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The cheapest GPU-hour is not necessarily the cheapest usable capacity. Compare the complete configuration—GPU count and VRAM, CPU and RAM, storage, region, networking, support, interruption behavior, and expected utilization—then match the purchase model to your workload. On-demand preserves flexibility, Spot or preemptible capacity can cut the bill but may stop without warning, and a one- or three-year commitment can lower rates while leaving you responsible for unused spend. A commitment also does not, by itself, reserve a GPU in a particular zone.
What a GPU cloud purchase really buys
A cloud GPU quote can describe several different things: one GPU-hour, a multi-GPU server, a reserved slice of regional capacity, or a contractual minimum spend. Those are not interchangeable units. A useful comparison starts by writing down the exact resource you need and the conditions under which it must run.
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- GPU: exact model, VRAM, interconnect, supported precision, MIG or other fractional options, and the minimum count needed for your software.
- Host: CPU cores, system RAM, local NVMe or network storage, operating-system image, drivers, and whether the quoted rate includes the machine around the GPU.
- Location: country, region, zone, data-residency requirement, network path to your data, and egress pricing.
- Commercial term: on-demand, Spot or preemptible, reservation, Capacity Block, one-year commitment, three-year commitment, or negotiated minimum.
- Operational promise: start-date certainty, replacement behavior after a failure, notice before interruption, support response, service credits, and data-preservation responsibilities.
Two offers that show the same GPU name can therefore have very different effective prices. Normalize all of those fields before deciding that one provider is cheaper.
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The following public figures were available on October 18, 2026. They are reference points for the specified regions and configurations, not guarantees that capacity will be available when you order.
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| Provider and configuration | Purchase basis | Published rate | What the number means |
|---|---|---|---|
| Google Cloud NVIDIA T4 | On-demand | $0.35 per GPU-hour | GPU-device price; the VM machine type is charged separately. |
| Google Cloud NVIDIA T4 | One-year resource-based commitment | $0.22 per GPU-hour | Discounted GPU rate under a one-year commitment; host and other charges still apply. |
| Google Cloud NVIDIA T4 | Three-year resource-based commitment | $0.16 per GPU-hour | Discounted GPU rate under a three-year commitment; host and other charges still apply. |
| CoreWeave NVIDIA HGX H100, eight GPUs, North America | On-demand | $49.24 per eight-GPU hour | Complete published system configuration; equivalent arithmetic is about $6.16 per GPU-hour before any other usage charges. |
| CoreWeave NVIDIA HGX H100, eight GPUs, North America | Spot | $19.71 per eight-GPU hour | Same eight-GPU configuration on Spot; equivalent arithmetic is about $2.46 per GPU-hour. |
| CoreWeave NVIDIA HGX H200, eight GPUs, North America | On-demand | $50.44 per eight-GPU hour | Complete published system configuration; equivalent arithmetic is about $6.31 per GPU-hour. |
| CoreWeave NVIDIA HGX H200, eight GPUs, North America | Spot | $20.93 per eight-GPU hour | Same eight-GPU configuration on Spot; equivalent arithmetic is about $2.62 per GPU-hour. |
Google’s resource-based commitments offer up to 55% off on-demand prices for most GPU types, but the exact discount depends on the resource and term. Google’s GPU page also says each GPU adds to the VM machine-type cost. A T4 figure should therefore never be multiplied by runtime and treated as the invoice total: add CPU, RAM, boot and persistent disks, snapshots, networking, software licenses, support, and idle time.
CoreWeave’s H100 and H200 figures are for eight-GPU HGX systems in North America. A workload that needs one GPU cannot assume it can buy one-eighth of that server, and a workload in another geography may see a different menu or no immediate capacity. Newer systems may be listed as Contact sales rather than instantly orderable. Recheck the live price and inventory at purchase time.
How to compare the full cost
Separate device price from system price
Some catalogs show a GPU add-on and bill the VM separately; others publish a complete server. Convert both offers to the same unit, such as cost per useful training hour for the required number of GPUs. Include the CPU and memory needed to keep the accelerators fed. A low GPU-only number can lose its advantage when the host is oversized, or when your framework requires a particular interconnect.
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Count boot disks, persistent volumes, local scratch, snapshots, backup copies, and the time spent transferring checkpoints and datasets. Ingress may be inexpensive or free in a particular service, while egress can become a major migration cost. Ask for the rate and any minimum before moving terabytes into a long-term commitment.
Model utilization, not theoretical runtime
Divide committed GPU-hours by the hours your jobs will actually consume. Development notebooks, queue delays, data preparation, failed runs, maintenance, and idle reservations all reduce utilization. A 50% discount is unattractive if only half of the purchased capacity is used. Build at least three cases: expected utilization, a delayed project, and a workload that ends early.
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Account for software and people
Driver images, orchestration, observability, support, license fees, and engineering time can exceed a small difference in hourly GPU price. Record whether the provider supplies the required CUDA or framework version, who patches the host, and which support tier covers a failed node.
Choosing on-demand, Spot, reservations, and commitments
On-demand
On-demand capacity has no long-term usage obligation and is the default for uncertain projects, interactive development, and workloads whose start time matters but whose future duration is unknown. You pay the current published rate while the resource runs. The trade-off is exposure to price changes and possible capacity shortages in popular zones.
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Spot is appropriate when a job can pause, checkpoint, and restart on another machine. Amazon EC2 describes Spot Instances as “a cost-effective choice if you can be flexible about when your applications run and if they can be interrupted.” Google’s capacity documentation says resources are provisioned as capacity becomes available but “can be preempted at any time.”
Before selecting Spot, verify the interruption notice, checkpoint interval, restart automation, maximum retry behavior, and whether local scratch disappears. Keep checkpoints on durable storage and make workers replaceable. Spot is a poor fit for an uncheckpointed final training run, a live interactive service, or a deadline with no recovery margin.
Reservations and Capacity Blocks
A reservation or Capacity Block is a capacity mechanism, not merely a price discount. It can improve confidence that a specified GPU count will be available in a specified location and time window, subject to the provider’s terms. It may require advance payment, a fixed region or zone, and less flexibility if the project changes.
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Resource-based commitments and similar discounts
A commitment exchanges flexibility for a lower rate over a defined term. Google lists one- and three-year resource-based commitments and states that most GPU types can receive discounts of up to 55% from on-demand prices. The discount applies to the committed resource, while associated VM, storage, network, and support charges remain separate unless the order says otherwise.
Do not confuse a discounted price agreement with guaranteed placement. Google explicitly states: “A commitment provides a 1- or 3-year discounted price agreement, but it doesn’t reserve capacity in a specific zone.” If a future launch requires a particular zone, pair the commercial commitment with an appropriate reservation or Capacity Block and confirm the quota.
AWS purchase vocabulary
AWS presents On-Demand, Savings Plans, Reserved Instances, Spot, Capacity Reservations, and Capacity Blocks as distinct tools. Compare what each one commits you to: a billing discount, an instance shape, a time window, or physical capacity. The names do not imply identical cancellation or capacity rights, so read the governing terms for the exact product and account.
Availability is a geography and quota problem
GPU inventory is not uniformly distributed. Google notes that GPU devices are available only in specific zones within some regions. A region that appears to offer a model may have no free capacity in the zone your network, residency policy, or reservation requires.
- Choose acceptable locations. List a primary region, fallbacks, data-residency limits, latency targets, and maximum egress distance.
- Check the exact SKU and zone. Confirm the model, GPU count, host type, interconnect, and minimum allocation. Do not rely on a regional model filter alone.
- Verify quota before scheduling. Request the required GPU quota early; approval does not itself create inventory.
- Test partial-provisioning behavior. Ask whether a multi-GPU request waits, fails atomically, or starts with fewer devices. Distributed training can be unusable when only part of the requested topology arrives.
- Reserve only when the date matters. Use a reservation or Capacity Block for a firm launch window, and obtain its zone, cancellation, and replacement terms in writing.
Specialist clouds may publish clearer Spot and on-demand menus, but a listed SKU is not proof of instant purchase. Capacity and prices are volatile; confirm both immediately before signing and again before a scheduled run.
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Contract terms that determine your real risk
Request the order form and governing service terms before committing money. A sales presentation is not a capacity guarantee or an exit right. Put material promises in the signed order.
Cancellation and unused minimums
Google’s public rule for resource-based commitments is unambiguous: “After you purchase a commitment, you can’t cancel or delete it. The commitment remains active until its specified end date.” Budget for the full obligation even if a project is cancelled, delayed, or migrated. The Compute Terms Observatory also records AWS reserved capacity as noncancellable and nonrefundable; verify the current product terms rather than assuming an exception.
Renewal, payment, and price changes
- Does the term end automatically, or does it auto-renew?
- Is payment monthly, upfront, or tied to a minimum spend?
- Can the provider change the rate, surcharge, or tax treatment during the term?
- What happens to unused credits or prepaid balances at expiry?
Substitution and portability
Ask whether the provider may substitute a different GPU model, host generation, region, or interconnect. A substitution right should define performance, VRAM, compatibility, notice, and your right to reject or terminate. Confirm that checkpoints can be exported in standard formats, that you can retrieve data after termination, and that the provider will assist with migration if a service is withdrawn.
Capacity, service levels, and remedies
For specialist clouds, specify the minimum GPU quantity, delivery date, region or zone, outage credits, replacement obligation, and escalation path in the order form. Define whether a missed start date permits termination or only a service credit. Clarify liability caps, maintenance windows, incident notification, and who bears the cost of re-running interrupted work.
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Data exit and egress
Record data-egress rates, deletion timing, snapshot export formats, encryption-key ownership, and the period available to download data after termination. A nominally cancellable contract can still be expensive to leave if checkpoints are trapped in proprietary storage.
A procurement sequence that limits lock-in
- Write the minimum specification. State GPU model, VRAM, count, interconnect, region, start date, runtime, storage, and interruption tolerance. Mark which items are mandatory and which are substitutes.
- Price identical configurations. Compare on-demand and Spot or preemptible offers from at least two providers using the same host, storage, region, and support assumptions. Convert multi-GPU prices to the same unit without hiding the minimum GPU count.
- Validate capacity. Check live zone inventory and quota. If the workload has a fixed launch date, obtain a written reservation or Capacity Block proposal and its failure remedy.
- Run a utilization model. Include idle time, failed jobs, checkpoint storage, egress, support, and a delayed-project scenario. Compare total committed spend with the flexible alternative, not just the discounted GPU line.
- Negotiate the order form. Add cancellation or termination-for-convenience rights where possible, substitution limits, portability, renewal notice, outage credits, delivery obligations, egress rates, and liability language.
- Prove portability before production. Build images from reproducible definitions, keep infrastructure configuration in version control, test a second provider, and export a restorable checkpoint.
- Stage the commitment. Keep uncertain demand on on-demand or Spot capacity; commit only the stable baseline after measured utilization supports it.
Decision matrix by workload
| Workload | Usually suitable starting point | Why | Controls to require |
|---|---|---|---|
| Interactive development | On-demand, possibly fractional or small GPU | People value immediate access and can stop machines between sessions. | Automatic idle shutdown, persistent home storage, and a fallback zone. |
| Batch inference | Spot or preemptible with on-demand fallback | Jobs can queue and retry, making interruption tolerable. | Checkpointed batches, idempotent tasks, retry limits, and durable output storage. |
| Fine-tuning | On-demand for short runs; Spot for restartable runs | Run length and checkpoint frequency determine interruption cost. | Frequent checkpoints, tested resume logic, and a deadline buffer. |
| Large distributed training | Reserved or Capacity Block for a firm window; on-demand for experiments | All workers need compatible topology at the same time. | Atomic multi-GPU allocation, zone confirmation, replacement terms, and a second-provider plan. |
| Production inference | On-demand or reserved capacity | Availability and predictable latency outweigh the deepest discount. | Multi-zone design, autoscaling limits, failover, and service-level remedies. |
| Long-lived predictable baseline | Commitment after utilization is proven | A stable floor can justify a lower rate. | Forecast review, no auto-renewal without approval, and an exit budget. |
Common failure modes and fixes
Comparing a GPU add-on with a full server
Symptom: the apparently cheaper quote produces a higher invoice. Fix: add VM CPU and RAM, storage, networking, support, and software to the GPU-only price before comparing.
Assuming a commitment guarantees a GPU
Symptom: a discounted commitment exists but the required zone cannot start the job. Fix: treat the commitment as a price instrument and obtain a separate reservation or Capacity Block when placement is critical.
Putting an uncheckpointed job on Spot
Symptom: an interruption erases hours or days of work. Fix: persist checkpoints, automate resume, and keep an on-demand fallback for deadline-sensitive stages.
Accepting a multi-GPU offer without checking topology
Symptom: all GPUs are present but distributed training scales poorly or fails. Fix: confirm interconnect, host type, minimum allocation, driver support, and whether partial provisioning is possible.
Leaving exit language to a sales email
Symptom: the provider says a model, region, or delivery date is no longer available, while the minimum spend continues. Fix: put substitution limits, delivery obligations, termination rights, credits, and export assistance in the signed order.
Bottom-line buying rule
Buy flexibility until demand and utilization are demonstrated. Use Spot or preemptible GPUs only with durable checkpoints and automated recovery. Use reservations or Capacity Blocks when a specific launch window and location matter. Sign a one- or three-year commitment only after modeling unused capacity and confirming cancellation, renewal, substitution, portability, egress, and service remedies in the contract. The winning offer is the lowest cost for reliable, recoverable work—not the lowest number in a GPU-price column.
Quick Recap
Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
