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The cheapest GPU-hour is not automatically the cheapest way to finish your workload. On-demand capacity buys flexibility, spot capacity trades interruption risk for a steep discount, and reservations reduce the rate for a committed baseline that you can keep busy. The right choice depends on useful GPU utilization, restart tolerance, demand variability, and regional capacity—not on the advertised hourly number alone.
As a current North America example, an eight-GPU H100 instance is listed at $49.24 per hour on demand and $19.71 on spot. That is a large discount, but it only helps if your job can survive preemption and complete useful work after interruptions. Prices and availability change frequently, so treat all provider figures below as dated examples rather than a universal market rate.
What a neocloud is and why GPU pricing differs
Microsoft for Startups defines a neocloud as “a cloud provider built specifically for GPU compute and AI workloads rather than general-purpose enterprise applications.” These providers concentrate on dense GPU clusters, high-speed interconnects, fast local storage and software tuned for training, inference and other accelerated workloads.
Pricing is therefore commonly expressed per GPU-hour, or per hour for a multi-GPU instance. The meter may cover an entire host rather than one physical card. An eight-GPU price cannot be compared with a single-GPU price without checking the GPU count, memory, host RAM, local storage and network configuration.
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The market is expanding rapidly. Synergy Research Group, as reported by Microsoft for Startups, valued the neocloud segment at more than $25 billion in 2025 and projects it will approach $400 billion by 2031, with a compound annual growth rate near 58% from 2025 through 2031. That growth also means pricing, capacity and product names can change quickly.
How the three main pricing models work
On-demand capacity
On-demand is the flexible default. Crusoe describes it as billed per hour or per second, with no minimum commitment, for workloads where uptime, predictability and stability are critical. You request capacity when needed and stop paying when the instance is released, subject to the provider’s billing granularity and any attached services.
- Best fit: interactive development, uncertain demand, latency-sensitive production serving and jobs whose restart cost is high.
- Main benefit: you avoid a long commitment and generally receive the most predictable access among the three options.
- Main drawback: the hourly rate is the highest, and a busy project can spend substantially more than it would with a properly sized reservation.
Spot capacity
Spot pricing offers a discount in exchange for interruption risk. Crusoe says spot is intended for fault-tolerant workloads that can be stopped and restarted without major disruption. The provider can reclaim capacity, so your system must expect termination, requeue work and recover from the latest checkpoint.
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- Best fit: distributed training with frequent checkpoints, batch inference, rendering and other jobs with flexible completion times.
- Required engineering: automatic checkpointing, durable storage for checkpoints, restart logic, idempotent job submission and monitoring for preemption events.
- Poor fit: interactive sessions, strict latency objectives and workloads where rebuilding state costs more than the spot discount saves.
Reserved capacity
A reservation is a committed capacity allowance for a matching GPU product line. It is not a monthly averaging bucket. Crusoe’s reservation documentation says unused capacity does not roll over. If you run above the reserved quantity at a given moment, the excess is billed on demand or at a negotiated rate.
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This creates two separate planning questions: how much capacity you can keep busy as a baseline, and how you will handle peaks. A reservation should normally cover the stable floor. On-demand or spot capacity can absorb bursts, provided those burst workloads tolerate the corresponding price or interruption risk.
| Model | What you pay for | Interruption exposure | Use when |
|---|---|---|---|
| On-demand | Actual running time at the published flexible rate | Lowest among these options, subject to provider operations | Demand is uncertain or completion and uptime matter most |
| Spot | Actual running time at a discounted, reclaimable rate | Provider may stop or reclaim capacity | Jobs checkpoint, restart automatically and have flexible deadlines |
| Reservation | Committed allowance for matching GPUs; excess usage is charged separately | Capacity is planned, but the commitment can be wasted when idle | A measured baseline is stable enough to keep utilized |
Current CoreWeave North America price examples
The following figures are live examples from a North America pricing table and represent eight-GPU instances. They are not a worldwide price list or a guarantee of availability. Region, GPU count, VRAM, host memory and local storage differ between products, so compare equivalent configurations before drawing a conclusion.
| GPU instance (8 GPUs) | On-demand per hour | Spot per hour | Spot as share of on-demand |
|---|---|---|---|
| H100 | $49.24 | $19.71 | About 40% |
| H200 | $50.44 | $20.93 | About 41% |
| B200 | $68.80 | $34.11 | About 50% |
| L40 | $10.00 | $6.27 | About 63% |
| A100 | $21.60 | $9.65 | About 45% |
In this table, the H100 spot rate is roughly 60% below its on-demand rate, and the H200 spot rate is roughly 59% below. Those ratios are calculated from the listed figures and can change whenever rates or capacity change. The B200 discount is smaller in percentage terms, while the absolute hourly price is higher.
These examples do not establish which provider has the “best” H100 price. A lower number can represent fewer GPUs, less memory, different host storage, a different region or a less certain capacity class. Compare the complete instance specification and the probability of obtaining capacity at the time your workload runs.
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How to decide whether a reservation pays off
Measure the baseline, not the peak
Start with several weeks of workload data. Record the number of matching GPUs actually running, queue time, completed jobs, failed or preempted work and idle periods. The reservation candidate is the capacity you can keep busy during ordinary demand, not the maximum you need during launches or occasional training runs.
For example, if a team usually keeps a fixed group of GPUs occupied and occasionally doubles that fleet for a training cycle, reserving the usual group and handling the extra machines on demand or spot follows the documented non-rollover and overage rules. Reserving the peak would leave paid allowance idle during the normal period.
Use a provider-specific break-even calculation
Let R be the effective reserved rate, O the comparable on-demand rate, and U the fraction of the reservation allowance you actually consume. In the simplest case, the reservation saves money only when the value of the consumed discount exceeds commitment costs:
consumed GPU-hours × (O − R) > unused-allowance cost + commitment and migration costs
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If the reservation has a fixed commitment fee, include it in the right-hand side. If guaranteed capacity prevents expensive queueing or missed deadlines, assign that operational value separately. If spot is a realistic alternative, include expected preemption recovery time and failed-work cost rather than comparing rates alone.
There is no universal utilization percentage at which every reservation becomes profitable. The threshold changes with the negotiated discount, contract duration, GPU model, region, interruption cost and the value of guaranteed capacity. A provider’s specific reservation terms must determine the final calculation.
Plan for overages explicitly
Because usage above the reserved quantity is billed on demand or at a negotiated rate, set an alert before crossing the allowance. Define which jobs may burst, whether they can use spot instead, and who approves an on-demand overage. Without that policy, a reservation can coexist with unexpectedly high on-demand spend during a peak.
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Spot economics depend on useful output, not allocated time. A job that runs for ten hours at a 60% discount but loses two hours of work during repeated interruptions may cost more per completed result than a stable on-demand run.
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Workloads that usually fit spot
- Training jobs that checkpoint to durable storage at short intervals.
- Batch inference that can divide input into independently retried units.
- Rendering, simulation and media processing with resumable queues.
- Development experiments where completion time is flexible.
Workloads that usually do not
- Online serving with strict latency or availability targets.
- Large jobs that cannot restore model or optimizer state efficiently.
- Systems whose restart process requires manual intervention.
- Deadlines where a preemption would trigger a missed service-level objective.
Track preemption frequency, checkpoint duration, restart duration and the amount of recomputation. Divide total spend by completed useful work—such as finished training steps, processed documents or served requests—not by the number of GPU-hours allocated.
Costs beyond the GPU-hour
A GPU-hour is only one line on the bill. Before comparing offers, check:
- Storage: persistent volumes, snapshots and the durable location used for checkpoints can continue accruing charges after a GPU is stopped.
- Networking: data transfer between regions, external egress and high-speed cluster networking may have separate meters.
- Support and contracts: response-time commitments, minimum terms, cancellation rules and negotiated rates can alter the effective price.
- Capacity certainty: a low spot rate has little value if the required GPU is rarely available in your region.
- Software and orchestration: license, control-plane or managed-service charges may be separate from compute.
Crusoe states that its on-demand and spot options have no upfront setup fees and that network ingress and egress are not charged at the time of publication. Its managed inference product is billed per one million tokens, which is a different meter from renting GPU hardware and should not be mixed into a GPU-hour comparison.
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A practical selection workflow
- Define the unit of useful work. Choose a measurable output such as completed training steps, images rendered or requests served.
- Profile demand over time. Separate the steady baseline from launches, experiments and other bursts.
- Match the hardware. Compare the same GPU model, GPU count, VRAM, host memory, storage and region.
- Classify interruption tolerance. Document checkpoint frequency, restart automation and the maximum acceptable delay.
- Price all three paths. Calculate on-demand spend, expected spot spend after recovery losses and reservation spend including unused allowance and overages.
- Set operational controls. Add budget alerts, reservation-usage dashboards, preemption monitoring and automatic shutdown for idle instances.
- Recheck before committing. Prices and availability are volatile and region-specific; verify the provider’s current terms immediately before purchase.
How to keep the decision current
GPU supply, regional capacity and discount structures can move faster than a contract cycle. Re-run the comparison when you change GPU generation, region, workload schedule or checkpoint strategy. A reservation that was sensible for yesterday’s stable training queue can become wasteful after a project ends, while a spot strategy can become unreliable when a deadline tightens.
The durable rule is simple: reserve only the capacity you can keep usefully occupied, use on-demand for uncertain or high-consequence work, and use spot where your software is designed to be interrupted. Evaluate the result by cost per completed useful workload, including storage, transfer, queueing and recovery—not by the advertised GPU-hour alone.
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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.
