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A reliable GPU cloud provider is one that commits the GPUs you need, measures failures in a way you can audit, responds when jobs stop, and lets you leave without losing data or months of engineering work. Evaluate service availability and GPU capacity as separate promises, put GPU model, quantity, topology, region, delivery dates, expansion rights, and shortfall remedies in the order form, then prove the arrangement with a pilot and a second-provider restore test.
Headline uptime is only a starting point. The contract’s measurement interval, exclusions, claim deadline, remedy, and provider change rights determine whether an impressive percentage has practical value.
What reliability means for a GPU cloud
GPU workloads fail in more ways than a website does. A control plane can be reachable while every GPU of the model you need is queued. A machine can be running while its interconnect, storage path, or network fabric prevents distributed training from making progress. A provider can meet a monthly uptime percentage yet miss the delivery date for a reserved cluster.
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- Service availability: Whether the control plane, virtual servers, networking, storage, and other contracted components can be used.
- Capacity availability: Whether the promised number and type of GPUs can actually be obtained when your workload needs them.
- Performance consistency: Whether GPU memory, interconnect topology, network throughput, storage throughput, and job-start behavior match the workload you tested.
- Operational response: Whether support detects, communicates, escalates, and repairs incidents within defined time targets.
- Portability: Whether images, checkpoints, data, logs, and infrastructure definitions can be exported and run elsewhere at a known cost.
A contract that covers only the first item can still leave a training program unusable. Ask for distinct definitions and measurements for service and capacity before comparing percentages.
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Read uptime claims as measurement rules, not marketing labels
Availability percentages are meaningful only with their clock, sample interval, exclusions, and remedy. For example, NVIDIA’s 2025 DGX Cloud SLA measures service availability over a calendar month and records service status every 15 minutes. It measures capacity availability over a calendar month and tracks it every 60 minutes. The SLA excludes service gaps shorter than 15 minutes for service availability and shorter than 60 minutes for capacity availability, along with planned maintenance and several customer-caused or force-majeure events. A validated miss must be claimed within two months of the incident, and service credits are stated as the sole remedy.
That structure illustrates questions to ask every provider:
- Is availability calculated monthly, quarterly, or over another period?
- What sampling interval turns a failure into downtime?
- Do partial failures count, such as half of a cluster or one zone?
- Are queue time, failed provisioning, degraded interconnects, and storage throttling included?
- Which maintenance, customer, network, legal, or force-majeure events are excluded?
- Who supplies the measurements and logs if your records disagree?
- How long do you have to submit a claim, and what evidence is required?
- Are credits the only remedy, or can repeated misses trigger termination, a fee reduction, or an SLA renegotiation?
NVIDIA’s 2026 AI-cloud requirements document sets a different kind of benchmark: a financially backed control-plane uptime target of 99.95% or better. It says published availability, response, performance, and telemetry targets are minimum service levels to incorporate into the applicable SLA. Treat such a requirement as a negotiation baseline, not as proof that a particular order has that protection; the signed SLA and order form control.
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Public claims that require contract confirmation
| Provider or model | Publicly stated figure or feature | What a buyer still needs to verify |
|---|---|---|
| NVIDIA DGX Cloud SLA, 2025 | 99% service availability and 95% capacity availability, measured monthly; service tracked every 15 minutes and capacity every 60 minutes. | Exact covered components, exclusions, claim evidence, two-month claim deadline, and the credit-only remedy. |
| NVIDIA AI-cloud requirements, 2026 | 99.95% or better financially backed control-plane uptime target described as a minimum level for the applicable SLA. | Whether the target appears in your order, how it is measured, and the financial remedy for a miss. |
| Paperspace security page | A 99.99% uptime claim covering network, power, virtual server, and storage availability. | The current SLA’s scope, exclusions, measurement method, support obligations, and remedy schedule. |
| AWS Capacity Blocks | Time-bounded, uninterrupted access to supported GPU instances in supported regions. | Eligible instance types and regions, booking rules, cancellation terms, and how capacity shortfalls are handled. |
| Lambda private cloud | Dedicated deployments from 2,000 to 64,000 NVIDIA GPUs with reservations of three to ten years. | Delivery milestones, acceptance tests, substitutions, ramp schedule, and termination rights for a long commitment. |
| CoreWeave platform | Kubernetes-native GPU compute and a zero-egress migration position are described publicly. | Eligible destinations, data types, duration of the offer, exclusions, and the exact commercial language. |
Put the right promises in the contract
Define the service and its boundaries
Use a definitions schedule rather than a broad phrase such as cloud platform. Name the GPU model and memory, host or virtual-machine type, interconnect and topology, region, tenancy model, storage tiers, network fabric, image or orchestration layer, control-plane components, and observability services. State whether a failure of one host, one rack, one zone, or a percentage of the reserved fleet counts as a service failure.
Attach the exact architecture and bill of materials to the order form. If the provider may substitute a GPU, specify an equivalent or better memory size, compute capability, interconnect, and performance acceptance test. Otherwise, a nominally available substitute may be unusable for your model.
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Separate capacity from availability
Capacity language should state committed GPU-hours or systems, the minimum quantity available at any time, delivery and ramp dates, queue behavior, and the remedy when the commitment is missed. A reservation that guarantees only a spending limit is not the same as a reservation that guarantees 256 GPUs of a named model in one topology.
Include expansion rights: how many additional GPUs can be requested, the lead time, the region in which they can be delivered, and whether expansion is firm or best effort. Specify what happens when the named model is temporarily unavailable. A substitution clause should require your approval and preserve the performance characteristics that made the original purchase acceptable.
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Define severity levels with examples. A complete regional outage, inability to start any reserved GPU, and a single failed job should not share one priority. For each severity, record acknowledgement, human response, workaround, restoration, status updates, and escalation targets. Require a status page that is accessible during an outage, named escalation contacts, and a root-cause report for material incidents.
NVIDIA’s 2025 SLA describes a 24-hour commercially reasonable target for critical incidents. That wording demonstrates why the target must be read with its qualifier: a commercially reasonable effort is not the same as a guaranteed restoration deadline. If your training window has a hard completion date, negotiate a restoration or replacement commitment that reflects it.
Understand remedies before accepting them
Calculate what a credit is worth against the cost of idle engineers, missed model launches, and reserved hardware that could not be used. Credits may be the sole remedy, as in the cited NVIDIA SLA. Ask for a service-level reopener, termination right, or fee cap after repeated capacity or availability misses. Define whether credits apply automatically or require a claim, and whether they are based on the affected service, the whole order, or a narrow monthly charge.
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Control changes, deprecations, and suspension
Review every clause allowing the provider to change, deprecate, suspend, or terminate a service. Require advance notice, a migration period, and assistance when a GPU generation, API, image, storage system, or orchestration feature is retired. NVIDIA’s agreement describes subscriptions as fixed-duration services subject to applicable terms and warns that pre-release offerings may have reduced reliability and may be terminated. Do not place production checkpoints or an irreplaceable dataset on a pre-release service without a separate exit path.
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Request a written capacity plan
Ask each finalist to provide, in writing:
- Named GPU SKU, memory, host configuration, and interconnect topology.
- Committed quantity and the minimum quantity available during your term.
- Region, data-center or zone placement, and tenancy model.
- Delivery date, ramp milestones, and acceptance criteria.
- Lead time for adding GPUs during a demand surge.
- Queue behavior when the committed fleet is temporarily full.
- Permitted substitutions and your right to reject an inferior configuration.
- Shortfall credits, replacement capacity, and termination or fee remedies.
Do not treat an inventory dashboard or sales statement as capacity for your account. Tie the commitment to an order, reservation, or SLA with a remedy.
Match the reservation model to the workload
| Workload situation | Reservation approach | Contract questions |
|---|---|---|
| Short, date-bound training or fine-tuning run | Time-bounded blocks such as AWS Capacity Blocks can reserve uninterrupted access to supported GPU instances. | Are your instance type and region supported? What happens if the block cannot start on time? Can you extend it if checkpoints take longer? |
| Continuous production with predictable demand | A recurring committed quantity with explicit minimum availability and expansion terms. | Is the minimum measured per hour, day, or month? Does a queue count as unavailable? What is the remedy for a shortfall? |
| Very large dedicated cluster | Private-cloud deployment; Lambda documents clusters from 2,000 to 64,000 NVIDIA GPUs and three- to ten-year reservations as an example of this model. | What are delivery and acceptance milestones? Who bears schedule risk? What substitutions are allowed, and how can you terminate if the cluster is late or underdelivered? |
Large reservations magnify both capacity benefits and contract risk. Break delivery into accepted milestones, with payment and commitment increasing only when each milestone passes your topology, performance, and reliability tests.
Use a scorecard instead of a single uptime number
Score each finalist on the same evidence. A simple 0-to-5 scale works if every score has a written justification and an attached document; do not award a high score for an unverified promise.
| Axis | Evidence to collect | Decision questions |
|---|---|---|
| Contract strength | Service and capacity definitions, measurement rules, exclusions, claim process, remedies, support severities, and change rights. | Can you calculate your remedy and leave after repeated misses? |
| Capacity reality | Named inventory, reservation mechanism, lead times, geographic redundancy, and expansion plan. | Can the provider deliver the required topology on your date, not merely sell on-demand access? |
| Performance consistency | GPU generation and memory, interconnect, network and storage throughput, job-start latency, and representative benchmarks. | Does the platform sustain your distributed or data-intensive workload under contention? |
| Operations | Status history, maintenance notices, support coverage, escalation paths, and root-cause reports. | Will a human own a production incident, and can you see what is happening? |
| Portability and exit cost | Container and Kubernetes compatibility, checkpoint export, data-transfer time, egress price, deletion confirmation, and termination assistance. | Can another provider run your workload without a redesign or an unaffordable transfer? |
Weight the axes according to the failure that would hurt your business most. A research team with flexible deadlines may emphasize price and burst capacity; a production inference service may give contract remedies and multi-region operations greater weight. Keep the scoring sheet with procurement records so an exception is visible rather than lost in sales notes.
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Run a pilot that measures failure, not just speed
A short benchmark that reports tokens per second is insufficient. Use a pilot with the same container, drivers, model, dataset shape, orchestration, and checkpoint format planned for production.
- Provision the intended topology. Confirm GPU model, memory, interconnect, region, tenancy, storage tier, and network path against the order form.
- Repeat jobs at different demand periods. Record request-to-start latency, queue time, provisioning failures, preemptions, and the percentage of jobs that require a retry.
- Measure the full data path. Test storage throughput, network throughput, checkpoint write and restore time, and distributed-training synchronization rather than only isolated GPU speed.
- Exercise recovery. Restore a checkpoint on a replacement host or after an intentional restart. Record the time, operator steps, and any data loss.
- Open a support case. Use a realistic severity, record acknowledgement and response times, and verify that escalation contacts and status updates work.
- Export the workload. Save the container image, infrastructure definitions, checkpoints, logs, and a representative dataset to an independent location. Document the commands and permissions needed to restore them.
- Compare the results with contract language. Convert observed queue and recovery behavior into acceptance criteria, not informal notes. Reject or renegotiate any gap before committing.
Run the pilot long enough to encounter maintenance and ordinary demand variation. One successful launch proves that a launch is possible; it does not prove sustained capacity.
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Ask for the provider’s maintenance policy, notice periods, status-page history, incident postmortems, and support coverage by region and time zone. Find out whether maintenance drains jobs, evicts instances, or merely blocks new provisioning. Require advance notice for changes that can invalidate drivers, images, APIs, or network routes.
Verify that telemetry is exportable. You should be able to retain job events, GPU health, utilization, storage errors, network errors, and audit logs outside the provider. Telemetry that disappears with the account weakens both incident claims and migration.
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Clarify ownership during a shared-failure event. If a storage service, network fabric, and GPU fleet are supplied by different parties, identify who accepts the incident, coordinates vendors, and issues the root-cause report. A contract that sends you between subcontractors is not an operational plan.
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Design the exit before you sign
Keep portable artifacts
Store infrastructure definitions, container recipes, lock files, model checkpoints, tokenizer files, configuration, observability rules, and recovery documentation in repositories you control. Prefer standard container formats and orchestration definitions that a second provider can run with limited changes. Record driver and runtime compatibility so a future migration is not blocked by an undocumented version.
Maintain a live second-provider path
Create an account with a second provider and periodically restore a checkpoint there. The test should include credentials, networking, storage permissions, dependencies, and a small production-like inference or training run. A dormant account is not an exit plan; a repeatable restore is.
Measure transfer time and cost
Move the largest datasets in a controlled test and record elapsed time, egress charges, temporary storage, transfer tooling, and engineering labor. Include the time needed to validate checksums and rebuild indexes. Ask whether snapshots, logs, and backups are exportable and whether the provider charges for deleting or retrieving them.
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Negotiate termination assistance
Specify who helps export data, how long assistance lasts, the response time for migration questions, and what happens to snapshots, logs, encryption keys, and credentials. Require written deletion confirmation and define the date on which access ends. Negotiate a termination right or fee cap after repeated capacity or SLA misses, especially for multi-year reservations.
CoreWeave’s public platform page advertises zero-egress migration and a Kubernetes-native environment, which may reduce switching friction. Validate the eligible destinations, data types, duration, and contractual scope, then test an actual migration; a public positioning statement is not a substitute for an executable exit clause.
Red flags that should stop a purchase
- The provider quotes one uptime percentage but will not define capacity availability.
- GPU model, topology, region, or delivery date appears only in a sales email, not the order.
- Exclusions remove planned maintenance, short failures, or provider dependencies without a compensating commitment.
- Claims must be filed quickly, require evidence the provider controls, or expire before your incident review is complete.
- Credits are the sole remedy and there is no reopener or termination right after repeated misses.
- Expansion is described as best effort with no lead time, quantity, or substitution rule.
- The provider can deprecate the service without migration notice or assistance.
- Export, egress, snapshot retrieval, or deletion costs are absent from the proposal.
- No pilot can be run on the exact GPU, topology, region, and software stack you intend to buy.
- Support cannot provide an escalation contact, status history, or root-cause report process.
A practical decision sequence
- Write the workload requirement. List GPU model or performance class, memory, quantity, topology, region, storage, network, start date, run duration, and expansion need.
- Issue the same evidence request. Send every candidate the contract, capacity-plan, support, maintenance, and exit questions in one checklist.
- Discard non-answers. Do not score a field as acceptable when the provider says it will be decided later or covered by a standard policy you have not received.
- Pilot finalists. Measure start latency, failures, checkpoint recovery, data-path throughput, and support response using your workload.
- Negotiate the order form. Attach the accepted topology, delivery milestones, minimum quantity, expansion terms, remedies, and change protections.
- Test the exit. Restore a checkpoint with a second provider, measure transfer cost, and retain the procedure with your operational runbooks.
- Review quarterly. Compare actual availability, capacity shortfalls, support response, and migration readiness with the contracted commitments. Trigger the negotiated remedy rather than waiting for renewal.
The most dependable choice is not necessarily the provider with the highest advertised uptime. It is the one whose capacity is committed in writing, whose measurements you can reproduce, whose support obligations are enforceable, and whose exit can be executed before a shortage or outage becomes a crisis.
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