Google CloudGoogle Cloud AlloyDBDatabase

`google_alloydb_cluster` cost estimation

PostgreSQL-compatible managed database with up to 4x faster queries than standard PG. CPU $0.06814/vCPU-hour + memory $0.00822/GB-hour. Storage $0.30/GB-month. Designed for OLTP and analytical workloads.

Cloud AlloyDB for PostgreSQL is Google's high-performance managed PostgreSQL service. The google_alloydb_cluster resource creates the cluster; google_alloydb_instance resources represent primary and read pool instances. Pricing is per vCPU + memory + storage.

Pricing components: - vCPU: $0.06814/vCPU-hour ($49.75/month per vCPU) - Memory: $0.00822/GB-hour ($6/month per GB) - Storage: $0.30/GB-month - Backup storage: $0.10/GB-month

A typical AlloyDB instance (4 vCPU, 32 GB memory): - vCPU: 4 × $49.75 = $199/month - Memory: 32 × $6 = $192/month - Total compute: ~$391/month per instance - Plus storage at $0.30/GB-month

Compared to Cloud SQL PostgreSQL (4 vCPU, 16 GB): ~$200/month compute. AlloyDB is roughly 2x more expensive per resource unit. The justification: up to 4x faster OLTP queries, up to 100x faster analytical queries (via columnar engine), and built-in vector search for AI workloads.

When AlloyDB wins: - High-performance OLTP needing better-than-PG performance - HTAP (hybrid OLTP + analytical) on the same database - PostgreSQL workloads at scale that hit Cloud SQL limits - AI/RAG applications using pgvector

When Cloud SQL is better: - Small/medium workloads where 4x performance isn't needed - Cost-sensitive workloads - Applications that work fine on standard PostgreSQL

Read Pool instances scale read throughput at a fraction of the primary cost. Right for read-heavy workloads.

c3x estimates AlloyDB based on cluster + instance configurations (cpu_count, instance_type).

Terraform example

A minimal but realistic configuration that C3X can estimate.

resource "google_alloydb_cluster" "main" {
  cluster_id = "prod-alloydb"
  location   = "us-central1"
  network    = google_compute_network.main.id

  initial_user {
    user     = "postgres"
    password = var.alloydb_password
  }
}

resource "google_alloydb_instance" "primary" {
  cluster       = google_alloydb_cluster.main.name
  instance_id   = "primary-instance"
  instance_type = "PRIMARY"

  machine_config {
    cpu_count = 4
  }
}

resource "google_alloydb_instance" "read_pool" {
  cluster       = google_alloydb_cluster.main.name
  instance_id   = "read-pool-instance"
  instance_type = "READ_POOL"

  machine_config {
    cpu_count = 2
  }

  read_pool_config {
    node_count = 2
  }
}

Pricing dimensions

What you actually pay for when you provision google_alloydb_cluster.

Dimension	Unit	What's being charged
vCPU hours	per vCPU-hour	Compute capacity per vCPU. Applies to primary and read pool instances. $0.06814/vCPU-hour
Memory	per GB-hour	Memory allocation. Typically 8 GB per vCPU. $0.00822/GB-hour
Storage	per GB-month	Database storage, shared across all instances in the cluster. $0.30/GB-month
Backup storage	per GB-month	Backup storage beyond the retention window. $0.10/GB-month
Read Pool instances	per vCPU-hour	Read replicas at same per-vCPU rate. Can scale independently. Same as primary vCPU rate

Optimization tips

Common ways to reduce google_alloydb_cluster cost without changing the workload.

Use Cloud SQL for smaller workloads

50% vs AlloyDB

AlloyDB's performance premium (2x cost) makes sense for large/demanding workloads. For modest PostgreSQL needs, Cloud SQL is significantly cheaper. Benchmark your actual queries on Cloud SQL first; only move to AlloyDB if performance is inadequate.

Right-size with Read Pools

Variable based on workload

Don't over-provision the primary for read scaling. Read Pool instances scale read throughput independently and cost less than scaling the primary up. A 4 vCPU primary + 4 vCPU read pool is often better than 8 vCPU primary.

Use committed use discounts

25-52% on commitment

1-year CUDs save ~25%; 3-year save ~52%. For production AlloyDB workloads with stable baseline, CUDs are essential. Apply to vCPU and memory portions.

Tune memory:vCPU ratio carefully

AlloyDB instances allow configurable memory:vCPU ratios. For OLTP, 8 GB/vCPU works. For analytical workloads with large working sets, higher memory ratios may justify the cost. For light workloads, lower memory ratios save money.

Audit Read Pool node count

Variable

Read Pool nodes scale read capacity but bill independently. Three-node read pool = 3x compute cost. Monitor read traffic distribution; right-size node count to actual read load + 20% buffer.

FAQ

Is AlloyDB really 4x faster than standard PostgreSQL?

Per Google's benchmarks, yes for OLTP workloads. For analytical queries (using the columnar engine), up to 100x faster. Real-world performance gain depends on workload — applications heavily dependent on PostgreSQL's query planner improvements see the biggest gains. Always benchmark your specific workload.

When should I choose AlloyDB over Cloud SQL?

Three signals. (1) You've hit Cloud SQL's performance ceiling (single instance limits, replication lag, query latency). (2) Your workload needs the columnar engine for analytics on the same database. (3) AI/RAG applications using pgvector at scale benefit from AlloyDB's optimizations. For typical CRUD workloads, Cloud SQL is sufficient and cheaper.

Is AlloyDB compatible with PostgreSQL extensions?

Many but not all. Most popular extensions (pgvector, pg_stat_statements, PostGIS) are supported. Some niche extensions aren't. Check the AlloyDB compatibility list before migrating from self-managed PostgreSQL.

Can I migrate from Cloud SQL PostgreSQL to AlloyDB?

Yes. The Database Migration Service supports Cloud SQL → AlloyDB migrations with minimal downtime. The data formats are compatible. Application code (using standard PG drivers) generally works unchanged. Performance characteristics differ — test workload behavior after migration.