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The cheapest advertised GPU-hour is rarely the cheapest way to finish an AI workload. Compare the total cost of completed training steps or inference requests, including the host machine, storage, utilization, startup time, retries, interruptions and outbound data. A provider with a higher GPU rate can cost less when it delivers the memory, interconnect, capacity and data-transfer policy your workload actually needs.
Use a repeatable process: define the workload and region, normalize equivalent hardware, calculate effective cost per productive GPU hour, verify capacity before committing, benchmark a representative run, and reprice the result as catalogs and policies change.
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Start with completed-workload cost, not the GPU-hour
A GPU-hour is only one line on an AI bill. Cloud providers may charge separately for the virtual machine that hosts the accelerator, attached storage, network transfer and time spent waiting for capacity or recovering from failures. A lower accelerator rate can therefore produce a higher cost per training run or served request.
Use this equation for every candidate configuration:
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Effective cost per productive GPU hour = (GPU charges + host CPU/RAM charges + storage + outbound transfer + expected retry and interruption cost) ÷ productive GPU hours.
For serving, also calculate cost per successful inference request. For training, calculate cost per completed step, epoch or converged run. “Productive” excludes provisioning delays, idle time, data loading bottlenecks, failed attempts and time spent restarting from a checkpoint.
Keep two totals. The first is the provider invoice for the run. The second is the engineering-adjusted cost after utilization, startup and recovery overhead. The second number is the one to use for a purchasing decision.
Why utilization changes the answer
Lambda’s billing documentation says an instance is billed while it is running, even when it is actively unused. A job that keeps a four-GPU instance alive while only two GPUs are busy is paying for capacity it does not turn into useful work. Shutdown automation, queue draining and automatic deletion of temporary instances directly affect effective cost.
Include the host, not just the accelerator
Record the machine type, vCPU count, RAM, local disk, network-attached storage and operating-system image. Google Cloud states that each attached GPU adds cost to the VM’s machine type, so a published per-GPU rate is not the complete VM price. Host resources can also determine whether the GPUs stay fed with data; a cheaper host that starves the accelerator may lengthen the run.
Normalize hardware before comparing providers
Two listings are comparable only when they can perform the same job. Match GPU memory, GPU count, precision, host resources, interconnect, software image, storage design and region. A rate comparison that pairs a single accelerator with a multi-GPU configuration or ignores memory capacity is not an apples-to-apples comparison.
Fields for an apples-to-apples SKU table
- Workload: model, batch or serving mode, precision, sequence length and parallelism.
- Accelerator: model, count and usable memory.
- Host: CPU architecture, vCPUs and RAM.
- Communication: GPU-to-GPU interconnect and topology for distributed jobs.
- Storage: local scratch capacity, network filesystem capacity and read/write performance.
- Location: country, region and exact zone.
- Billing: per GPU-hour, per instance-hour, one-minute increments, cluster GPU-hours or another unit.
- Operations: startup time, quota process, maximum cluster size and interruption behavior.
Normalize the software image as well. CUDA, driver, framework and orchestration differences can change throughput or force an expensive rebuild. If one provider supplies a ready image and another requires manual setup, include the engineering time and any warm-up period in the comparison.
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Published rates in the available evidence
The following figures are current values identified in provider documentation retrieved in 2026. They are not interchangeable units: Google Cloud quotes individual GPU-hours, while CoreWeave quotes hourly systems in its North America table.
| Provider and configuration | Published rate | Billing and scope |
|---|---|---|
| Google Cloud NVIDIA T4 | $0.35 per GPU-hour on demand | Google Cloud GPU page; 2026 retrieval. The attached VM machine type is billed separately. |
| Google Cloud NVIDIA V100 | $2.48 per GPU-hour on demand | Google Cloud GPU page; 2026 retrieval. The attached VM machine type is billed separately. |
| CoreWeave GB200 NVL72 | $42.00 per hour | CoreWeave North America table; 2026 retrieval. |
| CoreWeave HGX B200 | $68.80 per hour | CoreWeave North America table; 2026 retrieval. |
CoreWeave lists separate Spot prices where offered, but the cited table does not establish a single Spot rate for every configuration. Do not substitute a Spot number from another region or date.
Google Cloud reports Spot discounts of 60%–91% for most machine types and GPUs. Google says Spot prices can change as often as once every 30 days, so treat the discount range as a variable rather than a guaranteed price.
Calculate storage and data-transfer cost from the bytes moved
Egress is outbound traffic from the provider to another network or destination. Data-heavy training pipelines, retrieval systems, video generation and model-serving APIs can move enough data that transfer costs exceed accelerator charges.
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- Measure input bytes copied into the cloud for each run, including repeated downloads after a retry.
- Measure output bytes returned to users, an object store, another cloud or an on-premises system.
- Separate one-time dataset movement from recurring traffic. A dataset copied once has a different cost profile from an inference service returning results every hour.
- Apply the provider’s current per-GB egress rate for the destination and region. Keep the rate date and destination beside the estimate.
- Add transfer generated by checkpoints, logs, monitoring exports and failed attempts.
The calculation is egress cost = billable outbound GB × applicable dollars per GB. If the provider uses tiers, calculate each tier separately. Do not treat free ingress as proof that outbound traffic is free.
Providers with stated no-egress policies
CoreWeave states that customers pay no ingress, egress or transfer fees, allowing data to move without those listed charges. This is a current vendor statement; verify the exact service, region, traffic destination and any product-specific terms before relying on it.
Lambda’s billing documentation says users are not charged for ingress or egress. Confirm that the traffic pattern and service you select fall under that documentation, and still model bandwidth and operational transfer time.
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Bandwidth can cost time even when it costs no dollars
Google Cloud networking documentation lists a 7 Gbps total egress limit for many machine series, with higher limits for specific newer families. A no-fee transfer policy would not remove a throughput bottleneck. Compare the achievable sustained rate, not only the price per GB. At 7 Gbps, a large checkpoint can take long enough to delay the next training step or make a serving endpoint miss its latency target.
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Record whether the limit is per project, instance, machine series or other scope. A cluster that appears to have many GPUs may still share a network ceiling that prevents all workers from reading or exporting data simultaneously.
Check availability before price
GPU availability is a capacity and geography problem. Google Cloud states that GPU devices are available only in specific zones within some regions. A price shown for a region is not evidence that the required GPU is obtainable in your preferred zone today.
Verify these capacity gates
- Exact zone: Confirm that the GPU, machine type and attached storage are offered in the zone you need.
- Quota: Check accelerator quota for the project and request approval before the launch date.
- Reservation path: Determine whether capacity requires a reservation, committed use arrangement or provider approval.
- Cluster size: Verify the maximum simultaneously allocatable GPU count, not merely single-instance availability.
- Startup time: Measure queueing, provisioning, image pull and readiness time.
- Interruption policy: For Spot capacity, document eviction notice, restart behavior and whether the same zone is likely to refill.
Ask for a capacity confirmation using the exact SKU, zone, GPU count and duration. “The provider offers this GPU” is a catalog statement; “the provider can start my eight-GPU job in this zone at 09:00 UTC” is an availability statement.
Choose on-demand, Spot, reservations or commitments by certainty
On-demand
Use on-demand when utilization is uncertain, launch timing matters or a failed run is expensive. The rate is usually the simplest baseline for comparing providers. Keep an automatic shutdown policy because idle runtime remains billable, as Lambda explicitly warns.
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Spot
Spot is appropriate when the job can checkpoint, resume and tolerate interruption. Google Cloud’s 60%–91% discount range can materially reduce compute cost, but prices can change up to once every 30 days and capacity can disappear. Add expected rework to the rate:
Expected Spot cost = nominal Spot cost × (1 + interruption fraction × restart overhead).
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Estimate interruption fraction from your own run history or a provider’s documented behavior; do not assume the largest advertised discount. Include checkpoint storage, reload time, lost steps and any duplicate data transfer.
Reservations
Use a reservation when a known event requires capacity in a particular zone and date window. Compare the reservation’s commitment and lead time with the cost of occasional on-demand launches. A reservation that cannot be placed in the required zone does not solve the operational problem.
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Commit only the baseline utilization you can forecast. Separate always-on serving capacity from variable experiments. A commitment can lower unit cost while increasing the cost of idle capacity; model seasonal demand, model migrations and the risk that a newer accelerator makes the committed SKU unattractive.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.A step-by-step provider evaluation
- Define the workload. Write down model, batch or serving mode, precision, memory requirement, parallelism, target region, expected hours and bytes entering and leaving the provider.
- Select feasible SKUs. Remove any configuration that lacks enough memory, the required interconnect, the needed software image or the correct zone.
- Build the SKU table. Capture GPU model and memory, GPU count, host CPU/RAM, local and network storage, interconnect, region and billing granularity.
- Price the whole run. Add GPU, host, storage and expected transfer charges. Divide by productive GPU hours, completed training steps or successful requests.
- Add operational overhead. Include startup delay, idle periods, retries, checkpoint reloads and Spot preemptions.
- Check capacity. Confirm quota, exact-zone availability, reservation lead time, cluster-size limits and interruption behavior before treating a quote as usable.
- Benchmark representative traffic. Run the target model at target concurrency and measure startup latency, utilization, step or request time, failures, checkpoint overhead and outbound GB.
- Reprice monthly. GPU catalogs, Spot rates, quotas and egress policies change. Keep the date and region beside every figure.
Benchmark what the invoice cannot show
A short synthetic test can hide data-loader stalls, communication overhead or cold-start delays. Use the production framework, precision, batch size, tokenizer, checkpoint format and concurrency.
Training measurements
- Time to first usable step after allocation.
- Steady-state steps per second and GPU utilization.
- Scaling efficiency when moving from one GPU to the intended cluster size.
- Checkpoint write time, checkpoint size and restart time.
- Failed jobs, preemptions and duplicate work.
- Input and output GB per completed step or epoch.
Serving measurements
- Cold-start and model-load latency.
- Throughput at the target concurrency.
- Median and tail latency.
- GPU utilization and idle time between requests.
- Response bytes, logs and monitoring exports per request.
- Behavior during scale-out and capacity shortages.
Convert each result into a cost denominator the business understands: dollars per completed training step, dollars per converged run or dollars per million successful requests.
Common comparison mistakes and fixes
Comparing unlike billing units
A per-GPU-hour rate cannot be compared directly with a per-system hourly rate. Divide the system price by the number of GPUs only after confirming that all GPUs are included, then add host, storage and transfer charges.
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A cheaper accelerator that cannot hold the model or communicate fast enough may require smaller batches, more GPUs or a different parallelism strategy. Normalize usable memory and interconnect before looking at price.
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Using a regional price as a global price
CoreWeave’s cited GB200 NVL72 and HGX B200 figures are for North America. Google Cloud’s GPU availability is zone-specific. Record geography with every rate and test the exact deployment location.
Forgetting idle runtime
Instances continue billing while unused. Set timeouts, queue-based provisioning and post-job deletion; then verify that shutdown occurs after checkpoints and logs are safely written.
Assuming free egress means unlimited transfer
Lambda and CoreWeave state no egress charges in the cited documentation, but bandwidth, service scope and destination still matter. Google Cloud documents a 7 Gbps total limit for many machine series. Measure transfer time and concurrency alongside dollars per GB.
Treating Spot savings as guaranteed
Spot prices and capacity vary. Keep checkpoints frequent enough to bound lost work, maintain a fallback on-demand SKU and include restart costs in the decision.
A practical decision framework
Choose the provider and purchase mode that wins on the metric tied to your workload:
- Stable, high utilization: Compare commitments or reservations against the on-demand baseline, after confirming regional capacity.
- Unpredictable experiments: Start with on-demand and automate shutdown; test Spot only after checkpoint and restart behavior is proven.
- Fault-tolerant batch training: Price Spot using expected retries and compare cost per completed step, not the advertised discount.
- Large datasets or frequent exports: Put egress dollars and transfer throughput in the first screening pass. A higher GPU rate with no egress fee may produce the lower completed-run cost.
- Distributed training: Prioritize interconnect, cluster availability and scaling efficiency before a small per-GPU price difference.
- Latency-sensitive serving: Include cold starts, idle floor, regional capacity and bandwidth limits in cost per successful request.
The final shortlist should contain a measured effective cost, a confirmed zone and quota path, a documented failure recovery plan and a date-stamped price sheet. Re-run that review whenever the model, region, traffic pattern or accelerator generation changes.
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.
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