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Evaluate a cloud GPU provider twice: first as a financial counterparty that must remain solvent, and second as an infrastructure operator that must deliver usable GPU capacity. Ask for financial evidence rather than marketing claims, measure dependence on customers, facilities, power and hardware suppliers, and separate service uptime from the availability of the GPU capacity you actually need. An SLA is useful only when its measurement window, exclusions, claim process, telemetry, remedy, caps and termination rights are explicit. A high uptime percentage alone does not guarantee that a GPU will be available when your job is ready to run.
1. Start with counterparty and operator risk
A GPU cloud can fail in two different ways. The company may lack the cash or financing to pay for power, rent, hardware and staff. Or the company may remain solvent while a site, utility, network path or GPU supplier cannot deliver capacity. Your diligence must test both conditions.
Read the master service agreement, order form and SLA together. The commercial agreement determines what you bought; the SLA defines how failure is measured and what you receive in return. Ask for answers that apply to your exact region, GPU model, tenancy type and contract term, not an average across the provider’s global fleet.
2. Obtain financial evidence, not assurances
Public-company disclosures
For a listed provider or parent, obtain the latest audited annual report and most recent quarterly filing. Review:
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- Cash and restricted cash, including any limits on using that cash for operations.
- Operating cash flow and whether reported earnings are converting into cash.
- Debt maturities, revolving-credit availability, covenants and interest expense.
- Capital expenditure commitments for GPUs, data-center capacity, networking and cooling.
- Any going-concern language, material litigation, covenant breach or financing dependency.
- Customer receivables and the age of unpaid invoices.
Ask management to reconcile the filing with the capacity it has promised you. A large announced GPU fleet is not evidence of liquidity if the company must still fund deposits, installation, power upgrades and replacement inventory.
Private-company evidence
A private provider will not have public filings, so request current management accounts, a cash-flow forecast and a liquidity runway measured in months. Ask for committed financing, undrawn facilities, parent support or guarantees, insurance coverage and evidence that data-center, colocation and power suppliers are being paid on time. The provider should explain which funding is committed contractually and which depends on a future equity or debt raise.
Request a description of payment terms for major suppliers. A provider that must pay power or colocation bills before collecting customer invoices can face a cash squeeze even when demand is strong. Clarify whether your deposit is segregated, refundable, transferable or available for the provider’s general working capital.
Stress-test liquidity
Ask management to model at least six months of adverse conditions: GPU prices fall, utilization remains below plan, a major customer defaults, power costs rise and expected financing is delayed. The answer should quantify months of operating expense and debt service covered by current cash and committed facilities, identify spending that can be deferred, and show which maintenance or expansion commitments would be cut first.
3. Measure customer and contract concentration
A provider can look well funded while depending on one or two customers for the cash that pays for its fleet. Axe Compute Inc.’s 2026 Form 10-Q states: “A substantial portion of our compute revenue is expected to be derived from a limited number of customers and contracts.” It adds: “This concentration exposes us to heightened counterparty credit risk and to the risk of non-payment or non-performance.”
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Ask for the percentage of revenue, contracted GPU-hours and committed capacity represented by the largest customer and the five largest customers. Request the same analysis for deposits, reserved capacity and long-term take-or-pay contracts. Then ask what happens to maintenance, staffing and expansion if the largest customer becomes insolvent or stops paying. A customer default can reduce the provider’s ability to service every tenant, not just the defaulting account.
Do not demand confidential customer names. A concentration band, contract duration, payment history and credit-support description are usually enough to identify risk without breaching confidentiality.
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4. Find infrastructure concentration before it becomes an outage
Facilities and regions
Ask what percentage of your committed GPU capacity sits in one building, campus, metropolitan area or cloud region. Determine whether another site has the same GPU model, networking fabric, software image and support capability. A second region that cannot run your checkpoint or lacks the required interconnect is not a practical substitute.
Power and cooling
Obtain the contracted megawatts supporting your capacity and identify the utility and colocation counterparties. Ask whether power-price increases are passed through to you, whether the contract contains curtailment rights, and how much generator, UPS and cooling redundancy exists. Clarify fuel autonomy, maintenance schedules and the maximum load that can be sustained during a utility failure.
Network and hardware suppliers
Map the network paths between your users, control plane, storage and GPU nodes. Ask how much capacity depends on one carrier, switch vendor, accelerator supplier or original-equipment manufacturer. Confirm GPU replacement lead times, the spare-parts policy and who bears acceleration, customs or import delays. A provider that can replace a failed card in days is materially different from one that waits for a new overseas shipment.
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5. Define exactly what the SLA covers
“Uptime” can refer to a control-plane API, a running virtual machine, an individual GPU node, an entire cluster, storage, networking, orchestration or delivered GPU-hours. Put the covered object in the order form. If your workload needs eight connected GPUs, an SLA for an API endpoint does not promise that eight-card topology.
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1Fix the driver behind crashes, sound loss and screen glitches2Clear out junk files and repair common Windows errors3Scan for outdated or missing drivers - takes under a minuteRequire separate definitions for service availability and capacity availability. NVIDIA DGX Cloud publishes 99% service availability and 95% capacity availability — NVIDIA, 2025. The figures demonstrate why the distinction matters: a reachable control plane can coexist with insufficient GPUs to start your job. Capacity should be measured for the SKU, region and reservation you purchase, not for the provider’s entire platform.
Performance commitments may also be necessary. If job-start latency, inter-node network throughput, storage bandwidth or GPU performance affects your economics, specify a metric, test method and remedy. Otherwise those characteristics may remain best-effort even while the service is technically “available.”
6. Check measurement windows and granularity
Read how the provider samples and aggregates incidents. NVIDIA measures service availability monthly and tracks status every 15 minutes; its capacity availability is measured monthly and tracked every 60 minutes. Short gaps may be excluded. Ask whether an outage is counted per tenant, node, cluster, region or global service, and whether one failed node is diluted by thousands of healthy nodes in an aggregate calculation.
Confirm the start and end points. Does downtime begin when a health check fails, when your monitoring detects a failed job, or only after you open a ticket? Does recovery require the API to respond, a VM to boot, or a replacement GPU to pass a workload test? Require access to timestamped provider telemetry or an agreed independent monitoring method.
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For comparison, Google Compute Engine’s published SLA in its 2020 version states a 99.99% monthly uptime objective. That percentage has meaning only within Google’s defined service, measurement method and exclusions; it should not be treated as a universal guarantee for every GPU configuration.
7. Negotiate exclusions instead of accepting a blank list
Review each exclusion and its notice requirement. Typical categories include:
- Planned and emergency maintenance.
- Customer software, image, credential or network errors.
- Third-party storage, identity, carrier or marketplace services.
- Distributed attacks, abuse, quota exhaustion and account suspension.
- Unpaid invoices, exceeded limits and unapproved configuration changes.
- Pre-release or beta features.
- Force majeure, utility failure, natural disaster and government action.
- Incidents the provider declares outside the covered service.
Ask for advance notice and a maximum maintenance budget where possible. Require the provider to identify which exclusions are automatic and which require evidence. An outage should not become “customer-caused” merely because a support engineer cannot reproduce it after the system has recovered.
8. Make the claim process usable
Find the deadline, required evidence and response commitments before signing. NVIDIA requires a detailed incident description, start time and duration, affected offering and users, and troubleshooting attempts for a DGX Cloud claim. Confirm whether you must provide logs, job IDs, monitoring graphs or support-ticket numbers, and how quickly the provider must acknowledge and decide the claim.
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9. Value the remedy in real operating conditions
List every possible remedy: cash refund, future-use credit, service extension, replacement node, expedited restoration or termination. NVIDIA’s DGX Cloud remedy is a credit toward a future order after a validated claim. A credit that expires, applies only to the same unavailable SKU or cannot be used during a capacity shortage may have little practical value.
Check caps and aggregation. Google Compute Engine’s published SLA caps aggregate monthly credits at the amount due for the affected service. Compare that ceiling with the cost of missed deadlines, idle staff, duplicate infrastructure and data reprocessing. Ask whether credits apply to reserved fees, usage charges, support fees and pass-through power charges, or only to a narrow line item.
Negotiate termination or migration assistance for repeated misses. A credit should not be the sole remedy when the provider cannot deliver the capacity that your contract reserves. Define how deposits, prepaid balances and reserved capacity are handled after termination.
10. Include support, restoration and portability commitments
Support and restoration
Specify severity definitions, escalation contacts, acknowledgement times and restoration targets. If hardware failure affects a node, set a replacement-time objective and identify whether the provider may substitute an equivalent GPU. Include support coverage for the time zones in which your team operates and a process for declaring a major incident.
Exit and portability
Confirm data-egress pricing, image and checkpoint formats, log export, deletion certificates and the time allowed to retrieve data. Ask the provider to test an export before production. Define notice periods, migration assistance and the treatment of reserved capacity or deposits. A provider can meet an uptime target yet leave you trapped if your checkpoints cannot be moved within a reasonable time and cost.
11. Use a repeatable comparison framework
| Area | Evidence to request | Risk signal | Contract response |
|---|---|---|---|
| Liquidity and capital | Cash, operating cash flow, debt maturities, facilities and runway | Funding depends on an uncommitted future raise | Parent guarantee, escrow, termination and refund rights |
| Customer concentration | Revenue and GPU-hours by largest customers | One default would cut maintenance or expansion | Capacity ring-fencing and disclosure of material changes |
| Facility and power | Sites, megawatts, utilities, redundancy and curtailment terms | Most capacity in one site or utility | Regional failover, notice and capacity-replacement obligation |
| Hardware supply | GPU suppliers, spares and replacement lead times | No inventory or uncertain import timing | Equivalent substitution and restoration target |
| Capacity commitment | Reserved versus on-demand GPU-hours for your SKU | Only platform-level availability is promised | Per-tenant, per-SKU capacity metric |
| Measurement | Sampling interval, aggregation and incident clock | Provider-only telemetry or broad averaging | Telemetry access and agreed measurement method |
| Exclusions | Maintenance, quota, third-party and force-majeure language | Unlimited or provider-declared exclusions | Notice, maintenance budget and evidence standard |
| Remedy | Credit formula, cap, expiry and eligible charges | Credit unusable during shortage | Cash option, extension, replacement and termination triggers |
| Portability | Export time, cost, formats and migration help | Opaque checkpoints or punitive egress | Defined export window and assistance |
12. A practical diligence sequence
- Send a written evidence request. Ask for the financial, concentration, power, hardware and SLA information listed above, tied to the exact region and GPU SKU.
- Validate historical delivery. Request the last 12 months of monthly service availability and delivered GPU-capacity availability, plus outage, maintenance and quota logs for your target region.
- Run a failure tabletop. Walk through a utility outage, a major-customer default, a GPU shipment delay and a six-month utilization shock. Record who pays, who communicates and how quickly capacity is restored.
- Price the remedy. Convert credits, caps and expiry rules into a monetary value under your workload’s deadline and utilization assumptions.
- Test portability. Export a checkpoint, image, logs and representative data; measure time, cost and whether another provider can restart the job.
- Make approval conditional. Require unresolved financial, capacity or SLA risks to be accepted by a named executive, with a review date and a backup-provider plan.
Questions to send before signing
- What were the last 12 months of monthly service availability and delivered GPU-capacity availability for the exact region and SKU I will buy?
- Which outages, maintenance events, quota events and customer-caused incidents are excluded, and who decides?
- What percentage of revenue, contracted GPU-hours, power and data-center capacity depends on the top customer, site, utility or supplier?
- What cash, committed financing, parent guarantee and insurance support the service during a six-month utilization or funding shock?
- How are credits calculated, capped, claimed, paid and expired, and can repeated misses trigger termination and migration assistance?
- Can I export checkpoints, images, logs and data within a defined time and cost limit?
Bottom line: buy evidence-backed capacity, not a headline percentage
NVIDIA’s requirements for AI clouds state that “NCPs should be able to demonstrate the ability to meet the service-level agreements (SLAs) to be considered for NVIDIA consumption.” Apply that standard to every provider. Choose the counterparty that can document liquidity and supplier payment, spread operational dependencies, reserve the capacity your workload needs, measure failures transparently and provide a remedy that remains useful when capacity is scarce. If the provider cannot answer those questions, treat its uptime claim as an availability of an interface—not proof that your GPUs, jobs and data will be available when the business requires them.
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