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What “reliable” means for a neocloud
Microsoft for Startups defines a neocloud as “a cloud provider built specifically for GPU compute and AI workloads rather than general-purpose enterprise applications.” In practice, reliability has several separate dimensions:
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- Capacity reliability: whether the GPU model, quantity, topology, region, and delivery date you ordered actually become available.
- Service reliability: whether the control plane, compute nodes, storage, networking, and management interfaces remain usable.
- Operational reliability: how quickly the provider detects failures, replaces hardware, communicates incidents, and restores service.
- Data reliability: whether files remain durable, backups can be restored, and data can be exported without a provider-specific dead end.
- Contractual reliability: whether the promises are written into an SLA or order form, measured with a defined method, and backed by credits or another remedy.
A neocloud may be dependable for a training cluster and unsuitable for a globally distributed transactional application. Evaluate the exact workload and failure consequences rather than asking whether the category is reliable in the abstract.
Capacity: will the GPUs you reserve actually be there?
GPU scarcity makes capacity a first-order reliability issue. A reservation is meaningful only when the provider identifies what is reserved, where it will run, when it will be delivered, and what happens if the commitment slips.
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Put the capacity promise in writing
Ask the provider to specify each item below in the quote, service description, or order form:
- Exact GPU model, memory size, interconnect, and quantity.
- Topology, including whether GPUs are in one server, one rack, one cluster, or separate hosts.
- Region, availability zone, facility, and any permitted substitution.
- Reservation term, start date, minimum commitment, and delivery milestone.
- Whether capacity is dedicated, shared, interruptible, or subject to preemption.
- Replacement hardware and substitution rules if a GPU or host fails.
- Cancellation, delay, service-credit, or termination rights when delivery misses the agreed date.
“Equivalent GPU” should not be accepted without a definition. A replacement with a different memory size, interconnect, driver support, or regional location can change model performance and application behavior.
Check the delivery sequence
NVIDIA’s AI-cloud requirements describe a staged readiness model: API readiness and transport establishment before GPU delivery, developer capacity before cluster delivery, and a data-mover environment before GPU capacity so data can be staged. Ask the provider to map your onboarding plan to those dependencies. A cluster that is physically installed but cannot be reached through its API, lacks developer access, or has no practical data-ingest path is not production-ready.
Distinguish capacity types
Dedicated bare metal can provide predictable performance but may require you to manage more of the operating stack. Virtual machines, Kubernetes, and Slurm can improve scheduling or portability, but add control-plane and integration dependencies. Shared capacity may be cheaper or easier to obtain, while dedicated capacity generally gives clearer isolation and performance expectations. Have the provider state which layer it guarantees: the physical GPU, a VM, a Kubernetes workload, a Slurm allocation, or only a best-effort queue.
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How to read neocloud uptime numbers
An uptime percentage has meaning only when you know the service covered, the failure domain, the measurement window, exclusions, and remedy. The following published figures illustrate why percentages cannot be compared as if they measured the same thing.
| Published commitment | What it measures | Important qualification |
|---|---|---|
| NVIDIA AI-cloud requirements: 99.95% or higher | Production control-plane uptime | Financially backed target; it concerns the control plane, not automatically every GPU job, storage system, or network path. |
| National Compute Base Load Capacity SLA: 99% | Uptime per GPU node | Replacement-provisioning time counts as downtime; credits apply below the target. |
| Nebul: 99.9% | Combined monthly uptime for redundant multi-zone IaaS | Measured across a redundant multi-zone IaaS design and therefore not equivalent to a single-node figure. |
| Nebul: 99.5% | Single-zone PaaS uptime | Monthly commitment for a single-zone service with a different failure domain from multi-zone IaaS. |
The difference between control-plane, node, IaaS, and PaaS measurements is decisive. A control plane can be reachable while a GPU host is down; a node can be healthy while your data path is unavailable; and a multi-zone figure can assume redundancy that your deployment does not use.
Ask what counts as downtime
Request the exact start and stop events used for measurement. Clarify whether downtime begins when an API call fails, when a job cannot start, when a node loses power, or when performance falls below a threshold. Ask how scheduled maintenance, customer configuration errors, exhausted quotas, provider network dependencies, and third-party failures are excluded. Require the measurement period, time zone, monitoring source, aggregation method, and credit-claim deadline.
Separate durability from availability
NVIDIA’s AI-cloud requirements cite more than 99.99% durability for home-directory filesystems below 1 PB, 99.99% high-speed-storage availability over a rolling 30-day service-level objective, and more than 99.5% end-to-end availability per petabyte. Durability means data is not lost; availability means the service can be reached. Neither figure proves that backups are restorable or that an entire region can be recovered. Ask which storage tier your workload uses and whether the figure applies to your specific region and configuration.
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Redundancy: single zone, multiple zones, or multiple regions?
Architecture often matters more than the headline percentage. A single-zone deployment has one principal facility or failure domain. Multi-zone designs place components in independent zones, while multi-region designs add geographic separation and protection against a regional outage. Independence must be verified: zones that share power, network, control-plane dependencies, or storage systems may not fail independently.
Match redundancy to the workload
- Long training jobs: prioritize checkpoint frequency, restart automation, and spare capacity. A brief node outage may be acceptable if the job resumes from a recent checkpoint.
- Interactive inference: use redundant service replicas, health checks, and traffic failover. A single high-end GPU node is a single point of failure regardless of its SLA percentage.
- Batch pipelines: keep a queue and retry policy so work can move to another node or region.
- Data-intensive workloads: verify that storage, metadata, network bandwidth, and credentials remain available when compute fails.
Ask whether failover capacity is reserved or merely hoped for. A provider may advertise multi-zone availability while having no guaranteed spare GPUs in the second zone during a regional shortage.
Support is part of reliability
Hardware failures are inevitable; slow or unclear response turns them into prolonged outages. Before production, obtain the support policy and require:
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- Initial-response, update-frequency, workaround, and restoration targets for each severity.
- 24/7 channels for critical incidents, plus an escalation path beyond the first-line queue.
- Maintenance-notice periods, emergency-maintenance rules, and a status page or equivalent incident channel.
- Post-incident report contents, including timeline, affected services, root cause, corrective actions, and evidence of recovery.
- Credit-claim procedure, deadline, required logs, and whether credits are the exclusive remedy.
NVIDIA requires providers to map their severity levels to a common model. Its cloud-services SLA says NVIDIA will use commercially reasonable efforts to find a resolution within 24 hours after it determines an issue is critical, while explicitly warning that this is a target that may not always be achieved. The wording matters: a resolution target is not the same as a guaranteed restoration time, and the clock may start only after the provider classifies the incident as critical.
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Test a failure, not just the help desk
Ask whether the provider will run or demonstrate a failed-node replacement exercise. Record how a ticket is opened, what diagnostics are requested, how a replacement is assigned, whether data and IP addresses persist, and when users receive updates. If the provider will not perform a live exercise, request a recent anonymized incident timeline and the documented replacement workflow.
Data portability and exit rights
Portability should be proven before you commit production data. Inventory every object that must leave the service:
- Raw datasets, transformed data, and metadata.
- Model checkpoints, tokenizer files, images, containers, and build artifacts.
- Logs, metrics, audit records, experiment histories, and job definitions.
- Identity mappings, access-control policies, secrets, encryption keys, and certificates.
- Infrastructure configuration, Kubernetes manifests, Slurm scripts, network rules, and environment variables.
Confirm export mechanisms and costs
Ask which formats are supported, whether exports are complete or incremental, and whether timestamps, permissions, checksums, and metadata are preserved. Check egress prices, minimum charges, rate limits, transfer windows, and whether data can be copied directly to another cloud. NVIDIA documents scp and sftp transfer workflows and requires AI-cloud partners to support movement to external clouds, shared access to GPU-mounted storage, and stable egress IP addresses. Those capabilities should be verified for the specific service you are buying, not assumed for every neocloud.
Run a small round trip
- Select a representative dataset, checkpoint, configuration bundle, and log sample.
- Export them using the documented method, recording bytes, hashes, permissions, and elapsed time.
- Import the package into an independent environment and recreate a test job.
- Compare outputs, model hashes, metadata, and access behavior.
- Calculate the cost and operational steps for a full export, including any temporary storage and network fees.
Include deletion in the test. The contract should state when provider copies, snapshots, backups, caches, and support-access records are deleted, what evidence you receive, and how long legal or security holds can delay deletion.
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Storage, backup, and recovery responsibilities
Do not treat a durability statement as a backup plan. Establish who performs backups, how often they run, where copies are stored, how long they are retained, and who pays for restore operations. Require documented recovery-point and recovery-time objectives for critical datasets. Ask whether the provider can restore into another zone or region and whether encryption keys remain usable during an outage.
A draft NeoCloud security baseline recommends publishing responsibilities for backup and restore, export and deletion, evidence retention, support access, and end-of-service timelines. Put those responsibilities in your agreement rather than relying on an informal support promise.
Comparison checklist for vendor selection
| Capacity type and delivery evidence | GPU model/topology | Zone and region redundancy | Uptime scope and exclusions | Support severity, response, and restore targets | Credit or remedy | Storage durability and backup | Egress price and export mechanism | API/container/Kubernetes/Slurm portability | Incident evidence and termination rights |
|---|---|---|---|---|---|---|---|---|---|
| NVIDIA requirements call for API and transport readiness, developer capacity, and a data-mover environment before GPU delivery. | Not stated in the cited requirement; specify the exact model and topology in the order. | Not stated; require independent failure domains. | 99.95%+ financially backed production control-plane target; exclusions and other layers require service-specific terms. | Severity mapping required; critical-resolution target is 24 hours after classification, and may not be achieved. | Financial backing is specified for the control-plane target; exact credit schedule is service-specific. | More than 99.99% home-directory durability below 1 PB; other backup duties must be assigned contractually. | External-cloud movement, shared GPU-mounted storage, stable egress IPs, and scp/sftp workflows are documented requirements or methods. | Confirm the interfaces offered for your service and test scripts or containers. | Request incident records, export evidence, and explicit termination and deletion terms. |
| National Compute Base Load Capacity; replacement provisioning is part of the uptime measurement. | Per-GPU-node commitment; model and topology not stated in the cited figure. | Not stated. | 99% per GPU node; replacement-provisioning time counts as downtime. | Not stated in the cited figure. | Credits apply below the target. | Not stated. | Not stated. | Not stated. | Request current incident and termination terms. |
| Nebul redundant multi-zone IaaS. | Not stated. | Redundant multi-zone design. | 99.9% combined monthly uptime; review exclusions. | Not stated. | Not stated in the cited figure. | Not stated. | Not stated. | Not stated. | Request current incident and exit terms. |
| Nebul single-zone PaaS. | Not stated. | Single zone. | 99.5% monthly uptime; review exclusions. | Not stated. | Not stated in the cited figure. | Not stated. | Not stated. | Not stated. | Request current incident and exit terms. |
A practical pre-production reliability test
- Request the current SLA, service description, support policy, security terms, and order form for the exact region and product.
- Ask the provider to define downtime, exclusions, measurement source, maintenance handling, and credit-claim deadlines in writing.
- Obtain recent capacity evidence: delivered-versus-ordered dates, replacement times, and anonymized incident records.
- Run a failed-node replacement exercise if available, or review a complete recent replacement timeline.
- Deploy a small workload across the intended zones and verify scheduling, networking, storage mounts, checkpoints, and restart behavior.
- Export a representative dataset and checkpoint, restore them elsewhere, and record time, cost, hashes, and missing metadata.
- Document who controls encryption keys, logs, backups, administrator access, support access, and deletion approvals.
- Have legal and engineering teams confirm termination assistance, data-return format, deletion evidence, and end-of-service deadlines.
Do not treat a marketing uptime claim as an established reliability fact until its measurement method, scope, exclusions, and remedy are written into the contract.
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What public SLAs can—and cannot—tell you
Published SLAs show what a provider promises, not an independently measured outage rate across the neocloud market. There is no comparable public historical dataset covering neocloud providers as a group. SLA versions, regional capacity, hardware availability, and support policies can change, so verify the current documents before signing or renewing. The most defensible conclusion is therefore conditional: a neocloud is reliable when its capacity is contractually real, its architecture matches the workload’s failure tolerance, its support process is measurable, and you have tested recovery and exit before production.
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