How to choose data storage for AI Agent

Selecting data storage for an AI Agent requires evaluating the specific data types, access patterns, performance needs, and scalability requirements. It involves choosing between or combining different storage technologies.

Key factors include the nature of the data (structured, unstructured, or vector embeddings needed for semantic search), required query speed (low latency for real-time Agents), scalability for large datasets, durability guarantees, security compliance needs, and cost constraints. Vector databases are essential for efficient semantic similarity search within Agent memory or knowledge bases, while traditional relational (SQL) or document (NoSQL) databases suit structured operational data and state management. Consider hybrid approaches integrating multiple storage types.

Assess the Agent's core tasks: real-time semantic recall needs vector databases; transaction-heavy interactions might demand SQL/NoSQL. Start by defining data schemas and access patterns. Choose scalable, manageable solutions (cloud providers often offer managed services). Prioritize solutions enabling required Agent capabilities efficiently, balancing performance, development ease, and cost. This ensures reliable knowledge retrieval and Agent functionality.