All pricing options
us-central1 · USD · monthly = 730 hrs
Prices exclude local taxes
| Term | Hourly | Monthly | Savings |
|---|---|---|---|
On-Demand | $5.4075 | $3947.47 | Baseline |
1-Year Committed Use Discount (CUD) | $3.4066 | $2486.81 | -37% |
3-Year Committed Use Discount (CUD) | $2.4335 | $1776.48 | -55% |
Preemptible / Spot | $1.1786 | $860.40 | -78% |
You could save $3087.07/mo on this instance with the right pricing model.
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Estimate your spend based on actual usage.
730 h
1
Estimated monthly
$3947.47
730 hrs × $5.4075/hr × 1
Compute
vCPUs i128
Memory i512 GB
Physical processorAMD Milan, AMD Rome
Nested VirtualizationNot supported
Sole TenantNot supported
GPUNone
Within N2D family
n2d-highcpu-22 vCPU
n2d-standard-22 vCPU
n2d-highmem-22 vCPU
n2d-highcpu-44 vCPU
n2d-standard-44 vCPU
n2d-highmem-44 vCPU
n2d-highcpu-88 vCPU
n2d-standard-88 vCPU
n2d-highmem-88 vCPU
n2d-highcpu-1616 vCPU
n2d-standard-1616 vCPU
n2d-highmem-1616 vCPU
n2d-highcpu-3232 vCPU
n2d-standard-3232 vCPU
n2d-highcpu-4848 vCPU
n2d-highmem-3232 vCPU
n2d-highcpu-6464 vCPU
n2d-standard-4848 vCPU
n2d-highcpu-8080 vCPU
n2d-standard-6464 vCPU
n2d-highmem-4848 vCPU
n2d-highcpu-9696 vCPU
n2d-standard-8080 vCPU
n2d-highmem-6464 vCPU
n2d-highcpu-128128 vCPU
n2d-standard-9696 vCPU
n2d-highmem-8080 vCPU
n2d-standard-128128 vCPU
n2d-highmem-9696 vCPU
n2d-highcpu-224224 vCPU
n2d-standard-224224 vCPU
Lock in this rate across your fleet — typically save 30–40%
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Networking
Max egress bandwidth32 Gbps
Tier 1 bandwidth100 Gbps
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.
Specn2d-standard-128n2d-highcpu-2Δ
vCPUs1282-98%
Memory512 GB2 GB-100%
Hourly Price$5.4075$0.0624-99%
Monthly Price$3947.47$45.53-99%
Cost optimization
GCP recommender alerts can cut this bill ~30-40%.
Connect your GCP account. We'll identify every underutilized n2d.* instance and show exactly where downsizing, cleaning up orphaned disks, or shifting to CUDs saves money — with zero code changes.