The Tool Desk
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For a fair decision, calculate an effective usable GPU-hour cost, measure whether the exact capacity can be obtained by your deadline, and read the service-level agreement (SLA) for the resource you will actually run. Prices and capacity are dated snapshots, so record the region, date and offer type with every quote.
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What a fair GPU-cloud comparison includes
A GPU listing is not a standardized product. An H100 PCIe card, an H100 SXM card and an eight-GPU HGX H100 node can have different memory, interconnects, performance, scheduling behavior and prices. A valid comparison starts by fixing the workload specification.
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- GPU SKU and memory: specify the exact model and VRAM, such as H100 PCIe, H100 SXM or A100 PCIe/SXM.
- Node topology: state whether you need one card, a multi-GPU host, NVLink or another high-speed interconnect, and the number of GPUs per node.
- Region and network path: identify where the data resides and where users or training workers connect.
- Billing mode: distinguish community or marketplace supply, secure or dedicated capacity, spot, on-demand and reserved commitments.
- Duration: compare the price for the expected run length, not only the first hourly quote.
- Operational requirements: include persistent storage, checkpoint recovery, image or Kubernetes compatibility, support and multi-region failover.
Normalize a node quote to a per-GPU-hour figure before placing it beside a single-GPU offer. An eight-GPU node listed at $49.24 per hour is not a $49.24 GPU-hour; its simple equivalent is $49.24 ÷ 8 = $6.155 per GPU-hour, before storage and other charges.
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Current price snapshots
The figures below are dated 2026 examples. They are not universal rate cards: region, GPU variant, supply, billing mode and contract term can change the price.
| Provider or source | Offer | Published price | How to interpret it |
|---|---|---|---|
| RunPod | H100 PCIe | $2.89 per hour | Offer type and region must be confirmed at checkout. |
| RunPod | H100 SXM | $3.99 per hour | Different hardware from H100 PCIe; do not treat the two as interchangeable. |
| RunPod | A100 PCIe/SXM | $1.79 per hour | PCIe and SXM variants are grouped in the cited snapshot. |
| Lambda | H100 SXM instances | $3.99–$4.29 per GPU-hour | Range covers the listed instance configurations. |
| Lambda | 16-GPU 1-Click H100 cluster | $6.16 per GPU-hour | Listed for plans from two weeks to one year. |
| Lambda | 64-GPU 1-Click H100 cluster | $5.85 per GPU-hour | Listed for plans from two weeks to one year. |
| Lambda | 256-GPU 1-Click H100 cluster | $5.54 per GPU-hour | Listed for plans from two weeks to one year. |
| CoreWeave | Eight-GPU HGX H100 node, on demand | $49.24 per hour | Equivalent to $6.155 per GPU-hour if all eight GPUs are used continuously. |
| CoreWeave | Eight-GPU HGX H100 node, spot | $19.71 per hour | Equivalent to about $2.46 per GPU-hour, subject to spot interruption. |
| GPUCost comparison | H100 example, Vast.ai | $1.47 per hour | Comparison-table snapshot, not a guaranteed quote. |
| GPUCost comparison | H100 example, RunPod | $2.89 per hour | Comparison-table snapshot, not a guaranteed quote. |
| GPUCost comparison | H100 example, CoreWeave | $4.25 per hour | Comparison-table snapshot; offer configuration may differ from another CoreWeave listing. |
| GPUCost comparison | H100 example, Google Cloud | $9.80 per hour | Comparison-table snapshot, with cloud configuration and region affecting the final price. |
| GPUCost comparison | H100 example, Azure | $12.29 per hour | Comparison-table snapshot, with cloud configuration and region affecting the final price. |
The CoreWeave node arithmetic illustrates why normalization matters. If you need all eight GPUs and run them continuously, $49.24 per node-hour is $6.155 per GPU-hour. If your job can use only one GPU, the unused seven cards are part of your effective cost unless the provider offers a smaller allocation.
Calculate effective usable GPU-hour cost
The invoice rate is only the starting point. Use this model for each candidate:
Effective usable cost = (GPU charges + storage + egress + minimum-billing charges + setup and idle time + expected interruption/restart cost + commitment cost) ÷ productive GPU-hours.
GPU and node charges
Convert every quote to the unit your workload consumes. A single-card job should be compared with a single-card price. A distributed job should include the complete node or cluster price, the number of GPUs actually used and any required interconnect. Do not divide a node quote by its GPU count if your software cannot keep all GPUs busy.
Storage and data movement
Include persistent volume charges, object-storage reads and writes, snapshots and data egress. A low compute rate can be outweighed by repeatedly moving a large training set into a distant region or downloading checkpoints after each run. Put the data and workers in the same region when practical, and obtain the provider’s current storage and egress schedule for the exact service.
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Minimum billing and setup time
Check whether billing rounds up by the second, minute or hour, whether stopped instances continue to incur charges, and whether a minimum runtime applies. Count image pulls, node initialization, data staging and queue time when those periods are billed but produce no training work.
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For spot or marketplace capacity, estimate how often a job could be evicted and how long a restart takes. The restart cost includes lost productive GPU-hours, reloading data, rebuilding the environment and any queue to obtain replacement capacity. Checkpoint frequently enough that a single interruption does not erase a large portion of the run.
Commitment and utilization
Reserved or cluster pricing can reduce the GPU rate while creating a utilization risk. Compare the committed hours with the hours you can realistically consume. A discount on 256 GPUs is not a saving if the project uses 64 GPUs for only a few days.
Which provider class fits your needs?
Hyperscalers
AWS, Google Cloud and Azure are strongest when GPU work must use existing identity and access management, private networking, compliance controls, data services or multi-region redundancy. They can cost more and may have tighter capacity constraints, but the surrounding platform can reduce engineering and audit work.
GPU-native clouds
CoreWeave and Lambda focus on AI infrastructure, GPU-oriented clusters and high-performance training environments. They can be a good fit for dedicated training or inference when the required SKU, topology and region are available for the needed term. Cluster pricing should be compared with the complete cluster configuration, not just a card-level headline.
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Marketplaces and aggregators
Vast.ai and similar marketplaces pool third-party supply and may show very low rates. The buyer must verify host quality, interruption policy, network path, storage persistence, security posture and support. A cheap listing that disappears during a run, has poor disk throughput or requires a long replacement search may have a higher realized cost.
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How to measure availability instead of guessing
No controlled, longitudinal dataset establishes a universal ranking of realized provisioning availability for every GPU, region and provider. Treat availability as a dated measurement, not a permanent provider trait.
Write an exact capacity request
Ask for the GPU SKU, VRAM, region, quantity, interconnect or topology, storage type and deadline. Also identify whether the request is for community, secure, spot, on-demand or reserved capacity. “An H100” is not precise enough when PCIe and SXM offers have different characteristics.
Record the provisioning test
- Submit the same specification to each provider at the same decision point.
- Record the request timestamp, quoted price, offer type and region.
- Measure request-to-ready time, including image and storage initialization if those are part of the usable environment.
- Count failed placements, substitutions and wait-list events.
- Record whether the capacity remained available for the full planned duration.
- Repeat the measurement on dates that represent your real operating pattern; one successful launch does not prove dependable supply.
Interpret provider-specific supply models
RunPod advertises “Thousands of GPUs across 30+ regions” and separates Community Cloud from Secure Cloud. Those labels imply different supply and isolation choices, so compare the exact tier you would use. Lambda separates first-come self-serve instances from 1-Click Clusters ranging from 16 to more than 2,000 GPUs. A self-serve card and a large reserved cluster therefore require separate availability checks.
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What an SLA really covers
An SLA is a contract for a defined service, not a guarantee that every GPU job will run without interruption. Read the scope, measurement interval, exclusions, remedy and claim procedure.
| Provider or analysis | Published availability commitment | Scope or qualification | Credit information |
|---|---|---|---|
| AWS EC2 | 99.99% Region-Level uptime | Instances must run concurrently across at least two Availability Zones. | Credits range from 10% to 100%, depending on measured uptime. |
| AWS EC2 | 99.5% uptime | Individual-instance commitment. | Credits range from 10% to 100%, depending on measured uptime. |
| Google Cloud Compute Engine | At least 99.99% monthly uptime | Premium Tier multi-zone instances. | Credits rise to 100% when uptime falls below 95%. |
| Google Cloud Compute Engine | At least 99.9% monthly uptime | Single non-memory-optimized instance. | Credit schedule depends on the applicable SLA terms. |
| ClusterBid analysis, 2026 | 99.95% | Reported for CoreWeave reserved contracts; verify the contract offered to your account. | Secondary analysis; credit terms require contract review. |
| ClusterBid analysis, 2026 | 99.9% | Reported for Lambda on-demand; verify the contract offered to your account. | Secondary analysis; credit terms require contract review. |
| ClusterBid analysis, 2026 | No explicit spot SLA reported | Reported for RunPod spot. | Do not infer a credit entitlement without written terms. |
Check the SLA’s resource boundary
Determine whether the commitment applies to one virtual machine, a multi-zone fleet, a reserved cluster or only network connectivity. A network-availability promise may not cover GPU health, storage performance, interconnect failures or a scheduler that cannot place your requested node.
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Read exclusions and remedies
Look for exclusions covering planned maintenance, customer actions, storage, interconnect, force majeure and failures outside the provider’s control. Then calculate the maximum credit as a fraction of the affected bill. A credit is not automatically reimbursement for lost training time, delayed launch dates or staff effort.
Plan for claims
Identify the uptime measurement method, required logs, claim deadline and submission path before an incident. Keep timestamps, instance identifiers, monitoring records and restart history. If the contract requires a customer claim, an unfiled incident may produce no credit.
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Interruption behavior
Ask how much notice spot eviction provides, whether checkpoint hooks are available, whether replacement capacity is automatic and whether a failed host preserves local or persistent storage. For marketplace supply, ask who controls the host and what happens when that host leaves the pool.
Storage persistence
Separate ephemeral local disks from persistent volumes and object storage. Confirm that checkpoints survive a VM deletion, that a replacement node can mount them, and that snapshots can be restored in another region if required.
Network and distributed training
Distributed training depends on latency, bandwidth and the interconnect between GPUs. A cluster with the right card count but the wrong topology can underperform a smaller, tightly connected system. Request the network fabric, placement guarantees and any bandwidth limits in writing.
Support and operational tooling
Compare ticket response targets, escalation paths, metrics, logs, image registries, APIs, Kubernetes compatibility and automated provisioning. Test whether you can export images, checkpoints and infrastructure definitions so a later migration is practical.
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Portability and exit planning
Before committing, build a minimal migration path. Store container definitions and environment files in version control, keep checkpoints in a provider-independent format, document driver and CUDA requirements, and test restoring one checkpoint on a second provider. Check API and Kubernetes compatibility, quota procedures and the time required to recreate networking and storage.
Portability has a direct financial value. If a provider runs out of capacity, a portable workload can move; a tightly coupled image, proprietary scheduler configuration or region-bound dataset can turn a short outage into a long delay.
A practical selection process
- Define the workload: GPU model, VRAM, count, topology, region, storage, network and deadline.
- Collect like-for-like quotes: request the same configuration in community, secure, spot, on-demand and reserved forms where available.
- Normalize units: convert node and cluster prices to the GPU-hour or complete-cluster-hour your job actually consumes.
- Build the effective-cost estimate: add storage, egress, minimum billing, setup, idle time, expected evictions and commitment exposure.
- Run an availability check: record request-to-ready time, failures, substitutions and wait-list behavior.
- Review the contract: capture SLA scope, exclusions, uptime definition, credit cap and claim deadline.
- Run a portability test: launch a small job, write a checkpoint, destroy the instance and restore it elsewhere.
- Choose a fallback: keep a second provider or region for deadline-sensitive work, and document the failover steps.
Decision patterns by workload
Compliance-heavy or data-integrated workloads
Prefer a hyperscaler when identity, private networking, compliance evidence, managed data services and multi-region failover outweigh a higher GPU rate. The relevant comparison is total operating cost and risk, not the card price alone.
Dedicated training and production inference
Consider a GPU-native provider when it can supply the exact SKU, topology and region for the required term. Confirm support, persistent storage, network performance and contract remedies before reserving a large cluster.
Checkpointed experiments and batch jobs
Marketplace or spot capacity can be economical when interruption is acceptable and jobs checkpoint frequently. Set an eviction policy, keep replacement capacity options ready and include queueing and restart time in the effective-cost calculation.
Long training runs
Checkpoint at an interval that limits recoverable loss, monitor eviction and capacity events, and price the expected restart time. A spot rate can lose its advantage after repeated evictions and replacement queues.
Quick Recap
Questions to put in every provider quote
- Which exact GPU SKU, VRAM, host type and interconnect will be delivered?
- Which region and availability zone or placement group will be used?
- Is the offer community, secure, spot, on-demand or reserved?
- What is the minimum billable period and rounding rule?
- What storage, snapshot, ingress and egress charges apply?
- How quickly can the stated quantity be provisioned, and what happens if it is unavailable?
- What notice and recovery options exist for interruption or eviction?
- Does the SLA cover GPU availability, VM uptime, network connectivity, storage and interconnect?
- What exclusions, maximum credits and claim deadlines apply?
- Can images, checkpoints, volumes and infrastructure definitions be exported to another provider?
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