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Spot instances limit the cloud provider’s idle-capacity risk the most. The provider can reclaim spare GPU capacity when it needs it, shifting interruption and replacement risk to you. On-Demand avoids a long-term purchase commitment but still does not guarantee that GPUs will be available when you request them. Reserved capacity improves price and capacity predictability for stable demand, while creating a one- or three-year commitment that you may underuse. For many production systems, the least risky design is a committed baseline plus checkpointed Spot capacity for interruptible work.
What “provider risk” means in GPU cloud pricing
GPU capacity has two different risk owners. A provider risks paying for hardware that sits idle, while a customer risks paying for capacity that cannot be launched, is interrupted, or remains committed after demand changes. The three pricing models assign those risks differently.
Spot capacity is the clearest transfer of risk away from the provider. It uses excess capacity that can be reclaimed when the provider needs the hardware. AWS describes Spot Instances as spare EC2 capacity that Amazon may interrupt, and Google describes Spot VMs as excess capacity that may be preempted at any time. That reclaim mechanism is why Spot best limits provider utilization risk; this conclusion is an inference from the documented preemption rules, not a provider-published risk statistic.
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Spot, On-Demand and Reserved capacity compared
| Option | Who bears interruption or availability risk? | Price and volatility | Commitment | Best-fit workload |
|---|---|---|---|---|
| Spot | Customer bears interruption and replacement risk; provider can reclaim excess capacity. | AWS advertises savings of up to 90% versus On-Demand; Google advertises discounts of up to 91% for many machine types and GPUs. These are maximums, not guaranteed prices for a specific GPU, region or date. | No long-term purchase commitment, but availability changes with supply and demand. | Checkpointed training, batch processing, rendering, CI and other retryable jobs. |
| On-Demand | Customer avoids scheduled commitment risk but still faces failed launches when capacity is unavailable. | AWS bills On-Demand by the second. The rate is generally the most flexible baseline, not a capacity guarantee. | Stop whenever needed; no one- or three-year purchase commitment. | Interactive work, uncertain demand and workloads whose interruption cost is high. |
| Reserved capacity | Customer bears underutilization or configuration-mismatch risk; the commitment improves planning for stable demand. | Effective price can improve when utilization remains high and the reserved configuration is used. | AWS Reserved Instances use one- or three-year commitments for a consistent instance configuration and Region. | Stable, well-utilized GPU baselines and workloads where obtaining capacity matters more than flexibility. |
How Spot GPU instances work
Reclaimable excess capacity
A Spot request uses capacity the provider currently has available but does not need for committed or higher-priority demand. When that situation changes, the provider can reclaim the GPU. AWS says the only difference between On-Demand and Spot Instances is that Spot Instances can be interrupted by Amazon EC2; Google says a Spot VM can be preempted at any time.
AWS documents a two-minute interruption notice. That notice is useful only if the application can save state, stop cleanly or hand work to another worker within the window. Google’s “at any time” wording means you should design for preemption rather than depend on a predictable warning period.
Discounts are ceilings, not a quote
AWS documents savings of up to 90% compared with On-Demand. Google documents discounts of up to 91% for many machine types and GPUs. “Up to” is important: the actual discount depends on GPU model, host shape, Region, operating system, current supply and demand, and the time of purchase. Google also states that Spot prices can change as often as daily and that GPU charges are added to the machine-type charge.
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Spot availability can disappear exactly when demand rises. AWS states that Spot does not guarantee immediate availability or the aggregate capacity you request. A retry loop can therefore fail repeatedly if it asks for one scarce GPU type in one Availability Zone. Diversifying acceptable GPU models, instance sizes and Availability Zones can improve the odds of finding capacity, but it cannot turn Spot into a guarantee.
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Workloads that tolerate interruption
- Distributed or long-running training: write checkpoints to durable storage frequently enough that losing a worker does not erase substantial progress.
- Batch inference and data processing: divide work into independently retryable units and record completed items outside the worker.
- Rendering and simulation: use a queue so unfinished frames or jobs return to the pending state after reclamation.
- Continuous integration: make each build reproducible and restartable from source and dependency artifacts.
Do not make Spot the only layer for a stateful database, a user-facing service with strict availability objectives or any process that cannot recover from losing a machine. Keep durable state separate from the instance and maintain a fallback capacity class.
What On-Demand protects—and what it does not
Flexible billing and control
On-Demand lets you decide when to start and stop a GPU and avoids a one- or three-year purchase commitment. AWS bills these instances by the second, which suits experiments, interactive notebooks, changing project schedules and workloads whose future utilization is difficult to estimate.
No commitment is not the same as guaranteed capacity
An On-Demand launch can still fail with insufficient capacity. Paying the standard rate does not reserve a particular GPU unless you separately use a reservation mechanism. Capacity can be constrained by GPU model, host configuration, Region or Availability Zone, so an autoscaler should handle launch failures rather than assume every request succeeds immediately.
When On-Demand is the safer choice
- The workload is interactive and a user is waiting for the result.
- Usage is irregular enough that a reservation would often sit idle.
- The job is difficult to checkpoint or restart.
- You are still measuring utilization, model size, queue time or GPU memory needs.
On-Demand is usually the right temporary baseline while you collect those measurements. Once demand becomes steady and high, compare the cost of that flexibility with a reserved commitment.
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Reserved Instances and other capacity reservations
One- or three-year configuration commitments
AWS defines Reserved Instances as a one- or three-year commitment to a consistent instance configuration and Region. The commitment can lower the effective price and make budgeting easier when the same GPU shape runs for much of the term. In exchange, you carry the cost if the project ends, scales down, moves Regions or changes to a different GPU configuration.
Reserved capacity is therefore a utilization decision, not simply a discount decision. Estimate the hours you will actually consume, include expected growth and migration risk, and compare that with the price of remaining On-Demand. A nominally cheaper reservation can cost more in practice if half of it remains unused.
Capacity reservations and scheduled GPU blocks
AWS also lists Capacity Blocks for reserving a cluster of GPU instances for a defined schedule. Before purchasing, verify the exact GPU type, Region, start and end time, cluster size and current price. A scheduled block can address a known training window, but it is a poor match for demand that changes week to week.
When a reservation is justified
- GPU demand is stable enough that you can forecast high utilization for most of the term.
- The application needs predictable access and cannot rely on Spot retries.
- The team can keep the same instance configuration and Region.
- The cost of missing a training or serving window exceeds the cost of unused commitment.
A practical decision rule
Start with interruption tolerance
- If losing a worker is acceptable and the job can checkpoint, retry and wait for replacement capacity, start with Spot.
- If interruption is unacceptable but demand is uncertain or interactive, use On-Demand.
- If demand is stable, utilization is high and failed capacity acquisition is more expensive than commitment, evaluate Reserved Instances or a capacity reservation.
Then evaluate capacity concentration
Spot and On-Demand both become more fragile when every request targets one GPU model and one location. Define an ordered set of acceptable alternatives: for example, several compatible GPU families, multiple instance sizes and more than one Availability Zone. Test that your software and container images run on each alternative before an outage forces a decision.
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Finally price the failure, not just the hour
Compare the hourly rate with the operational cost of interruption: lost GPU time, checkpoint storage, queue delay, engineer intervention, missed service-level objectives and user-visible downtime. A 90% maximum Spot discount can be economically inferior to On-Demand if repeated preemptions discard hours of work. Conversely, paying On-Demand for idle overnight capacity can cost more than designing a resumable Spot queue.
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Commit a predictable baseline
Place the portion of demand that must be available continuously on On-Demand or an appropriately sized reservation. Size this baseline from measured minimum demand, not from the largest peak. Keep durable state, model artifacts, queues and checkpoints outside the GPU instance so a replacement worker can resume.
Use Spot for elastic capacity
Route retryable jobs to Spot workers. Configure interruption handling to mark the worker unavailable, flush any state that can be saved, requeue unfinished work and release attached resources. Maintain a bounded On-Demand fallback so the queue can drain when Spot supply tightens, while recognizing that AWS explicitly does not guarantee aggregate replacement capacity.
Checkpoint at a recoverable interval
Choose the interval from recomputation cost. If a training run can reproduce one hour of work, a five-minute checkpoint interval may be reasonable; if storage or synchronization overhead is high, measure the trade-off. Store checkpoints with versioned metadata so a new worker can verify the code, data shard and optimizer state before resuming.
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Observe the signals that change the decision
- Spot interruption and preemption counts by GPU type and location.
- Time to obtain replacement capacity after a failed launch.
- Checkpoint recovery time and the amount of recomputed work.
- Queue age, On-Demand fallback usage and effective cost per completed job.
- Reserved utilization and the hours of committed capacity left idle.
How to verify current GPU pricing
No single GPU price transfers cleanly between providers. GPU model, host machine, Region, operating system, storage, networking, taxes and commitment terms all change the total. Google publishes live Spot and GPU pricing pages, while AWS directs customers to its EC2 pricing and Spot pricing tools. Check the provider’s US pricing page on the day you purchase and record the exact configuration used for the quote.
For Spot, record the observed price and timestamp because supply-and-demand changes can alter it, including changes that occur daily on Google Cloud. For a reservation, record the committed term, Region, instance configuration, cancellation or modification rules and expected utilization. For On-Demand, include the cost of idle periods and any failed launch retries in your estimate.
Operational checklist before selecting a model
- Can the application checkpoint or replay work without corrupting state?
- How much work is lost after an interruption, and how long does recovery take?
- Which GPU models and locations are technically interchangeable?
- What happens when an On-Demand or Spot launch returns insufficient capacity?
- Is there a durable queue and a tested requeue path?
- What minimum GPU level must remain available during a supply shortage?
- Will a one- or three-year commitment still match the Region and instance configuration after planned migrations?
- Are the published discount figures being treated as provider maximums rather than guaranteed prices?
Bottom-line choice by workload
| Workload situation | Preferred starting point | Reason |
|---|---|---|
| Checkpointed batch or distributed training with flexible completion time | Spot, with retries and diversified capacity requests | The provider can reclaim capacity, but the job can resume after interruption. |
| Interactive development or uncertain project demand | On-Demand | You avoid a long commitment and can stop billing when work stops. |
| Continuous service with a known minimum GPU requirement | On-Demand baseline or Reserved capacity, plus Spot for elastic work | The baseline protects availability while Spot reduces the cost of retryable bursts. |
| Scheduled, high-utilization training window with fixed GPU shape and Region | Reserved capacity or a suitable Capacity Block | Predictable access can be worth the commitment when utilization is dependable. |
If the question is strictly which option limits provider risk, the answer is Spot: reclaiming excess capacity protects the provider from idle hardware. If the question is which option limits your operational risk, there is no universal winner. Use On-Demand for flexibility, Reserved capacity for stable and highly utilized demand, and a mixed design when production needs both predictability and economical elasticity.
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