Vertex AI vs GKE for GPU workloads: comparing the two GCP paths

Quick answer

Both run on the same underlying GPUs, so the per-hour hardware rates are close: an L4 machine like g2-standard-4 is about $0.71 per hour either way, and an A100 40GB via a2-highgpu-1g is about $3.67. The differences are structural. GKE Standard adds a cluster management fee of about $0.10 per hour, roughly $73 a month per cluster, but gives you full access to spot GPUs at 60 to 70 percent off and committed use discounts of 37 to 55 percent. Vertex endpoints bundle serving, scaling, and monitoring with no cluster fee but less discount leverage. GKE wins on large steady fleets; Vertex wins on small or spiky ones.

Google Cloud gives you two reasonable homes for a GPU workload, and the marketing does not make the cost difference obvious because the underlying hardware is identical. The distinction is in what surrounds the GPU: cluster fees, discount eligibility, scaling behavior, and how much idle time each path encourages.

The hardware baseline

Machine	GPU	Approx per hour	Monthly at 24x7
g2-standard-4	1x L4 24GB	about $0.71	about $518
g2-standard-16	1x L4 24GB	about $1.34	about $978
a2-highgpu-1g	1x A100 40GB	about $3.67	about $2,679
a2-highgpu-8g	8x A100 40GB	about $29.39	about $21,455
a3-highgpu-8g	8x H100 80GB	about $88	about $64,240

These are US list rates. Both paths consume the same capacity, so if you compare only the GPU line, the two options look identical and the decision appears arbitrary. It is not.

Where the costs diverge

Factor	Vertex AI	GKE with GPU node pools
Cluster fee	none	about $0.10/hr, roughly $73/mo per cluster
Spot GPUs	limited	full access, 60 to 70 percent off
Committed use discounts	partial	37 percent for 1 year, up to 55 for 3
Scale to zero	supported on some products	yes, with node pool autoscaling
Packing multiple models	limited	full control, time-slicing and MIG
Operational effort	low	high

The cluster fee is trivial next to GPU costs, about $73 a month against thousands. The discounts are not. A fleet of ten a2-highgpu-1g nodes costs about $26,790 a month at list. The same fleet on three-year committed use runs roughly $12,000, and a spot-heavy batch fleet can run under $9,000. Vertex gives you less access to those levers, which on a large steady workload is the dominant term. Autopilot pricing behaves differently again, as covered in GKE Autopilot pod pricing.

Where Vertex is cheaper in practice

Vertex wins when the alternative is a badly run cluster. A GKE GPU node pool with a minimum size of two nodes that serves an endpoint used four hours a day costs about $1,036 a month on L4 hardware for roughly $170 of useful work. A Vertex endpoint that scales down, or a batch prediction job that runs only when invoked, costs a fraction of that. Vertex also avoids paying for the cluster to exist during periods when nothing is running, and for small teams it removes the node upgrade, driver management, and autoscaler tuning work entirely. The management overhead framing in Vertex AI cost optimization applies directly.

A concrete comparison

Scenario	Vertex approx monthly	GKE approx monthly	Better
One model, 4 hrs/day traffic	about $150 to $300	about $591 (2 nodes plus fee)	Vertex
Three models, steady 24x7	about $1,554	about $1,627 list, about $1,050 with CUD	GKE with commitment
Batch scoring, nightly 3 hrs	about $65	about $95 with spot plus fee	Vertex, marginally
20 GPU training fleet	about $53,580	about $21,000 with spot	GKE, decisively

The costs that sit beside the GPU

Neither path is only GPUs. On GKE you also pay for the CPU nodes running system workloads, typically two or three e2-standard-4 at about $0.134 per hour each, roughly $290 a month, plus load balancers, persistent disks, and logging. Cloud Logging ingestion at about $0.50 per GiB beyond the free tier catches teams whose training jobs log verbosely, and a chatty job writing 200 GiB a month adds about $100. On Vertex, batch prediction and training jobs read from Cloud Storage, so keeping buckets in the same region as the compute matters just as much, since cross-region reads add per-GB charges on every epoch. Both paths also pay standard egress on anything leaving the region.

The hybrid, again

The pattern that keeps appearing across clouds also applies here: use the managed product for the long tail of small, spiky, low-traffic models where idle time would otherwise dominate, and run the large steady workloads on your own cluster where spot and committed use discounts cut 40 to 70 percent off the hardware. Trying to force everything onto one path is what produces either an over-operated cluster full of idle GPUs or a managed bill inflated by workloads that should have been committed.

Because node pools, machine types, accelerator counts, and minimum sizes are all Terraform, both paths can be priced before they exist. Check them against the resource catalog so the discount strategy is part of the design review rather than a retrofit after the first full month.

FAQ

Is Vertex AI more expensive than GKE for GPUs?

The raw hardware rates are similar because both consume the same GPU capacity, with an L4 machine around $0.71 per hour and an A100 40GB node around $3.67. The difference is discount leverage: GKE gives full access to spot GPUs at 60 to 70 percent off and committed use discounts of 37 to 55 percent, which on a large steady fleet makes it substantially cheaper than Vertex.

What does a GKE cluster cost on top of the GPU nodes?

GKE Standard charges a cluster management fee of about $0.10 per hour, roughly $73 a month per cluster, in addition to the node VMs. That is trivial next to GPU costs running into thousands, so it should not drive the decision. What matters far more is whether node pools are sized and autoscaled well enough to avoid paying for idle GPUs.

When is Vertex AI cheaper than running my own cluster?

When the alternative is a poorly utilized cluster. A GKE node pool with a minimum of two L4 nodes serving an endpoint used four hours a day costs about $1,036 a month for roughly $170 of useful work, while a scaling Vertex endpoint or an on-demand batch prediction job costs a fraction of that. Vertex also removes node upgrades, driver management, and autoscaler tuning.

How much do GCP committed use discounts save on GPUs?

About 37 percent for a one-year commitment and up to roughly 55 percent for three years, depending on resource type and region. A fleet of ten a2-highgpu-1g nodes at about $26,790 a month list can drop to roughly $12,000 under a three-year commitment, and a spot-heavy batch fleet can run under $9,000. Those levers are the main financial argument for running your own cluster.

Should I run both Vertex AI and GKE?

Often yes. Use the managed product for the long tail of small, spiky, low-traffic models where idle time would dominate a dedicated cluster, and run large steady training and inference workloads on your own GPU node pools where spot and committed use discounts cut 40 to 70 percent. Forcing everything onto one path produces either idle cluster GPUs or an inflated managed bill.

How does C3X help compare Vertex AI and GKE costs?

Node pools, machine types, accelerator counts, minimum sizes, and cluster settings are all defined in Terraform, so C3X can price both paths against a live catalog before either exists. That makes the cluster fee, the node baseline, and the effect of a chosen instance family explicit in the pull request, so the discount strategy is designed in rather than retrofitted.

What to do next

Compare GCP AI paths before you build one. C3X reads your Terraform and prices your resources against a live catalog. Start with the quickstart.