Cloud SQL vs. AlloyDB for PostgreSQL: what earns the migration

For a modest PostgreSQL application, we’d usually start with Cloud SQL. When it meets the required latency, availability, and cost, a faster database benchmark isn’t a reason to move. An existing system also has working deployment, connection, backup, and recovery procedures that a migration must replace or revalidate.

AlloyDB deserves a serious evaluation when the workload benefits from its architecture: substantial read capacity, demanding transactional work, or analytical queries that need to run near current operational data. Company size isn’t the deciding factor. A small team can have that workload, and a much larger company can have an application that runs comfortably on Cloud SQL.

Compare the configurations you would actually operate

“Cloud SQL versus AlloyDB” is too broad unless you specify editions and topology. Cloud SQL has Enterprise and Enterprise Plus configurations with different capabilities. AlloyDB organizes managed PostgreSQL-compatible compute around a cluster with shared, distributed regional storage, a primary instance, and optional read pools. Its primary can be highly available or a basic single-node instance. Google’s Cloud SQL edition guide and AlloyDB overview describe those choices.

Cloud SQL has a lower entry cost for small databases. AlloyDB’s minimum allocation is larger, even with a basic instance. Paying for unused capacity in every development and staging environment can matter more than a throughput improvement the application doesn’t need yet.

Check Google’s Cloud SQL pricing and AlloyDB pricing for your region and configuration. Include storage, backups, networking, and any applicable taxes alongside compute.

The entry row uses a zonal db-f1-micro and a basic c4a-highmem-1. Both are development/test configurations with no uptime SLA. The AlloyDB C4A option is available only in supported regions.

The entry row compares development cost floors, with very different CPU and memory allocations. Google’s AlloyDB machine types list the 1-vCPU C4A option and its regional availability; N2 starts at 2 vCPUs and 16 GiB. For production, price configurations that meet the same availability and recovery requirements. Google’s Cloud SQL HA guide and AlloyDB basic-instance guide explain the failover arrangements. Google recommends HA rather than basic instances for production AlloyDB deployments.

Storage can change the comparison as the database and read capacity grow. Google’s Cloud SQL replication guide describes the storage capacity required on replicas, while its AlloyDB pricing guide explains the shared storage layer. Calculate the whole deployment before assuming the product with the lower starting price will also have the lower bill for a large database with several readers.

Read pools are no longer a simple dividing line

Cloud SQL for PostgreSQL supports read pools in Enterprise Plus on its new network architecture; the associated primary must also use Enterprise Plus. The pool provides a common read endpoint across its nodes. Google’s read-pool documentation also describes the limitations, including the fact that healthy nodes can serve lagging data.

That means “we need load-balanced reads” isn’t enough to select AlloyDB. Compare the supported Cloud SQL configuration with the AlloyDB arrangement, including setup changes, capacity, and the consistency your application expects.

For either choice, identify which requests can use a read-only endpoint. A user opening a record immediately after editing it may need different routing from an analytical report. Test replication lag and session behavior; a read pool doesn’t automatically make every application read eligible for offloading.

Managed connection pooling isn’t exclusive to AlloyDB either. Both services offer transaction and session modes under their respective requirements. Compare the connection paths described in Google’s Cloud SQL pooling documentation and AlloyDB pooling documentation against your authentication, transaction, and session-state needs rather than assuming that a managed pool is transparent to every client.

Establish what is making the current system slow

Capture a representative busy period before proposing a migration. Separate time spent executing queries from time spent waiting for a connection, a lock, an external call, or the application itself. Check the plans for expensive queries and the number of concurrent operations reaching the database.

An oversized pool multiplied across Cloud Run instances can exhaust connections without requiring a different database engine. A report repeatedly scanning a large table may be better served by an index, a summary table, or a separate analytical path. Those repairs can remain necessary after a migration. The Cloud Run connection budget explains the scaling part of that problem.

Time-box the investigation. Once you can describe the constraint and a plausible architectural improvement, a representative comparison is more useful than another round of speculative tuning.

Give the columnar engine a relevant test

Google’s AlloyDB columnar engine can accelerate eligible analytical queries. It isn’t a blanket multiplier for every PostgreSQL operation. Point lookups, lock-heavy transactions, and queries that don’t use the accelerated path need to be evaluated on their own behavior.

Test a realistic dataset with the application’s query mix and concurrency, not only the one scan that makes the largest improvement. Include ongoing writes and the background work that normally shares the database. Inspect plans to verify which queries actually use the columnar path, and measure tail latency and error rates as well as throughput.

For reporting that can tolerate warehouse refresh latency, compare moving that work out of the transactional database too. For reporting that must use current operational state, AlloyDB may be a better fit.

Test recovery before accepting the performance result

Run the application against the candidate environment with its real connection library, pool configuration, authentication, extensions, and transaction patterns. PostgreSQL compatibility reduces migration work; it doesn’t remove the need to check supported versions, permissions, flags, and extensions.

Exercise failover and connection replacement, then verify that the application resumes without duplicating work or remaining stuck on dead connections. Rehearse a restore and check the restored data, not just the existence of a successful backup. Record the endpoint changes, permissions, and operator actions needed to recover.

Set acceptance criteria before the test: the workload and latency target, tolerable errors, recovery requirements, and cost ceiling.

Include the migration in the price

Price compute, HA, read capacity, storage, backups, networking, and the environments needed to operate the system. Then add the temporary cost of running both systems, changing infrastructure and monitoring, validating data, and rehearsing cutover. Apply commitments only when they match a workload you’re willing to commit to.

A rollback plan also needs to explain what happens to writes accepted after cutover. Keeping the old database running doesn’t make it current. The migration checklist covers those application and operational changes in more detail.