A cloud GPU provider can fail in two different ways: it can run out of money or infrastructure, or it can remain online while the GPUs you need are unavailable. Assess both risks before signing. Request financial and concentration evidence, verify power and hardware resilience, and negotiate an SLA that measures capacity separately from control-plane uptime, defines exclusions and claims precisely, and provides remedies you can actually use.
Start with two separate questions: can the provider survive, and can it deliver?
Financial stability and service reliability are related but not interchangeable. A provider may have a reachable API and functioning billing system while having no suitable GPUs to allocate. It may also advertise abundant capacity yet lack the liquidity to pay for electricity, colocation, replacement hardware or debt service during a downturn.
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Evaluate the provider as both a contractual counterparty and an infrastructure operator. Your diligence should answer four practical questions:
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- Does the company have enough cash, financing and operating resilience to keep serving customers through a utilization or funding shock?
- Is your capacity dependent on one customer, facility, utility, network path or hardware supplier?
- Does the SLA measure the resource your workload actually consumes, rather than only a web console or API?
- Can you recover data, checkpoints and deposits if capacity disappears or the provider fails?
Do not treat an uptime percentage as a complete answer. A useful contract combines financial disclosure, capacity commitments, operational targets, workable claims and an exit plan.
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Financial-stability checks before you commit
Public-company evidence
For a listed provider, ask for the latest audited annual report and most recent quarterly filing. Review the balance sheet and cash-flow statement rather than relying on a credit-rating summary or sales presentation.
- Cash and restricted cash: Separate immediately available cash from funds pledged for debt, leases or other obligations.
- Operating cash flow: Check whether ordinary operations generate cash or require repeated external funding.
- Debt maturities and interest burden: Note near-term maturities, variable-rate exposure, covenant requirements and interest payments relative to operating cash flow.
- Capital expenditure: GPU servers, networking, power systems and data-centre build-outs require continuing investment. Compare planned spending with available liquidity and committed financing.
- Going-concern language: Treat an auditor’s or management’s warning about the company’s ability to continue as a material risk requiring additional protection.
Ask management to explain how those figures change if GPU prices fall, utilization remains below plan, power costs rise or a major customer stops paying. A profitable quarter does not prove that cash is available when a large equipment or power invoice arrives.
Private-company evidence
Private providers do not publish the same level of detail. Request management accounts, a current liquidity forecast and evidence that key obligations are being paid.
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- Committed financing, undrawn credit lines and the conditions that could prevent access to them.
- Parent-company support, guarantees or letters of credit, including their duration and enforceability.
- Insurance coverage for property damage, business interruption, cyber incidents and equipment failure.
- Evidence of current payment to data-centre, colocation, utility and hardware suppliers.
Ask for a named executive or finance contact who can answer liquidity questions under confidentiality terms. If the provider refuses every form of verification, treat the absence of evidence as a counterparty risk rather than as proof of insolvency.
Measure concentration risk, not just total capacity
Customer and contract concentration
A provider that depends on one or two large customers can lose the cash needed to maintain or expand its fleet if one customer defaults. Axe Compute Inc.’s 2026 Form 10-Q states that a substantial portion of its compute revenue is expected to come from a limited number of customers and contracts, and warns that this concentration creates heightened counterparty credit risk, including non-payment, non-performance and bankruptcy risk.
Ask for the percentage of revenue and contracted GPU-hours attributable to the largest customer and the top five customers. Also ask whether a default would trigger delayed maintenance, cancelled expansion or a breach of financing covenants. You may not receive customer names; concentration bands or percentages are still useful.
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Facility, region and network concentration
Determine how much of your reserved capacity sits in one building, metropolitan area, utility service territory, network path or orchestration cluster. A provider can have thousands of GPUs in total and still be unable to replace a failed site quickly.
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- Whether a second site has the same GPU SKU, interconnect and software image.
- Independent network routes and carrier diversity.
- Time required to move a workload, including queueing and data transfer.
- Whether the provider can allocate replacement capacity outside the affected site under your contract.
Axe Compute’s 2026 filing also warns that a prolonged outage or capacity constraint at a concentrated facility can lead to missed service levels, credits, penalties or termination rights. Ask what happens operationally before you negotiate the legal remedy.
Power, cooling and hardware dependencies
GPU availability depends on electricity and thermal management as much as on server inventory. Request the provider’s contracted megawatts, utility and colocation counterparties, and the terms under which power-price increases can be passed through to you.
- Generator and uninterruptible-power redundancy, including fuel-resupply arrangements.
- Cooling redundancy and the temperature or water constraints that could reduce load.
- GPU replacement lead times and the stock of spare boards, servers and networking components.
- Responsibility for import delays, export controls, accelerator allocation and hardware acceleration or depreciation.
- Whether a hardware failure results in repair, an equivalent GPU, a different model or only a service credit.
Require the provider to identify single points of failure. “Multiple data centres” is not enough if all sites share one utility, supplier, network carrier or management plane.
Run a liquidity stress test
Ask management to model at least a six-month shock combining lower utilization, falling GPU prices, a major customer default, higher power costs and delayed financing. The answer should show the number of months of operating expense, debt service, power and critical maintenance covered by current liquidity and committed funding.
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Read the SLA and the service agreement together
The SLA is usually an attachment or policy, while the service agreement controls definitions, liability, suspension, termination and dispute procedures. Check that the contractual hierarchy does not allow a general disclaimer to override a promised metric.
Define the covered service
Write down exactly what is covered:
- API or control-plane reachability.
- A running virtual machine or bare-metal node.
- A specific GPU model, interconnect or cluster.
- Storage, network connectivity and orchestration.
- Delivered GPU-hours or reserved capacity.
An SLA for a reachable API does not necessarily promise that an H100 node, a multi-GPU cluster or a minimum number of GPU-hours will be available when you request it.
Separate service availability from capacity availability
Use two metrics when capacity matters. NVIDIA DGX Cloud publishes 99% service availability and 95% capacity availability for 2025. The distinction matters: a functioning control plane can coexist with an empty queue or no GPUs in your region.
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|---|---|---|
| Service availability | Whether the defined platform or endpoint is operational | Does a reachable API count if no qualifying GPU can be started? |
| Capacity availability | Whether the contracted GPU type, quantity and location can be delivered | Is availability measured for my tenant and SKU, or across the whole service? |
| Performance | Whether the running resource meets agreed throughput or latency | Are GPU performance, network throughput and job-start latency covered? |
Inspect measurement windows and granularity
NVIDIA’s published DGX Cloud methodology calculates service availability monthly and tracks status every 15 minutes. Its capacity availability is measured monthly and tracked every 60 minutes. Those intervals can hide short interruptions or repeated queue failures.
Confirm whether the provider measures per tenant, node, cluster, region or global fleet; whether a partial outage counts as a full outage; and whether the clock starts when you submit a job, when a node is assigned or when the provider declares an incident. Ask how rounding, aggregation and scheduled maintenance affect the result.
Negotiate exclusions instead of accepting a blanket force-majeure list
List every exclusion and require a clear definition. Common categories include planned maintenance, emergency maintenance, customer software or network faults, third-party services, attacks, quota limits, unpaid invoices, pre-release features and force majeure.
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For planned work, require advance notice, a maximum maintenance budget and maintenance windows that respect your region and time zone. For emergency work, require prompt notice and an incident record. “Provider-declared incident” should not allow the provider to exclude an outage merely by labeling it operationally inconvenient.
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Clarify whether power shortages, utility curtailment, hardware allocation delays and upstream data-centre failures are excluded. If they are, negotiate a replacement-capacity obligation or termination right rather than accepting an unlimited exclusion.
Make the claims process usable
An SLA remedy is valuable only if you can prove the event and claim it before the deadline. NVIDIA’s DGX Cloud process requires a detailed incident description, start time and duration, affected offering and users, and troubleshooting attempts. Use that level of detail as a checklist when negotiating.
- Specify the claim deadline in calendar or business days.
- State which logs, timestamps and job records are required.
- Require the provider to acknowledge the claim and respond within defined periods.
- Give you access to relevant telemetry or an export of the provider’s measurements.
- Define how disputes over the provider’s telemetry are escalated and resolved.
Do not let the provider’s internal monitoring be the only admissible evidence. Preserve your own scheduler logs, API responses, queue times and node-health records.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Value credits, caps and termination rights economically
Identify the remedy in practical terms: cash refund, future-use credit, service extension, replacement node, fee waiver or termination. NVIDIA’s DGX Cloud remedy is a credit toward a future order after a validated claim. That may have little value if capacity remains scarce or the credit expires before you can use it.
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Check expiration dates, transferability, minimum order requirements and whether credits apply to reserved capacity, on-demand usage or only the affected service. Google Compute Engine’s published SLA in its 2020 version capped aggregate monthly credits at the amount due for the affected service. A cap can prevent recovery even when downtime disrupts a much larger business loss.
For critical workloads, add repeated-miss termination, migration assistance and a right to recover prepaid unused amounts. A credit should supplement, not replace, a workable exit mechanism.
Include support, restoration and portability commitments
Support and restoration
Define severity levels, support channels, escalation contacts, response times and restoration targets. If your workload depends on a particular accelerator, specify hardware replacement time and what counts as an equivalent substitute. Include network throughput, storage performance and job-start latency when they affect production output.
National Compute’s current Base Load Capacity SLA publishes a 99% per-GPU-node base-load uptime commitment. Compare the scope of that commitment with your own workload: a per-node target may not cover cluster-wide scheduling, interconnect failures or the ability to obtain replacement nodes.
Exit and portability
Before committing data or deposits, document:
- Data-egress pricing and any free transfer allowance.
- Time and method to export checkpoints, images, logs, configuration and raw data.
- Whether exported images run on another provider without proprietary dependencies.
- Notice periods for cancellation and termination for repeated SLA failure.
- Ownership and refund treatment for reserved capacity, deposits and prepaid credits.
- Migration assistance, including staff support and temporary overlap capacity.
Test a small export before production. A theoretical right to retrieve data is not a recovery plan if transfer takes weeks or the image cannot boot elsewhere.
Questions to send the provider
- What were the last 12 months of monthly service availability and delivered GPU-capacity availability for the exact region and SKU I will purchase?
- 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-centre 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?
- Can repeated misses trigger termination and migration assistance?
- Can I export checkpoints, images, logs and data within a defined time and cost limit?
Request written answers, not only a sales call. Compare the answers with the executed order form and SLA so that operational promises do not disappear during contracting.
A practical provider-comparison scorecard
| Area | Evidence to compare |
|---|---|
| Liquidity and capital | Cash, operating cash flow, debt maturities, committed financing and parent support |
| Customer concentration | Revenue and GPU-hour share of largest customers; default impact |
| Infrastructure concentration | Capacity by facility, region, utility, network and hardware supplier |
| Capacity model | Committed versus on-demand GPUs, queue behavior and replacement options |
| SLA definitions | Service, capacity and performance metrics with tenant and SKU scope |
| Measurement | Window, tracking interval, aggregation, rounding and provider telemetry |
| Exclusions | Maintenance, quotas, third parties, force majeure, power and hardware events |
| Remedies | Credit value, cash rights, caps, expiry and repeated-failure termination |
| Operations | Support severity, restoration, hardware replacement and escalation |
| Portability | Egress, exports, migration assistance, deposits and termination notice |
NVIDIA’s Requirements for AI Clouds v2.4 says network cloud providers should be able to demonstrate the ability to meet their SLAs to be considered for NVIDIA consumption. Treat demonstrable historical performance as more meaningful than a headline percentage without measurement records.
What happens if the provider runs out of power or enters insolvency?
Power or facility failure
If power, cooling or a concentrated facility fails, the immediate question is whether equivalent capacity exists elsewhere. Your contract should specify substitute regions or nodes, restoration targets, data-preservation duties and the point at which you may terminate. Credits alone do not restart a training job or recreate a lost checkpoint.
Financial distress or bankruptcy
Insolvency can interrupt access even when hardware is physically intact. Prepaid balances may become unsecured claims, equipment may be repossessed and a data-centre landlord may restrict access. Reduce exposure with shorter billing cycles, escrow or guarantees, frequent exports, duplicate capacity and a contractual right to retrieve data promptly.
For a high-value production workload, maintain a tested secondary provider or owned capacity for restart. The goal is not to eliminate every failure; it is to ensure that one provider’s financial or infrastructure problem does not become an unrecoverable business interruption.
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