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.
Reviewed March 2026: Focused on current architecture patterns, platform tradeoffs, and how AI workloads are changing database requirements.
Key trends reshaping how databases serve AI applications
Database creation and setup are increasingly being automated by agents and CI systems
Seconds matter when environments are created on demand for tests, previews, and AI tasks
Short-lived preview, evaluation, and task databases are becoming a normal pattern
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.