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None of these poolers preserves every PostgreSQL session behavior in transaction mode. PgBouncer can track protocol-level prepared statements when configured; Supavisor transaction mode does not support prepared statements or query pipelining; and RDS Proxy may pin sessions instead of multiplexing them. The right choice depends on your application’s session state, driver, deployment, and measured workload—not a universal speed ranking.
What transaction pooling changes
In transaction pooling, a client uses a backend database connection while its transaction is active. Once the transaction ends, the pooler can return that connection to the shared pool. The client may receive a different backend connection for its next transaction, so connection-level state cannot be assumed to persist.
That trade-off can let many client connections share fewer database connections, but it makes compatibility with session-oriented behavior the first question to answer. A pooler may reuse connections after transactions without making every operation safe to move between them.
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How the three poolers compare
| Decision point | PgBouncer transaction mode | Supavisor transaction mode | Amazon RDS Proxy |
|---|---|---|---|
| When a backend can be reused | After the client transaction ends. | After transaction work finishes; the shared pooler returns the backend for reuse. | After a transaction ends, when the session can be multiplexed. |
| Session state | Many session-dependent features are incompatible in transaction mode. | Session-level state does not persist across transactions. | Some requests pin a session to its current connection, limiting multiplexing. |
| Prepared statements | Protocol-level prepared plans can work when max_prepared_statements is nonzero; SQL PREPARE/DEALLOCATE are a separate, incompatible case. |
Not supported in transaction mode; disable prepared statements in the client or driver. | Consult current AWS documentation for the target engine and test the actual workload. |
| Where it runs | Self-managed pooler. | Supabase-managed shared pooler; Supabase also offers dedicated PgBouncer. | AWS-managed proxy for supported RDS deployments. |
| Best fit indicated by documented behavior | Teams seeking a configurable pooler and able to adapt applications to transaction semantics. | Supabase workloads with many short-lived or serverless connections that meet the documented client constraints. | RDS users who prefer managed infrastructure and can accommodate AWS-specific constraints. |
This is a compatibility and deployment comparison, not a benchmark. The cited documentation does not establish a like-for-like performance winner among the three.
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Which PostgreSQL behaviors survive transaction mode?
PgBouncer: protocol-level prepared statements are not SQL PREPARE
PgBouncer’s feature guide identifies several session-oriented features as incompatible with transaction pooling: SET/RESET, LISTEN, holdable cursors, SQL PREPARE/DEALLOCATE, persistent temporary tables, LOAD, and session-level advisory locks. Its feature table distinguishes those SQL commands from protocol-level prepared plans, which can work if max_prepared_statements is nonzero. Protocol-level prepared-statement tracking in transaction pooling has been available since PgBouncer 1.21.0, according to its FAQ. Check the feature guide and configuration documentation for the behavior and setting relevant to your deployment.
Supavisor: configure the client for its limitations
Supabase documents that Supavisor transaction mode does not support prepared statements or query pipelining, and that session-level state does not persist. Its guide also calls out session-level settings and advisory locks, LISTEN/NOTIFY, temporary tables, and cursors that need to outlive a transaction. Supabase advises disabling prepared statements with the option appropriate to the driver. Examples in its connection guide include prepare: false for Postgres.js/Drizzle, pgbouncer=true for Prisma, statement_cache_size=0 for asyncpg, and prepareThreshold=0 for JDBC. Confirm the correct setting for your driver and version rather than applying one driver’s option to another.
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RDS Proxy: transaction boundaries help, but do not guarantee multiplexing
AWS says all statements in a transaction use the same underlying database connection. After the transaction completes, that connection can become available to another session. If the proxy detects a request that makes moving the session impractical, it pins the session to its current connection. Read AWS’s RDS Proxy concepts and pinning guidance to assess which application behavior can reduce reuse.
Choose based on your deployment and workload
Choose PgBouncer when you want control over the pooler
PgBouncer is the most direct fit when your team can operate a self-managed pooler, needs configurable behavior, and can avoid or redesign session-dependent application features. If prepared statements matter, identify whether the driver uses the PostgreSQL protocol’s prepared-plan mechanism or SQL PREPARE; PgBouncer’s support for the former does not make the latter compatible.
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Choose Supavisor for compatible Supabase serverless workloads
Supabase positions its shared Supavisor transaction mode for serverless and edge functions that open many short-lived connections. The shared pooler is documented as IPv4-reachable. Supabase separately describes a dedicated PgBouncer pooler on paid plans, running alongside the database; the company says it has lower latency than the shared pooler because it runs on the same machine, while using project compute. Direct connections and session mode remain alternatives when the application needs long-lived session behavior. Check the project dashboard’s connection string and current plan and network details before configuring the application. See Supabase’s connection guide and pooling and limits documentation.
Choose RDS Proxy when managed AWS integration is the priority
RDS Proxy is intended for supported Amazon RDS database deployments. AWS says the proxy can preserve application connections through database failover, but actual reuse depends on whether requests permit multiplexing. PostgreSQL-specific limitations documented by AWS include lack of client CancelRequest support, lastval results that are not always accurate, and lack of streaming replication mode support. Availability varies by Region and engine version, so verify eligibility for the exact database and Region in the RDS Proxy documentation.
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Evaluate compatibility before switching
- Inventory session-dependent behavior. Search application code, migrations, ORM configuration, and driver settings for
SETstate, advisory locks,LISTEN, temporary tables, cursors, prepared statements, and query pipelining. - Decide which behavior must cross transaction boundaries. Compare each requirement with the relevant pooler’s official compatibility documentation. If state must survive across transactions, use a session-oriented connection mode or direct connection where appropriate, or redesign the operation so its state is transaction-scoped.
- Check the deployment boundary. PgBouncer can be self-managed; Supavisor and Supabase’s dedicated PgBouncer are Supabase service choices; RDS Proxy requires a supported RDS target.
- For RDS Proxy, inspect pinning and PostgreSQL limitations. Frequent pinning can reduce multiplexing even if the proxy still helps with connection handling.
- Test with the real stack. Run integration and representative load tests using the production ORM, driver options, session commands, and deployment topology. Measure connection reuse and application behavior under your traffic rather than extrapolating from generic connection-count claims.
What to conclude from the comparison
Transaction mode is viable only when the application’s behavior matches what the pooler can preserve or multiplex. PgBouncer offers protocol-level prepared-statement tracking with the required configuration; Supavisor transaction mode requires clients to avoid prepared statements and query pipelining; RDS Proxy can reuse connections across transactions but may pin sessions. Test compatibility first, then compare operational fit and measured behavior for your workload.
Quick Recap
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