Connection Pooling

Establishing a new database connection involves TCP handshake, authentication, and session initialization, which can take tens of milliseconds. Under high concurrency, the overhead of repeatedly creating and tearing down connections becomes a significant bottleneck. Connection pooling maintains a set of pre-established connections that requests check out, use, and return, amortizing setup costs across many operations.

For AI product teams, connection pooling is critical because AI features often require multiple database queries per request: fetching user context, looking up feature values, retrieving cached predictions, and logging results. Without pooling, each AI inference request might establish four or five database connections, quickly exhausting database connection limits under load. Growth teams should monitor connection pool metrics because pool exhaustion is a common cause of sudden latency spikes during traffic surges. Serverless functions add complexity because each function instance may create its own pool, leading to connection count explosion. Tools like PgBouncer or ProxySQL sit between applications and databases to centralize connection management and prevent this problem.