI database platforms and architectures
Complete comparison of database platforms for AI workloads: vector databases, PostgreSQL with extensions, and specialized AI data stores.
Complete guide to using PostgreSQL for AI & ML: vector embeddings, pgvector extension, AI agent workflows, and LLM integrations.
Database architecture for AI agents: ephemeral databases, instant provisioning, scale-to-zero economics, and agent-driven data management.
Key questions to guide your AI database platform choice
Vela provides enterprise PostgreSQL with AI capabilities: vector search, instant cloning for AI experiments, and BYOC deployment for data control.