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The safest way to choose an AI infrastructure vendor is to test evidence against your exact workload, deployment and risk—not to accept a platform’s headline benchmark or certification badge. Define the job first, map every infrastructure dependency, require current proof for each material claim, run a workload-specific proof of concept, and put measurable obligations and exit rights in the contract. This method applies whether you are buying GPU capacity, a managed training environment, an inference service, or a partner-operated deployment.
1. Define the decision before you contact vendors
Start with a one-page requirements brief. A vendor cannot be evaluated fairly until every candidate is answering the same question. UK government AI-procurement guidance recommends multidisciplinary preparation, discovery of available data and output-based requirements tied to user needs and required performance.
Describe the workload precisely
- Workload type: training, fine-tuning, batch inference, interactive inference, retrieval, simulation, or a mixture.
- Workload shape: model families and sizes, sequence lengths, dataset sizes, concurrency, job duration, checkpoint frequency and expected accelerator utilization.
- Service targets: maximum latency, throughput, queue time, job-completion time, accuracy or quality thresholds and recovery objectives.
- Growth pattern: steady demand, predictable peaks, research bursts, seasonal use or an uncertain experiment pipeline.
Record constraints and consequences
- Data classification, residency and sovereignty requirements.
- Required regions, network locations and connectivity to existing systems.
- Retention, backup and deletion obligations for data, model weights, checkpoints, logs and artifacts.
- Availability, recovery-time and recovery-point objectives, plus the business or safety impact of an outage.
- Budget assumptions, purchasing authority, contract term and acceptable commitment.
- Existing identity, networking, storage, orchestration, observability and security tooling.
- Internal skills available to operate clusters, pipelines, security controls and incident response.
Include procurement, engineering, security, privacy, legal and domain specialists as the use case requires. The National Cyber Security Centre (NCSC) recommends scaling assurance to the consequences of data leakage, corruption or service unavailability. That consequence assessment determines how much independent evidence, testing and contractual protection you need.
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“AI infrastructure vendor” can mean very different things. An infrastructure provider may supply accelerators, servers, networking, storage, virtualization, containers, schedulers, orchestration, observability and support. An AI application or model supplier may instead expose an API or managed environment while relying on another cloud and several subcontractors. Some companies combine both roles.
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| Layer | What to identify | Buyer question |
|---|---|---|
| Compute | GPU or TPU model, host server, memory, interconnect, virtualization and allocation policy | Which exact hardware generation is reserved for this proposal, in which region, and how is capacity assigned? |
| Network | Cluster fabric, customer connectivity, segmentation, bandwidth, egress paths and dependencies | What throughput and failure behavior should we expect between nodes, storage and our systems? |
| Storage | Block, file and object tiers, performance, replication, backup, deletion and location | Where do datasets, weights, checkpoints, logs and backups reside, and when are copies destroyed? |
| Software | Drivers, libraries, images, schedulers, container runtime, orchestration and supported versions | Which versions are fixed, which can we control, and how are compatibility changes announced? |
| Security and operations | Identity, privileged access, monitoring, vulnerability management, support and incident response | Which controls are operated by the supplier, which are ours, and what evidence covers each? |
| Subcontractors | Underlying cloud, colocation, managed services, support and data processors | Who operates each dependency, and who owns the response when it fails? |
The Cloud Security Alliance’s AI infrastructure auditing guidance calls for a complete inventory covering GPU or TPU clusters, storage, networking, schedulers, virtualization and orchestration. The NCSC adds that if a service is built on another cloud, you must assess the underlying platform and how the supplier configures it; a product-level assurance statement is not enough.
3. Turn sales claims into an evidence register
Create a claim/evidence register before scoring vendors. Each row should connect one material claim to proof that applies to the contracted service and your intended deployment.
| Register field | What to capture |
|---|---|
| Claim and owner | The exact promise, the supplier entity responsible and the person who approved it. |
| Scope | Service name, region, hardware, software edition, tenancy model and subcontractors covered. |
| Date and validity | Evidence issue date, expiry or review cycle, and whether it is current for the buying decision. |
| Conditions | Workload assumptions, utilization, network path, data volume, concurrency and exclusions. |
| Independent validation | Audit, test report, customer-specific demonstration or other verification, with its limitations. |
| Exceptions and owner | Out-of-scope components, compensating controls, residual risk and who accepts it. |
Ask for demonstrations and documents rather than corporate-level badges. NCSC guidance says the scope and freshness of certification matter more than the existence of a certificate, and recommends current evidence and recurring audits. AI TrustMark’s 19 September 2026 guidance similarly emphasizes matching evidence to the exact product and deployment. A certificate for a different region, service tier or underlying platform should be recorded as a gap, not treated as proof.
4. Compare workload fit, then run your own proof of concept
Do not infer suitability from peak accelerator specifications. Set acceptance criteria before a proof of concept (PoC), use representative or appropriately synthetic data, and measure the complete path from data ingestion to usable output. UK procurement guidance recommends PoC testing; the Data & Trusted AI Alliance (DTA) framework includes uptime, accuracy, resilience and integration among performance considerations.
Build a comparable test plan
- Freeze the model, software versions, dataset characteristics and configuration that each vendor will use.
- Define pass/fail thresholds for throughput, latency, queue time, job completion, quality or accuracy, availability and recovery.
- Measure steady-state operation and burst behavior, including queueing and capacity acquisition.
- Test scale-out and scale-down, multi-node communication, checkpointing and restart after an injected failure where the contract and safety rules permit it.
- Record operational effort: setup time, integration work, alerts, support interactions and the skills required from your team.
- Preserve raw measurements, configuration files, timestamps and exceptions in the evidence register.
End-to-end results matter more than an isolated accelerator number. Include data transfer, storage reads and writes, orchestration overhead, software compilation, retries and human operations. A vendor benchmark is evidence of what the vendor measured under its assumptions; it is not a buyer test.
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5. Examine capacity, reliability and operations
Ask how the service behaves when demand rises or a component fails. A nominally fast cluster may be unusable if jobs wait in a queue, capacity is not guaranteed or support cannot restore a failed run.
- Capacity certainty: reserved, committed and on-demand capacity; allocation priority; lead time; and what happens during shortages.
- Scheduling: queue policy, fairness, pre-emption, maximum job duration and visibility into queue position.
- Resilience: redundant power, network and storage paths; failure domains; checkpoint and restart behavior; and regional recovery options.
- Maintenance: notice period, maintenance windows, customer control over upgrades and treatment of interrupted jobs.
- Support: coverage hours, severity definitions, response and restoration targets, escalation path and access to engineers.
- Incident communication: notification channels, update cadence, root-cause reports and customer responsibilities.
Request historical service-level reports or a customer-specific demonstration where available, but distinguish a supplier’s general history from a commitment for your configuration. Put the required availability, performance and support measures in the contract rather than relying on a presentation.
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Security diligence must address the actual accelerator and storage design, not only the provider’s corporate security program. The Cloud Security Alliance auditing guidance specifically covers multi-tenant isolation, GPU or TPU memory protection, storage and network separation, runtime monitoring, data sanitization, incident response and compliance.
Identity and privileged access
- Require centralized identity, multifactor authentication, least privilege, short-lived credentials and separate administrative roles.
- Ask how supplier staff access hosts, hypervisors, orchestration systems and customer data, and how that access is logged and reviewed.
- Confirm how secrets, service accounts and emergency (“break-glass”) access are controlled.
Isolation and data handling
- Request evidence that workloads cannot read another tenant’s accelerator memory, host memory, storage blocks, container layers or network traffic.
- Ask how accelerators are partitioned or shared, how memory is cleared between tenants and how failed or decommissioned hardware is sanitized.
- Verify segmentation between management, storage, cluster and customer networks, including controls on egress.
- Confirm encryption in transit and at rest, key ownership, backup protection and deletion verification.
Detection and response
- Identify logs and metrics you receive, retention periods, time synchronization and integration with your monitoring system.
- Review vulnerability scanning, patch timelines, image provenance and the process for urgent accelerator-driver or firmware flaws.
- Define incident severity, notification deadline, evidence preservation, investigation cooperation and recovery support.
Ask which attestation covers the proposed service and region, the period audited, controls excluded and any inherited controls. Independent validation is useful only when its scope matches the deployment.
7. Map data governance, model provenance and responsibility
Draw the complete data path before signing: source systems, upload, preprocessing, training or inference, caches, logs, backups, support access and deletion. Record where each artifact is stored and how long it persists.
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Questions the data map must answer
- Is customer data retained after a job, and is it used to improve a service or train a provider model?
- Which model weights, prompts, outputs, checkpoints and logs are stored, and can support personnel view them?
- Which subprocessors receive data, in what countries and under what transfer mechanism?
- Who is controller, processor or equivalent responsible party for each processing activity?
- How are deletion, access, correction, legal hold and backup-expiry requests handled?
The Information Commissioner’s Office (ICO) recommends agreeing processing roles and responsibilities, documenting AI systems and their limitations, and setting acceptable accuracy in advance. Request evaluation and fairness evidence relevant to your use case; ask how bias, accuracy trade-offs and drift are monitored, and which monitoring duties remain with you. The OECD’s 2026 Responsible AI Due Diligence Guidance treats compute, cloud and hardware firms as AI input suppliers in the value chain, while recognizing that roles can overlap. Your contract should therefore assign responsibilities across the whole chain instead of assuming the model provider owns every risk.
8. Evaluate integration and portability before lock-in occurs
Compatibility is more than an API demo. Test identity integration, network routes, data formats, container images, orchestration, observability, CI/CD, model registries and incident workflows with the systems you already operate.
- Which APIs, drivers, container formats and orchestration interfaces are supported?
- Can you bring your own images, libraries, keys, logging and monitoring agents?
- Can data, weights, checkpoints, prompts, configuration and audit logs be exported in documented formats?
- Which open standards or widely supported interfaces make migration possible?
- What performance or feature loss occurs when moving to another platform?
- What assistance, lead time and charges apply when you migrate or terminate?
UK procurement guidance recommends anti-lock-in strategies that address open standards and procurement terms. Treat portability as a design requirement from the first PoC: perform a small export and restore test, document dependencies and keep a usable copy of critical artifacts outside the service where policy permits.
9. Compare total cost on one consistent basis
Current prices vary by hardware generation, region, capacity type and contract. The reviewed guidance provides no comparable market price, so obtain written quotes for the exact configuration and normalize them to the same workload and period.
| Cost category | What to include in the comparison |
|---|---|
| Compute | On-demand, committed and reserved rates; minimum billing units; interrupted-job treatment; idle and failed-run charges. |
| Storage | Capacity, operations, snapshots, replicas, backups, retention and deletion fees. |
| Network | Ingress, egress, cross-zone or cross-region transfer, private connectivity and data staging. |
| Software | Operating images, orchestration, management tools, licenses, support tiers and premium features. |
| Operations | Setup, migration, integration, monitoring, security tooling, managed-service labor and internal staffing. |
| Scaling and shortage | Burst premiums, capacity reservation, queue-related productivity loss and alternative-region charges. |
| Exit | Export, data transfer, media handling, transition assistance, dual running and contract termination fees. |
Model at least the expected, low-utilization and peak cases. Separate recurring charges from one-time allowances and identify assumptions such as utilization, data volume and retention. Compare business value and risk as well as invoice totals; the DTA framework explicitly weighs cost, impact, return on investment, risk, performance, reliability and integration.
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10. Make the contract express the risk decision
Translate every critical acceptance criterion into a measurable obligation. Generic security language may provide little protection; NCSC guidance recommends specific, testable requirements.
| Contract area | Specify |
|---|---|
| Service levels | Availability measurement, latency or throughput scope, queue or capacity commitments, support response, restoration and remedies. |
| Security | Control responsibilities, isolation design, access logging, vulnerability timelines, audit rights and independent evidence. |
| Data | Locations, retention, training or improvement use, subprocessors, deletion, backup handling and breach notice. |
| Change control | Notice for hardware, software, model, region or policy changes; testing windows; approval rights for material changes. |
| Incidents | Notification deadline, communication cadence, cooperation, evidence preservation and root-cause reporting. |
| Exit | Export formats, migration assistance, transition period, fees, deletion certification and continued access during transfer. |
The ICO specifically advises including accuracy-based key performance indicators or service-level agreements in written contracts with third-party suppliers. If accuracy or fairness affects your use case, define the measurement method, test data boundaries, review frequency and remedy rather than promising an undefined “accurate AI.”
11. Ask every finalist the same questions
- Which exact service, region, hardware generation, software stack and subcontractors are included?
- Can you map where data, model weights, checkpoints, logs and backups reside and how long each persists?
- What evidence demonstrates isolation between customers on the proposed accelerator and storage configurations?
- Which security attestations cover this service and region, what period do they cover, and what is out of scope?
- Which workload-specific performance and availability commitments can be written into the contract?
- How are incidents, vulnerabilities, maintenance, capacity shortages and material changes communicated?
- What are all usage, storage, transfer, support, commitment and exit charges for our expected workload?
- How can we export data and workloads, and which standards or interfaces support migration?
- What customer-side skills, controls and operating responsibilities are required?
Require written answers tied to the proposed configuration. Resolve contradictions between a sales answer, technical documentation and contract language before scoring the vendor.
12. Score candidates and document uncertainty
Use one matrix for every candidate. Record evidence, not just a subjective score, across these axes:
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|---|---|
| Workload fit | Training, inference and fine-tuning support; accelerator and software ecosystem; scale; latency; throughput. |
| Availability and operations | Capacity certainty, queueing, resilience, maintenance, recovery, support and incident communication. |
| Security and isolation | Tenant boundaries, identity, data protection, auditability, independent validation and evidence freshness. |
| Data and governance | Location, retention, subprocessors, processing roles, logging, provenance and monitoring duties. |
| Integration and portability | Compatibility, APIs, open standards, export, migration effort and exit support. |
| Commercial fit | Normalized total cost, commitment flexibility, network and storage charges, support and business value. |
| Contract quality | Measurable service levels, security duties, change controls, remedies and termination assistance. |
Weight the axes according to the consequences you documented at the start. Preserve assumptions, unresolved questions, accepted gaps and compensating controls. The DTA framework says a shortfall against one framework item need not automatically disqualify a vendor when the supplier explains the choice and provides safeguards; use that principle to make exceptions explicit rather than silently lowering the bar. The framework was shaped by 26 member companies across 17 industries, but that development history is not evidence of any vendor’s performance.
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13. Keep the evaluation current after selection
AI infrastructure changes quickly. Hardware availability, software versions, service regions, pricing, contract language and compliance evidence can change during a contract. Set reassessment triggers for a major hardware or software change, new subprocessors, a region move, a material incident, repeated service-level failure, a new workload or a change in data sensitivity.
NVIDIA’s Requirements for AI Clouds, version 2.4 updated 1 September 2026, can be used as a provider-specific technical reference for GPU-cloud requirements. It is not a neutral certification that every vendor meets those requirements. Treat any similar supplier-authored baseline as an input to diligence, then verify the controls in your deployment.
Maintain a tested export, current architecture and dependency inventory, fresh assurance documents and a named owner for each residual risk. Re-run targeted PoC tests when a change could affect performance, isolation, accuracy or portability.
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What a defensible buying decision looks like
A defensible choice is not the vendor with the largest accelerator count or the longest compliance page. It is the option whose evidence applies to your workload and region, whose measured behavior meets pre-agreed thresholds, whose data and security responsibilities are clear, whose total cost is normalized, and whose contract gives you remedies and an exit path. Keep the evidence register and decision record with the contract so a future reviewer can see not only which supplier you selected, but why the residual uncertainty was acceptable.
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