How to Choose a Database for AI Applications

Organizations in various industries are increasingly incorporating AI into essential offerings, but the reliability of these AI capabilities largely depends on the underlying data infrastructure. Selecting a suitable database for AI applications involves more than just model support; it requires ensuring that the infrastructure can handle production demands, such as scale, concurrency, and consistency. Many teams initially choose databases that work during experimentation but may fail under production pressures, highlighting the importance of elastic scaling and strong consistency, particularly with ACID transactions. High availability is crucial to avoid costly downtime, while a unified data platform can help manage operational data, vector search, and agent states efficiently. Multi-region deployment enhances performance and compliance, and securing AI agent access protects sensitive data. PostgreSQL compatibility can facilitate smoother development and faster deployment of AI features. CockroachDB, a distributed SQL database, is designed to meet these production requirements, offering features like native vector search and fine-grained governance to support robust AI applications.