AI Applications Require Both Vector and Relational Databases

Production AI applications necessitate both a vector database for semantic retrieval and a relational database for structured, transactional workloads. This dual approach is essential as a vector database alone cannot support the complexities involved with real users and transactions.

Vector databases, such as Pinecone, Milvus, and Weaviate, excel at semantic searches, utilizing high-dimensional embeddings to identify data based on meaning and intent. They are particularly effective in environments where flexible, meaning-based retrieval is crucial, such as legal AI applications that must process queries about tenant rights without exact phrasing.

However, vector databases have significant limitations. They cannot guarantee correctness for structured lookups, meaning they may fail to return all relevant records or include records that do not meet specific criteria. For example, retrieving support tickets by user ID in a given timeframe requires a relational database, as SQL queries provide the accuracy and structure needed for precise data retrieval.

Moreover, vector databases struggle with tasks such as aggregation and state management. Operations like counting active user sessions or managing user profiles are trivial with SQL, but become inefficient or impossible with vector embeddings alone. Therefore, for AI applications that involve user interactions, billing, or permissions, a relational database is essential to manage these operational complexities.

Relational databases serve as the backbone of AI systems, managing critical domains such as user identity and access control with absolute accuracy. They must ensure that permissions are precise and reliably enforced, which is vital for maintaining data security and integrity. Additionally, they store essential metadata for embeddings, linking the semantic representation of data to its original context, which is crucial for retrieval and application functionality.