Synthèse
AlloyDB AI bypasses these workarounds — and their limits — by introducing native, in-database AI Functions like ai.generate(). This allows you to call Gemini directly from SQL, keeping your data and intelligence in one place. This approach provides three core advantages: No data movement: All text preprocessing and segmentation happen directly within the database engine. This minimizes network latency and keeps your data protected within your database boundaries. In-database intelligence: You
AlloyDB AI bypasses these workarounds — and their limits — by introducing native, in-database AI Functions like ai. generate(). This allows you to call Gemini directly from SQL, keeping your data and intelligence in one place. This approach provides three core advantages: No data movement: All text preprocessing and segmentation happen directly within the database engine. This minimizes network latency and keeps your data protected within your database boundaries.
In-database intelligence: You do not need to build, deploy, or maintain external microservices or orchestration frameworks. The database engine coordinates the model calls natively. Stored procedure-based batching: By using a PL/pgSQL stored procedure with array aggregation, you can process rows in parallel batches, unpack the results safely using GENERATE_SERIES, and commit each batch immediately. This prevents database memory exhaustion, bypasses row lock contention, and supports stable, performant execution even when handling massive tables.