AI applications

The database landscape has seen transformative shifts with the rise and fall of various technologies, leading to a resurgence in the adoption of PostgreSQL (Postgres) as a favored database system for modern AI applications. AI workloads have disrupted traditional managed database models, which were designed for predictable workloads, by introducing demands for high parallelism and continuous ingestion of large datasets. Engineering teams face challenges when scaling managed Postgres instances due to IOPS limits, throttling, and latency spikes. Major database vendors are emphasizing PostgreSQL compatibility, recognizing the need for a stable SQL system with strong transactional support. Despite its advantages, traditional managed database models present limitations, including reliance on network storage that introduces latency and IOPS constraints. A new model called Bring Your Own Cloud (BYOC) Postgres is emerging, allowing organizations to control their cloud environments while leveraging managed services, improving performance by colocating data with compute resources. Utilizing local NVMe storage with Postgres can enhance performance by reducing latency and IOPS limitations. Modern Postgres platforms facilitate efficient cloning processes that support parallel experiments in AI development, leading to shorter feedback loops and faster iteration cycles. The robust ecosystem of Postgres allows it to handle diverse workloads, streamlining data management and minimizing operational complexity. Developer velocity is a critical factor in the shift back to Postgres, as it enables rapid feedback and safe testing environments, empowering teams to innovate more effectively.