All pricing options
us-central1 · USD · monthly = 730 hrs
Prices exclude local taxes
| Term | Hourly | Monthly | Savings |
|---|---|---|---|
On-Demand | $1.5200 | $1109.59 | Baseline |
1-Year Committed Use Discount (CUD) | $0.9576 | $699.02 | -37% |
3-Year Committed Use Discount (CUD) | $0.6840 | $499.35 | -55% |
Preemptible / Spot | $0.4886 | $356.65 | -68% |
You could save $752.95/mo on this instance with the right pricing model.
Find my savings Monthly cost estimator
Estimate your spend based on actual usage.
730 h
1
Estimated monthly
$1109.59
730 hrs × $1.5200/hr × 1
Compute
vCPUs i32
Memory i120 GB
Physical processorIntel Broadwell, Intel Haswell, Intel Ivy Bridge, Intel Sandy Bridge, Intel Skylake
Nested VirtualizationNot supported
Sole TenantNot supported
GPUNone
Within N1 family
f1-microshared vCPU
g1-smallshared vCPU
n1-standard-11 vCPU
n1-highcpu-22 vCPU
n1-standard-22 vCPU
n1-highmem-22 vCPU
n1-highcpu-44 vCPU
n1-standard-44 vCPU
n1-highmem-44 vCPU
n1-highcpu-88 vCPU
n1-standard-88 vCPU
n1-highmem-88 vCPU
n1-highcpu-1616 vCPU
n1-standard-1616 vCPU
n1-highmem-1616 vCPU
n1-highcpu-3232 vCPU
n1-standard-3232 vCPU
n1-highmem-3232 vCPU
n1-highcpu-6464 vCPU
n1-standard-6464 vCPU
n1-highcpu-9696 vCPU
n1-highmem-6464 vCPU
n1-standard-9696 vCPU
n1-highmem-9696 vCPU
Lock in this rate across your fleet — typically save 30–40%
Connect your Google Cloud account to apply this pricing logic to every running instance automatically.
Networking
Max egress bandwidth32 Gbps
Tier 1 bandwidthN/A
Enhanced networking iYes (gvnic/virtio)
IPv6 supportYes (Dual-stack VPC)
Storage
Max persistent disks128
Max disk size512 TB
Local SSDsSupported (Scratch Disk)
Compare with another instance
vs.
Specn1-standard-32f1-microΔ
vCPUs32shared—
Memory120 GB0.6 GB-99%
Hourly Price$1.5200$0.0076-99%
Monthly Price$1109.59$5.55-99%
Cost optimization
GCP recommender alerts can cut this bill ~30-40%.
Connect your GCP account. We'll identify every underutilized n1.* instance and show exactly where downsizing, cleaning up orphaned disks, or shifting to CUDs saves money — with zero code changes.