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A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11The cheapest advertised GPU is not automatically the cheapest usable GPU. Compare the same model, VRAM, node topology, region, billing mode and workload duration, then add storage, egress, minimum-billing waste, setup time and the expected cost of interruptions. A low hourly rate with no capacity—or with repeated evictions—can cost more than a higher, reliably available offer.
For 2026 snapshots, RunPod lists H100 PCIe at $2.89 per hour and H100 SXM at $3.99; Lambda lists H100 SXM configurations from $3.99 to $4.29 per GPU-hour; and CoreWeave lists an eight-GPU HGX H100 node at $49.24 per hour on demand or $19.71 spot. Those figures are useful only after you normalize the hardware and contract terms.
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Start with an effective usable GPU-hour
Provider price pages usually show a rental rate, not the cost of completed work. The number you need is the effective usable GPU-hour: the amount paid for GPU time that actually advances your job.
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Use this calculation for each quote:
effective usable GPU-hour = (GPU charges + storage + data egress + minimum-billing waste + setup-time cost + expected interruption/restart cost + reservation commitment) ÷ usable GPU-hours
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“Usable” means time during which your workload is running productively. A machine that takes an hour to provision, spends time copying checkpoints, or is evicted before saving progress contributes fewer usable hours than the billing clock suggests. Record the quote date, currency, geography, GPU variant and billing mode beside every calculation because both prices and capacity move with supply.
Normalize the hardware before comparing rates
- Match the exact GPU model and variant, such as H100 PCIe versus H100 SXM.
- Match VRAM and the number of GPUs per node.
- Record topology and interconnect requirements. An eight-GPU node with the required fabric is not equivalent to eight isolated single-GPU instances.
- Use the same region and start date.
- Separate community, secure, spot, on-demand and reserved capacity.
- Use the same expected run length and checkpoint frequency.
Convert node quotes to per-GPU figures
CoreWeave’s 2026 listing illustrates why this matters: an eight-GPU HGX H100 node is quoted at $49.24 per hour on demand and $19.71 per hour spot. Dividing by eight gives derived rates of $6.155 and about $2.464 per GPU-hour respectively. Those are arithmetic normalizations, not separate CoreWeave offers; the node’s topology and any node-level minimums still apply.
Published 2026 price snapshots
The following figures are dated examples, not universal market prices. Region, supply, billing mode and GPU configuration can change the amount you actually pay.
| Provider and offer | Published price | What the figure represents |
|---|---|---|
| RunPod H100 PCIe | $2.89/hour | 2026 Pod price example |
| RunPod H100 SXM | $3.99/hour | 2026 Pod price example |
| RunPod A100 PCIe/SXM | $1.79/hour | 2026 Pod price example |
| Lambda H100 SXM instances | $3.99–$4.29 per GPU-hour | Range across listed instance configurations in 2026 |
| Lambda 1-Click H100 Cluster, 16 GPUs | $6.16 per GPU-hour | Listed plan range from two weeks to one year |
| Lambda 1-Click H100 Cluster, 64 GPUs | $5.85 per GPU-hour | Listed plan range from two weeks to one year |
| Lambda 1-Click H100 Cluster, 256 GPUs | $5.54 per GPU-hour | Listed plan range from two weeks to one year |
| CoreWeave HGX H100 node, on demand | $49.24/hour per eight-GPU node | 2026 node quote; equivalent to $6.155 per GPU-hour by division |
| CoreWeave HGX H100 node, spot | $19.71/hour per eight-GPU node | 2026 node quote; equivalent to about $2.464 per GPU-hour by division |
RunPod describes its Pods as spanning “Thousands of GPUs across 30+ regions.” Lambda describes its pricing as “Clear, straightforward pricing for Instances, 1-Click Clusters™, and Superclusters.” These statements describe the products and advertised footprint, not a guarantee that your requested SKU is free in your preferred region at a particular moment.
Cross-provider H100 snapshot
GPUCost’s 2026 comparison table gives another set of example H100 prices: Vast.ai $1.47/hour, RunPod $2.89/hour, CoreWeave $4.25/hour, Google Cloud $9.80/hour and Azure $12.29/hour. The table does not provide a directly comparable AWS H100 value. Treat every figure in that comparison as a snapshot rather than a standing quote, and check whether the underlying entry is spot, community, on-demand or reserved before using it in a budget.
| Provider | GPUCost 2026 H100 example |
|---|---|
| Vast.ai | $1.47/hour |
| RunPod | $2.89/hour |
| CoreWeave | $4.25/hour |
| Google Cloud | $9.80/hour |
| Azure | $12.29/hour |
Account for costs that do not appear in the GPU rate
Storage and checkpoint traffic
Training usually needs a persistent location for datasets, container images and checkpoints. Ask whether the quoted storage is local ephemeral disk, persistent block storage or an object-storage service, and price both capacity and operations. A cheap ephemeral instance can become expensive if every restart requires a full dataset transfer.
Data egress and network path
Estimate the bytes leaving the provider, not just the bytes entering it. Include checkpoint copies to another region, serving traffic to users, and transfers to a separate data platform. For distributed training, confirm the internal network fabric and topology; a single-GPU price does not describe the performance of a multi-node job.
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Minimum billing and setup time
Minimums can dominate short jobs. If a provider bills a full hour or a larger unit after a few minutes, include the unused portion. Measure request-to-ready time and image or dataset preparation separately. A machine that is billed while an image is still downloading has a lower effective rate.
Commitments and reservations
Reservation discounts are meaningful only if the workload will consume the commitment. Add the value of unused reserved time to the jobs that actually run. For Lambda’s 1-Click H100 clusters, the listed 16-, 64- and 256-GPU prices apply to plans ranging from two weeks to one year; compare the commitment period with your expected utilization instead of treating the lowest per-GPU number as a flexible on-demand rate.
Interruption and restart cost
For spot or marketplace capacity, estimate how often an eviction can occur, how long a replacement takes and how much work is lost since the last checkpoint. A practical model is:
expected restart cost = eviction probability × (lost compute time + checkpoint reload time + queue time + operator time)
Checkpoint frequently enough that the expected lost work is acceptable, but account for the storage and I/O overhead of doing so.
Compare provider classes by the problem they solve
| Provider class | Typical strength | Trade-off to test |
|---|---|---|
| Hyperscalers: AWS, Google Cloud, Azure | Existing identity and access management, private networking, compliance controls, data services and multi-region redundancy | GPU prices and capacity constraints can be higher; your exact accelerator may require a quota request or a different region |
| GPU-native clouds: CoreWeave and Lambda | AI-focused infrastructure, GPU-specific products and cluster configurations | Confirm that the required SKU, topology and start date are actually available; cluster pricing may require a commitment |
| Marketplaces and aggregators: Vast.ai and similar services | Low prices by pooling third-party supply | Host quality, interruption policy, network route and persistence vary; validate each listing and host |
Measure availability instead of guessing
No controlled, longitudinal dataset establishes a universal ranking of realized provisioning availability for every GPU, region and provider. Availability is therefore a dated measurement, not a permanent provider attribute.
Send an identical capacity request
Ask every provider for the same specification:
- Exact GPU SKU and VRAM.
- Number of GPUs and node topology.
- Required interconnect or fabric.
- Region and acceptable fallback regions.
- Storage type and capacity.
- Billing mode: community, secure, spot, on-demand or reserved.
- Required start deadline and expected run duration.
Do not accept “an H100” as a sufficient answer. PCIe and SXM variants can have different performance, host design and availability.
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Log the complete placement experience
- Record the request timestamp, region, SKU, quantity and offer type.
- Measure request-to-ready time, including image and data initialization.
- Count failed placements, quota errors and cancellations.
- Record whether the provider placed you on a wait-list and how long it lasted.
- Note any substitution of GPU variant, region or topology.
- Repeat at different times and on multiple dates before drawing a conclusion.
RunPod separates Community Cloud and Secure Cloud, so record which one answered the request. Lambda separates first-come self-serve instances from 1-Click Clusters supporting 16 to 2,000-plus GPUs; a result from one product should not be generalized to the other.
Read the SLA as a contract, not a headline percentage
An SLA percentage is useful only when its scope matches the failure that would damage your job. AWS states, “AWS makes two SLA commitments for Amazon EC2.” Its documentation lists 99.99% Region-Level uptime when instances run concurrently across at least two Availability Zones and 99.5% for an individual instance; service credits range from 10% to 100% according to the measured uptime.
Google Cloud’s current SLA lists at least 99.99% monthly uptime for Premium Tier multi-zone instances and at least 99.9% for a single non-memory-optimized instance. Credits can rise to 100% when uptime falls below 95%.
Those commitments are not interchangeable. A multi-zone condition may require you to design for redundancy, while a single-instance commitment may not cover a failed training fleet. Before signing, identify all of the following:
- Whether the SLA applies to one VM, a multi-zone fleet, a reserved cluster or only network connectivity.
- Which regions, GPU products and billing modes are included.
- Exclusions for scheduled maintenance, customer action, storage, interconnect failures and force majeure.
- The measurement window and evidence you must provide.
- The maximum credit as a fraction of the affected bill.
- The claim deadline and submission process.
ClusterBid’s 2026 comparative analysis reports 99.95% for CoreWeave reserved contracts, 99.9% for Lambda on-demand and no explicit RunPod spot SLA. This is secondary analysis; verify the exact agreement offered to your account, especially for spot and marketplace capacity. A credit also does not restore lost training time, delayed launches or missed data-science deadlines, so resilience still matters.
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AWS
Use AWS when your GPU workload must live beside established IAM, private networking, compliance controls, data services or a multi-region failover design. Evaluate the individual-instance and Region-Level EC2 commitments separately, and confirm that your architecture actually runs across the Availability Zones required for the higher commitment. GPU quota, regional capacity and data-transfer paths can determine the realized cost more than the headline instance rate.
Google Cloud
Google Cloud is a fit when Premium Tier networking, multi-zone design and integrated data services are central to the workload. Distinguish the multi-zone 99.99% commitment from the single non-memory-optimized 99.9% commitment, then check whether the selected GPU instance and region qualify. Include egress and the time needed to stage data into the chosen zone.
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Azure
Azure can be attractive when identity, compliance and existing enterprise services outweigh a higher GPU quote. The $12.29/hour H100 example in GPUCost’s 2026 table is a snapshot, not a universal Azure rate. Request the exact VM SKU, region, quota and networking charges before comparing it with a single-GPU marketplace listing.
CoreWeave
CoreWeave’s GPU-native products are designed around AI workloads and cluster configurations. Its 2026 HGX H100 example is an eight-GPU node, so divide the node quote for a first comparison but retain node-level topology, storage and commitment conditions in the decision. Spot savings are meaningful only if your checkpoint and restart plan tolerates eviction.
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Lambda
Lambda offers first-come self-serve instances and 1-Click Clusters. Its H100 SXM instance range is $3.99–$4.29 per GPU-hour, while the listed 1-Click H100 cluster examples fall from $6.16 per GPU-hour at 16 GPUs to $5.54 at 256 GPUs on plans lasting two weeks to one year. Use the cluster figures only when the planned scale and commitment match your job.
RunPod
RunPod’s 2026 examples are $2.89/hour for H100 PCIe, $3.99/hour for H100 SXM and $1.79/hour for A100 PCIe/SXM. It separates Community Cloud and Secure Cloud and advertises more than 30 regions. Check the offer type, host class, persistence and interruption behavior rather than combining all Pods into one availability assumption.
Vast.ai and other marketplaces
Marketplace pricing can be the lowest: GPUCost’s 2026 H100 example for Vast.ai is $1.47/hour. The trade-off is variability among third-party hosts. Validate host history, replacement behavior, network route, storage persistence and eviction terms for the exact listing, and use marketplaces primarily where checkpointing and flexible scheduling make interruption acceptable.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.A repeatable comparison workflow
- Define the workload. Write down model size, framework, GPU memory requirement, number of GPUs, topology, expected duration, checkpoint interval, data location and launch deadline.
- Create a single request sheet. Keep SKU, VRAM, region, quantity, interconnect, storage and billing mode identical for each provider.
- Collect dated quotes. Save the displayed rate, minimum billing, commitment term, spot rules, setup charges and egress assumptions with the date and geography.
- Normalize units. Convert node prices to per-GPU-hour, and do not compare PCIe, SXM or different topology as if they were identical.
- Run a provisioning trial. Measure request-to-ready time, failed placements, wait-list behavior and initialization time for the exact offer.
- Calculate usable cost. Add storage, egress, minimum-billing waste, setup-time cost and expected restart cost, then divide by productive GPU-hours.
- Audit the contract. Map the SLA scope, exclusions, credit cap and claim process to the failure modes your workload can experience.
- Test portability. Confirm that images, checkpoints, Kubernetes or API tooling and network configuration can move to a second provider if capacity disappears.
Choose according to failure tolerance
Compliance-heavy or data-intensive production
Prefer a hyperscaler when identity, compliance evidence, integrated databases and multi-region failover are more valuable than the lowest accelerator rate. The premium can be rational when moving data or rebuilding security controls elsewhere would cost more than the GPU difference.
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Use a GPU-native provider when the required SKU, topology and region are confirmed available and the provider’s storage and network design match the job. For multi-node training, verify the fabric and node shape before accepting a per-GPU comparison.
Checkpointed experiments and batch work
Spot and marketplace capacity can work well when jobs are restartable, deadlines are flexible and checkpoints are frequent. Compare the expected restart cost, not just the spot rate. A queue after eviction can erase the apparent saving.
Long-running training
Set a checkpoint interval based on the amount of work you can afford to lose. Keep a tested copy outside the compute instance, document the exact image and dependencies, and maintain a fallback region or provider. This turns a provider outage or eviction into a restart rather than a full re-run.
Operational checks before committing
- Run a short benchmark on the exact GPU variant and topology; do not infer performance from the model name alone.
- Verify that the container image, drivers and CUDA stack are supported by the selected instance.
- Measure dataset and checkpoint throughput, not only GPU utilization.
- Confirm that persistent storage survives stop, restart and host replacement.
- Test cancellation, replacement and refund behavior on the selected billing mode.
- Store credentials and logs outside the GPU VM so an instance loss does not remove your evidence.
- Document the provider’s support channel and escalation path before a production run.
Common comparison mistakes
Picking the lowest number in a search result
A low figure may be a different GPU variant, a spot listing, a community host, a promotional snapshot or a larger minimum-billing unit. Put the offer type and date next to the number before ranking it.
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An eight-GPU node quote includes hardware and topology that a single-GPU offer does not. Divide for an initial unit comparison, then preserve node-level costs and constraints in the final model.
Treating an SLA credit as uptime insurance
Credits are usually limited to part of the affected bill and do not repay lost research time. Design checkpoints, replicas and fallback capacity independently of the credit calculation.
Assuming advertised capacity equals immediate capacity
A large regional footprint or a “thousands of GPUs” claim does not guarantee your exact SKU, quantity and topology at your deadline. Measure placement with a dated request and keep the result tied to the region and product tier.
Decision checklist
- Is the quote for the exact GPU model, VRAM, topology and region?
- Have you converted node pricing to a per-GPU figure without discarding node constraints?
- Does the effective usable-hour calculation include storage, egress, minimums, setup and restarts?
- Did you test request-to-ready time and failed placements for the real quantity?
- Is the SLA scope aligned with the failure you need covered, and do you know its exclusions and credit cap?
- Can you restore a checkpoint on another region or provider?
The best provider is the one that delivers the required GPU, topology and start time at the lowest effective usable cost while meeting your workload’s recovery and compliance requirements. Price, availability and reliability must be measured together; optimizing any one of them in isolation can increase the cost of finished work.
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.
