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This guide gives you a repeatable way to compare GPU clouds using equivalent hardware and a measured workload instead of headline rates.
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Start with completed-workload cost, not the GPU-hour
A GPU-hour is only one input to your bill. A useful comparison is:
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Effective cost per productive GPU-hour = (GPU charges + host CPU/RAM + storage + network transfer + orchestration + retry cost) ÷ productive GPU-hours
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For a training job, use the total cost of a successful run, including checkpoint restores and failed attempts. For inference, use cost per request or per million tokens at the target concurrency and latency, not an idle instance’s hourly rate.
Cloud invoices often separate the GPU from the virtual machine. Google Cloud says each attached GPU adds a charge to the VM’s machine type, so a GPU price alone does not represent the full instance cost. Lambda warns that an instance is billed for as long as it is running, even when it is actively unused; shutdown automation therefore changes your effective rate.
What belongs in the denominator
- Training: completed steps, samples or tokens after failed and restarted work is included.
- Serving: successful requests at the required latency, batch size and uptime.
- Interactive development: productive hours only; include notebook idle time and environment startup.
If one provider completes a run 20 percent faster but costs 10 percent more per GPU-hour, it may still be cheaper per completed run. Conversely, a low rate can lose its advantage when low utilization, slow startup or preemptions leave the GPU running without useful work.
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Define the workload before requesting prices
Write a one-page workload specification before collecting quotes. Without it, providers will quote different configurations that cannot be compared.
- Model and mode: model family, parameter count, training, fine-tuning, batch inference or online serving.
- Precision: FP32, FP16, BF16, INT8 or another format, because precision changes memory demand and throughput.
- Memory requirement: peak GPU memory, host RAM, checkpoint size and expected activation or KV-cache growth.
- Parallelism: number of GPUs, data or tensor parallelism, and whether workers need a fast interconnect.
- Region: the user’s data location, latency target and any residency requirement.
- Schedule: expected hours per day, days per month, deadline and tolerance for a queue.
- Data movement: bytes uploaded, bytes downloaded, checkpoint frequency and traffic to downstream systems.
- Reliability target: acceptable interruption rate, maximum restart time and checkpoint interval.
Record these values in the same units for every quote. A single-GPU fine-tune and a multi-GPU distributed training run are different products even when they use the same GPU model.
Normalize every SKU to an apples-to-apples configuration
For each candidate, record GPU model and count, usable GPU memory, host CPU and RAM, local and network storage, interconnect, region, image or software stack, billing granularity and maximum supported cluster size. Matching only the GPU name can hide a slower host, a weaker interconnect or a different software image.
Usable memory matters more than the model label. Two instances with the same nominal GPU can expose different memory limits, and a configuration that spills to host RAM may have a very different completion time. For multi-GPU work, document whether the GPUs are connected by the interconnect your framework expects; otherwise a lower hourly price can be offset by communication overhead.
Published prices that can be compared directly
| Provider and location | GPU | Published on-demand price | Price unit and qualification |
|---|---|---|---|
| Google Cloud, eligible zones within supported regions | NVIDIA T4 | $0.35 | Per GPU-hour, on demand; Google Cloud GPU page retrieved in 2026 |
| Google Cloud, eligible zones within supported regions | NVIDIA V100 | $2.48 | Per GPU-hour, on demand; Google Cloud GPU page retrieved in 2026 |
| CoreWeave, North America | GB200 NVL72 | $42.00 | Per hour; CoreWeave North America table retrieved in 2026 |
| CoreWeave, North America | HGX B200 | $68.80 | Per hour; CoreWeave North America table retrieved in 2026 |
These figures are not interchangeable units: Google’s figures are per attached GPU-hour, while the CoreWeave figures are per listed system hour. A GB200 NVL72 or HGX B200 system contains multiple GPUs, so divide a system price by the number of GPUs only if your workload can use them efficiently and the provider’s billing definition supports that comparison.
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Google states that Spot prices can be 60%–91% below on-demand for most machine types and GPUs. Spot prices can change up to once every 30 days, so treat the discount as a range rather than a guaranteed rate. CoreWeave publishes separate Spot prices where offered; obtain the current figure for the exact system and region.
Billing granularity changes short-job economics
Lambda’s billing documentation says on-demand instances are billed hourly in one-minute increments, while 1-Click Clusters are priced per GPU-hour. Filesystems have no minimum storage period, and Lambda states that users are not charged for ingress or egress. Confirm which product you are buying before applying those rules: a cluster, instance and filesystem can have different meters.
For a simple arithmetic check, eight hours on one Google Cloud V100 at the published $2.48 GPU-hour rate equals $19.84 in GPU charges before the VM, storage and network costs. It is not a quote for the complete machine.
Calculate storage and transfer instead of guessing
Storage
Separate persistent filesystem time from compute time. Include the dataset, checkpoints, container layers and logs, and note whether storage persists after the instance is stopped. A filesystem with no minimum storage period can be deleted immediately after a run; a storage product with a longer commitment can continue billing after compute ends.
For each run, estimate:
- Average stored terabytes multiplied by the storage rate and the number of days retained.
- Temporary disk consumed during preprocessing, shuffling and checkpoint conversion.
- Read and write operations if the storage product meters them separately.
Egress
Compute outbound bytes at the workload boundary, not just the dataset size. Include model downloads to customers, generated media, checkpoints copied to another provider, logs sent to an external system and cross-region replication. The basic calculation is:
Egress cost = billable outbound GB × provider price per GB
Then check whether the link can move that volume in the time available. Google Cloud’s networking documentation lists a 7 Gbps total egress limit for many machine series, with higher limits for specific newer families. A nominally free transfer is still operationally expensive if the bandwidth cap extends your instance runtime.
CoreWeave states that customers pay no ingress, egress or transfer fees, while Lambda states that users are not charged for ingress or egress. Treat both as current vendor statements and verify the exact service, region and traffic path: a special product, third-party network, marketplace image or destination outside the provider’s stated scope can have different terms.
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When a provider does charge, convert its rate to dollars per GB and multiply by the actual bytes moved. A 10 TB export is roughly 10,000 GB using decimal units; at a hypothetical $0.05 per GB, the transfer line would be $500. Use the provider’s current rate in the calculation rather than this illustration.
Check capacity before comparing price
Availability is a capacity and geography problem. A provider may list a GPU publicly while having no capacity in your required zone or no quota for your project.
Capacity checklist
- Identify the exact region and zone where the workload must run.
- Check that the GPU is offered in that zone, not merely somewhere in the region. Google notes that GPU devices are available only in specific zones within some regions.
- Request or verify quota for the GPU count, vCPU count and addressable regional capacity.
- Ask whether capacity is on-demand, reservation-only, queued or available through a separate cluster product.
- Record the largest cluster size you can obtain and the expected startup time.
- For Spot, document the interruption notice, reclaim behavior, restart path and checkpoint location.
Run this check for every zone you might use. Moving to another zone can change latency, data-transfer pricing, image availability and compliance status.
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On-demand
On-demand is appropriate when the deadline matters more than the lowest possible rate, utilization is uncertain or the job is difficult to restart. It also provides a clean baseline for measuring other options. Automate shutdown because a running but unused instance continues to accrue charges.
Spot or interruptible capacity
Spot can be attractive for checkpointed training, batch inference and experimentation. Google’s published 60%–91% discount range is substantial, but the price can change and capacity can be reclaimed. Calculate expected cost with interruption overhead:
Expected Spot cost = advertised run cost + (probability of interruption × restart work and waiting cost)
Use frequent, validated checkpoints stored outside the ephemeral instance. Measure the time to restore, reload the dataset and rebuild the software environment. If a restart takes longer than the work saved by the discount, Spot is not the economical choice.
Reservations and commitments
Commitments or reservations can make sense when GPU utilization, region and model are predictable for a long period. Compare the committed spend with the on-demand baseline after accounting for unused hours, reservation lead time, cancellation terms and the risk that a newer GPU will better fit the workload before the commitment ends. A reservation does not remove the need to verify quota and physical capacity.
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Benchmark a representative slice
Price sheets cannot reveal throughput, startup latency or failure behavior. Run the same representative benchmark on each shortlisted configuration.
- Prepare the identical image: pin the framework, CUDA or accelerator runtime, drivers, model revision and dataset shard.
- Warm up: discard initialization iterations, then measure steady-state throughput at the target batch size and concurrency.
- Measure completion: record time per training step, tokens per second or requests per second, plus GPU utilization and memory headroom.
- Measure startup: time quota allocation, VM launch, image pull, data staging and first useful computation.
- Exercise recovery: stop or interrupt a test worker, restore a checkpoint and record lost work and restart time.
- Measure data movement: log input and output GB, transfer duration and any throttling.
- Reprice: multiply measured runtime and bytes by the provider’s current rates, then divide by productive work.
Use enough iterations to expose thermal throttling, storage-cache effects and communication overhead. For multi-GPU jobs, test the full worker count; single-GPU results do not predict scaling.
Provider-specific questions to ask
Google Cloud
- Which exact zones currently offer the requested GPU and machine type?
- What quota is required, and is approval separate from capacity availability?
- What are the VM, disk and network charges in addition to the GPU rate?
- What egress limit applies to the selected machine family? Google lists 7 Gbps total for many series and higher limits for specific newer families.
- What is the current Spot price and interruption behavior for the zone?
The published T4 rate is $0.35 per GPU-hour on demand and the V100 rate is $2.48 per GPU-hour on demand, based on Google’s 2026-retrieved table. Attach those rates to the full VM configuration and zone rather than treating them as standalone instance prices.
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CoreWeave’s North America table lists GB200 NVL72 at $42.00 per hour and HGX B200 at $68.80 per hour, with separate Spot prices where offered. Ask whether the quoted hour covers the complete listed system, what host resources and storage are included, and how quickly the required cluster can start. CoreWeave states that it has no ingress, egress or transfer fees; confirm the statement for the exact service and destination you will use.
Lambda
Lambda bills on-demand instances hourly in one-minute increments and prices 1-Click Clusters per GPU-hour. Its documentation says filesystems have no minimum storage period and that ingress and egress are not charged. Ask which meter applies to your deployment, how idle instances are stopped, and what startup and interruption behavior applies to the chosen capacity type.
Use a decision matrix instead of a single winner
| Workload characteristic | Evaluation priority | Suitable purchasing posture |
|---|---|---|
| Unpredictable experiments | Minute-level billing, fast startup and no long commitment | On-demand baseline; use Spot only for restartable trials |
| Long, checkpointed training | Effective cost after interruptions and storage of checkpoints | Spot when capacity is reliable; reserve stable capacity for deadlines |
| Large outbound datasets or generated media | Per-GB egress price and achievable bandwidth | Prefer a provider and region whose transfer terms fit the data path |
| Distributed multi-GPU training | Interconnect, usable memory, cluster size and placement | Benchmark the complete cluster before committing |
| Latency-sensitive serving | Region, startup time, sustained throughput and headroom | Keep warm capacity and price per successful request |
This matrix does not replace measurement. It tells you which variables deserve the most weight for your workload.
Common mistakes and how to correct them
Comparing unlike billing units
Do not compare a per-GPU-hour rate with a multi-GPU system-hour rate without knowing GPU count and utilization. Convert both to the completed-workload metric.
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Ignoring host resources
A GPU can be starved by insufficient CPU, RAM, disk throughput or network bandwidth. Record host specifications and monitor utilization during the benchmark.
Assuming regional availability
A catalog listing is not a capacity guarantee. Confirm zone, quota, reservation path and maximum cluster size immediately before scheduling the run.
Leaving idle instances running
Use an inactivity timer, job-completion hook or infrastructure scheduler. Verify that the automation stops both the compute instance and any separately billed resources.
Underestimating retries
Spot interruptions, image-pull failures and data-loader errors all add runtime. Include checkpoint restore and lost work in the expected-cost calculation.
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Even when a provider states that egress is free, bandwidth limits, destination restrictions or longer runtime can affect cost. Measure bytes and transfer duration on the real path.
Reprice the decision every month
GPU catalogs, Spot rates, quotas and network policies change. Keep a dated worksheet containing the SKU, zone, GPU and host configuration, storage retained, measured throughput, startup time, interruption history, outbound GB and the rate used. Re-run the representative benchmark after a major GPU, image or region change.
For each candidate, report three numbers to stakeholders: total cost per completed run, cost per productive GPU-hour and cost per unit of useful output. Include the assumptions and the date so a later price change does not look like a performance regression.
Bottom line
Select a GPU cloud by the cost and reliability of finished work. Normalize memory, interconnect, host resources, software image, region and billing unit; verify zone capacity and quota; measure startup, throughput and interruption recovery; and multiply the actual outbound bytes by the applicable transfer terms. On-demand is the reference for certainty, Spot is a discount with interruption risk, and commitments are justified only when utilization and location are predictable. A provider with a higher GPU-hour can be the economical choice if it finishes faster, starts when needed and avoids a large egress bill.
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.
