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A service can be stateless between requests and still hold database connections. The catch is that a connection pool is usually local to an application process: when you add replicas, workers, or serverless invocations, you can multiply the number of possible database connections even if each instance has a modest pool. A shared proxy or pooler can aggregate clients and limit backend connections, but it shifts pressure into queues, wait timeouts, and pool limits rather than removing it.
Why a stateless service can exhaust database connections
“Stateless” describes where application state is kept between requests; it does not mean the service has no sockets, authentication work, database sessions, or resource limits. An application process can keep a local pool of reusable database connections. Each independently scaling process may have its own pool, so the fleet’s possible total grows with the number of processes.
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A serverless function or short-lived worker may not live long enough to reuse a process-local pool effectively. AWS describes this pattern in its RDS Proxy common usage scenarios: “This usage pattern can cause connection churn on the database side with no possibility to implement connection pooling on the application side.” When many clients connect directly, database connection limits can become client-facing failures.
Where the connections sit
Think of the path as: application instances or functions → optional process-local pool → shared proxy or pooler → bounded set of database server connections. The local pool reuses connections only within its process. A shared layer can aggregate connections from multiple processes and, depending on its mode and session requirements, assign or multiplex requests over fewer backend connections. Google Cloud describes its managed pooler as routing client connections to the database and reusing server connections after requests.
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The key distinction is between client connections accepted by the pooler and backend connections open to the database. A pooler may accept many clients while keeping fewer database connections open. Clients that cannot get a backend connection immediately wait; that waiting consumes time and can end in a timeout.
Diagnose the bottleneck before changing pool sizes
A single “connection count” is not enough to identify the problem. First determine whether the database has run out of backend connections, the pooler is rejecting client connections, or clients are waiting behind a saturated backend pool.
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- PgBouncer: its admin console’s
SHOW POOLSreports connection counts by state for each pool;SHOW DATABASESshows applied connection limits; andSHOW STATSreports request and traffic statistics. See the PgBouncer usage reference for how to interpret current connections, maximum server connections, and client caps. - Azure Database for PostgreSQL with managed PgBouncer: use the Azure PgBouncer documentation for logging and admin-console guidance. Logs can reveal drops, authentication failures, lifecycle events, errors, server state changes, and pool exhaustion; the console also supports the same three
SHOWcommands. - Supabase: dashboard reports include database connections and client connections to dedicated and shared poolers, but they are not real-time. For current counts, Supabase points to
pg_stat_activityin its connection pooling and limits documentation.
Also inspect pool wait time and waiters, authentication failures, connection churn, database-side active and idle connections, and which database/user partition each connection belongs to. Metric definitions and refresh intervals vary by provider, so interpret counts in the context of the product reporting them.
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| Approach | Where connections are managed | When to investigate it | Main trade-off |
|---|---|---|---|
| Application-side pool | Inside each application process or server | Persistent containers or VMs that reuse processes | Simple and low-latency within a process, but each scaling process has a separate pool to include in the capacity budget. |
| Shared pooler such as PgBouncer | Between multiple clients and the database | PostgreSQL workloads with short sessions or many independently scaling workers | Aggregates clients, but introduces a shared capacity boundary and queue; pooling mode and database/user partitions affect behavior. |
| Managed proxy or pooler | Provider-operated proxy tier | Teams that prefer a managed deployment or provider integrations | Eligibility, network and authentication setup, modes, caps, limitations, costs, and operational ownership depend on the provider. |
| Direct connections | Application to database | Long-lived sessions, session-dependent features, or low connection counts | Avoids pooler overhead and compatibility constraints, but does not aggregate connections from separate application processes. |
Examples include Amazon RDS Proxy, Cloud SQL Managed Connection Pooling, Supabase poolers, and Azure managed PgBouncer. A sensible choice depends on runtime lifetime, replica or invocation concurrency, total backend budget, number of pool partitions, session features, queue behavior and timeout, networking and authentication, monitoring, provider eligibility, ownership, cost, and latency. There is no universal winner or general performance improvement established for every workload.
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Size the whole connection budget, not one pool
Count every source of database connections: direct application connections, possible backend connections from every proxy or pooler, and provider or platform services. Supabase specifically notes that its Auth, Storage, PostgREST, and health-checker connections also consume the Postgres connection maximum. A pool cap is not necessarily a fleet-wide cap: it may apply per pooler, database, user, or database/user pair.
For example, Google Cloud’s current Managed Connection Pooling documentation, accessed 2026-10-05, gives a default max_pool_size of 50 server connections for each database/user pair on each pooler. Its example shows that two poolers with that pool size can permit 100 server connections for the illustrated pair. These are Cloud SQL configuration defaults and an example, not general sizing advice; the feature requires Cloud SQL Enterprise Plus and specified network and maintenance conditions. Recheck the current Cloud SQL documentation and your deployed configuration.
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The same Cloud SQL documentation, accessed 2026-10-05, states defaults of 5,000 client connections per pooler and a 120-second query_wait_timeout. These provider-specific defaults are volatile and configuration-dependent. The timeout can be disabled, which permits indefinite client queuing; an indefinite queue does not create capacity. Choose a finite wait policy that fits the application’s latency and retry behavior.
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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchPgBouncer also separates client caps from server-side caps such as max_db_connections and max_user_connections. Its documentation cautions that closing a client does not necessarily free its server connection for another pool immediately: the backend can remain open until it is closed, for example after an idle timeout. Some authentication configurations create a separate pool per user, limiting cross-pool reuse.
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Common sizing traps
- Multiplying a local pool maximum by the fleet’s possible worker or replica count and overlooking the total.
- Adding pool sizes across poolers, database/user pairs, or users as if a per-pool limit were global.
- Forgetting direct database traffic alongside pooled traffic, or connections used by provider services.
- Allowing queues to grow without a useful timeout, turning a clear overload error into long hangs.
- Setting the backend cap so low that requests wait continuously, or so high that the database’s connection budget is consumed.
- Using transaction pooling when the application relies on session state that the mode cannot preserve.
Raising the database’s maximum connection count is not automatically a fix: more connections consume database resources, and the underlying workload may still exceed what the database can serve. First identify which cap is binding and whether requests are rejected, queued, or using more backend connections than expected.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Check transaction-pooling compatibility
In transaction pooling, the backend connection is returned to the pool when a transaction ends rather than remaining attached to a client session. That can suit short-lived transactional requests, but it changes session semantics. Google Cloud’s current Cloud SQL documentation lists these unsupported features in transaction mode: SET/RESET, LISTEN, WITH HOLD CURSOR, PREPARE/DEALLOCATE, certain temporary-table operations, LOAD, and session-level advisory locks. It also notes that some clients require prepared-statement configuration.
These compatibility details are product- and version-specific. Check the deployed pooler’s documentation and test the application’s actual SQL and session behavior before switching modes. Session pooling preserves a backend connection for each connected client session and therefore provides less multiplexing; Cloud SQL says each connected session uses a dedicated backend connection. For its own specific networking scenario, Supabase describes shared-pooler session mode as an alternative, while recommending direct connections for long-lived sessions. Those are provider-specific recommendations.
What a pooler can—and cannot—fix
Google Cloud defines its service this way: “A connection pooler is a database proxy service that manages and routes database connections between client applications and the database server.” Pooling can reduce connection churn and let a smaller backend set serve more client connections when the workload and pooling mode allow safe sharing. It cannot remove the database’s capacity ceiling: it relocates the limit into backend caps, client caps, queue wait, timeouts, pool partitions, and session compatibility.
Choose direct connections when long-lived sessions or required session features outweigh aggregation. Investigate a process-local pool when application processes persist and reuse work. Consider a shared or managed pooler when independently scaling clients create connection churn or exceed the database’s practical backend budget. In every case, size and observe the entire path rather than treating the application’s individual pool as the system-wide limit.
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