All pricing options
us-central1 · USD · monthly = 730 hrs
Prices exclude local taxes
| Term | Hourly | Monthly | Savings |
|---|---|---|---|
On-Demand | $9.4631 | $6908.07 | Baseline |
1-Year Committed Use Discount (CUD) | $5.9615 | $4351.92 | -37% |
3-Year Committed Use Discount (CUD) | $4.2587 | $3108.84 | -55% |
Preemptible / Spot | $2.0626 | $1505.69 | -78% |
You could save $5402.37/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
$6908.07
730 hrs × $9.4631/hr × 1
Compute
vCPUs i224
Memory i896 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-224n2d-highcpu-2Δ
vCPUs2242-99%
Memory896 GB2 GB-100%
Hourly Price$9.4631$0.0624-99%
Monthly Price$6908.07$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.