How to Evaluate AI Infrastructure Providers for a Transatlantic Deployment

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Choose the provider that can prove fit for your exact workload, data flows, legal transfer mechanism, operational control, security, governance support, portability and total cost across both continents. A European region label or a sovereignty marketing term is not enough. Build a workload-specific evidence matrix, validate the EU–US data-transfer route with privacy counsel, and require architecture, assurance and exit documentation before committing critical AI systems.

Define what your transatlantic deployment actually includes

“Transatlantic” can describe several very different designs. A model may be trained in the United States, fine-tuned in the European Union, served from both regions, or operated in one region while support staff and telemetry systems remain elsewhere. Each arrangement creates different performance, privacy and sovereignty questions.

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Write the intended operating model before contacting providers. Record:

• Which models, model weights, datasets and prompts are involved.
• Where training, fine-tuning, inference, evaluation and batch processing will run.
• Which users are in the EU, the United States or other jurisdictions.
• Whether prompts, outputs, embeddings, logs, backups, support tickets and abuse-monitoring records contain personal or confidential data.
• Which business entity acts as controller, processor, model provider, infrastructure provider and deployer for each processing activity.

This inventory prevents a common error: approving an EU compute region while overlooking US-based support access, centralized logging, backup replication or a subprocessors’ analytics system.

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Set a workload baseline before comparing providers

Ask every candidate to price and test the same workload. Without a common baseline, accelerator names, region counts and advertised throughput are not comparable.

Capture the technical requirements

• Model shape: parameter size, quantization, context length, multimodal inputs and expected concurrent sessions.
• Accelerators: required GPU or other accelerator class, memory per device, interconnect and minimum cluster size.
• Storage: training data capacity, checkpoint frequency, object-storage durability, high-performance scratch space and retention periods.
• Network: bandwidth between compute and storage, cross-region replication, private connectivity and acceptable egress latency.
• Service objectives: tokens per second, batch completion time, p50 and p95 latency, maximum queue time, availability target and recovery time objective.
• Scaling pattern: steady inference, scheduled training bursts, elastic experimentation or a mixed workload.

Demand comparable evidence

Request a service-specific benchmark using your model, input distribution, batch size, concurrency and software stack. The result should state hardware, software versions, measurement method, warm-up treatment and whether network or storage time is included. If a provider cannot run your workload, require its benchmark methodology and a service commitment that maps to your acceptance test. No provider-specific performance result is established here, so do not substitute a generic benchmark for your own test.

Map every geography and data flow

Separate four locations that sales material often combines: the physical region, the provider’s contracting entity, the people who can access systems, and the jurisdictions whose laws may apply. A workload can run on EU hardware while administrators, support tools or backups operate from the United States.

Build a data-flow register

1. List each data class: training records, prompts, outputs, embeddings, model weights, logs, telemetry, billing data, support attachments and backups.
2. For each class, record origin, processing location, storage location, replication destination, retention period and deletion trigger.
3. Identify every human and machine access path, including provider operations, subcontractors, support escalation, security monitoring and abuse prevention.
4. Mark whether the data is personal, confidential, regulated, export-controlled or commercially sensitive.
5. Document onward transfers and the legal entity receiving each transfer.

Distinguish residency from sovereignty

Residency answers where data is stored or processed. Sovereignty additionally asks who controls the provider and operating entities, which laws can compel access, who controls encryption keys, where software is developed and maintained, and whether the customer can continue operating if a foreign parent, supplier or legal regime changes.

Ask for a region-specific architecture diagram rather than a map of available regions. The diagram should show control planes, metadata services, identity systems, key-management systems, logging, backups, support tooling and failure-mode traffic. Require the provider to identify any service that cannot be pinned to the selected geography.

Validate the EU–US transfer mechanism and contracts

The European Commission adopted its EU–US Data Privacy Framework (DPF) adequacy decision on 10 July 2023. For US organizations covered by that decision, personal data within its scope can flow from the EU to the United States. The European Data Protection Board describes the effect this way: “The effect of such a decision is that personal data covered by the decision can freely flow from the EU to that non-EU country or international organisation.”

That decision is a transfer mechanism, not a blanket finding that a cloud service, deployment or customer is compliant with the GDPR. Check the current DPF listing for the exact US legal entity and the relevant data categories. If the recipient is not covered, or the transfer falls outside the decision’s scope, document another valid route and the safeguards that accompany it. Have privacy counsel assess the actual data, purposes, recipients and risk rather than relying on a region label.

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Review the processor relationship

A controller-to-processor arrangement still requires a contract governing personal data processed on the controller’s behalf, whether or not the processor participates in the DPF. Examine the processor terms for:

• Documented processing instructions, confidentiality and security obligations.
• Subprocessor names, locations, notice periods and objection rights.
• Onward-transfer controls and assistance with data-subject requests.
• Breach notification timing, investigation cooperation and evidence access.
• Deletion or return at termination, including replicas, snapshots and support systems.
• Audit rights, independent reports and a process for handling government-access requests.

Do not accept “DPF participant” as a substitute for these terms. Record the contracting entity, service-specific scope and effective dates in the procurement file, then recheck them before production.

Use sovereignty frameworks as questions, not certifications

The European Commission’s proposed Cloud and AI Development Act (CADA), described on its 3 June 2026 proposal page, aims to strengthen the EU cloud and AI ecosystem. Its proposed assurance levels progress from EU-based processing and storage toward greater independence, control and software supply-chain transparency.

CADA is a proposal, not an operative certification or final legal test. Use its progression to structure evidence requests:

For workloads that must remain usable during a geopolitical or contractual disruption, add continuity evidence: alternative operators, escrow or reproducible build arrangements where appropriate, independent key control, and a tested migration path.

Compare security and resilience evidence

Ask for current, independent assurance reports that cover the services and regions you will use. A corporate certificate that excludes AI accelerators, managed control planes or your selected geography has limited value.

Security controls to examine

• Identity federation, least-privilege roles, privileged-access approval and administrator session logging.
• Customer-managed keys, hardware security modules, key-location controls and separation of duties.
• Encryption in transit and at rest, confidential-computing options where relevant, and secrets handling.
• Vulnerability disclosure, patch windows, image provenance, software bill of materials and emergency-change procedures.
• Isolation between tenants, accelerator sanitization and secure decommissioning.
• Incident response contacts, notification deadlines, forensic cooperation and evidence retention.

Resilience questions

• What happens when a selected accelerator type is unavailable?
• Can workloads fail over between EU and US regions, and what data-transfer implications follow?
• How are checkpoints, keys, container images and configuration restored after a regional outage?
• What recovery time and recovery point objectives are contractually supported?
• Which dependencies remain single-region or single-supplier?

Require a recovery exercise for critical systems. A written disaster-recovery description is not proof that the required model, data and keys can be restored within your objective.

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Assess AI governance support and regulatory roles

NIST’s AI Risk Management Framework is voluntary guidance for incorporating trustworthiness considerations into AI design, development, use and evaluation. It is a useful way to organize questions about governance, measurement, monitoring and incident handling, but it is not a provider certification.

Ask whether the service supports:

• Dataset and model-version records, approval workflows and provenance.
• Access controls for prompts, outputs, evaluation sets and model artifacts.
• Pre-deployment testing, continuous evaluation, drift detection and abuse monitoring.
• Human oversight, incident intake, rollback and evidence retention.
• Exportable logs and documentation for internal audits or regulator inquiries.

Map legal roles for the actual system. The customer, infrastructure operator, model developer and deployer may have different responsibilities. The European Commission’s Article 50 guidance indicates that providers outside the EU may be in scope when system output is used in the EU. Determine the particular obligations, dates and role assignments from current official guidance; do not assume the infrastructure provider is always the regulated AI-system provider.

Test portability and plan the exit before signing

Portability is a technical and commercial requirement, not a clause added after deployment. Compare:

• Export formats for datasets, logs, embeddings, model weights, fine-tuning adapters, containers and infrastructure definitions.
• Compatibility with alternative accelerators, orchestration platforms, object stores and identity systems.
• Use of proprietary APIs, serving runtimes, feature stores or observability tools that create rework.
• Data-egress, snapshot-transfer and temporary dual-running costs.
• Provider assistance, migration windows, deletion certificates and support after termination.

For a critical workload, run a small exit rehearsal: export a representative dataset and model, deploy it in an independent environment, verify outputs and measure elapsed time, engineering effort and transfer cost. Record dependencies that could not be moved and assign owners to remove them.

Compare total cost for the same workload

Published rates and discounts change frequently, so obtain current quotes for an identical architecture and usage profile. The cost model should include:

Normalize each quote to a business unit such as one completed training run, one million generated tokens at a defined quality target, or one month of inference at specified concurrency. Show assumptions, committed-use discounts, minimums and currency. A low compute rate can be outweighed by egress, reserved-capacity risk or proprietary migration work.

Build an evidence-based comparison matrix

Score only what each provider demonstrates for the proposed service. A simple 0-to-3 scale works: 0 means no evidence, 1 means a policy or claim without service-specific proof, 2 means documented controls or a test with limitations, and 3 means independently supported evidence plus a successful customer test. Set minimum gates for privacy, security, recovery and exit rather than allowing a high performance score to compensate for a failed legal requirement.

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Request this evidence from every candidate

Send a common request so responses are comparable. Require:

1. A data-flow and subprocessor map for the exact services, regions and data classes.
2. The applicable DPF listing or alternative transfer documentation, with the recipient legal entity identified.
3. Processor terms, onward-transfer controls, deletion language and data-subject assistance.
4. Region-specific architecture showing control planes, support access, keys, logs and backups.
5. Current independent assurance reports and scope statements.
6. Service-specific AI compute, latency, availability and capacity evidence.
7. AI governance, audit, monitoring and incident-support capabilities.
8. Portability, recovery, migration and termination procedures, including a test option.
9. A complete workload quote with assumptions, commitments, data movement and support charges.

Mark every response as contractual, independently assured, provider-reported, customer-tested or unanswered. Unanswered items are procurement risk, not neutral scores.

Follow a staged procurement process

1. Classify the workload and data. Identify personal, confidential and regulated information, required regions and recovery objectives.
2. Shortlist on hard gates. Remove candidates that cannot provide required geography, transfer documentation, accelerator capacity or security scope.
3. Run a technical proof. Measure the intended model and software under representative load in both planned regions.
4. Complete privacy and legal review. Confirm roles, processor terms, subprocessors, transfer route and government-access provisions.
5. Test resilience and exit. Exercise recovery, export a representative workload and measure migration effort.
6. Negotiate measurable commitments. Put region restrictions, notification, availability, support response, deletion and evidence delivery into the contract.
7. Approve with an operating review date. Assign owners to recheck DPF status, subprocessors, AI Act guidance, regions, prices and assurance reports.

Avoid common evaluation mistakes

Treating a region selector as data sovereignty

Region selection may not control metadata, support access, keys or backups. Demand the complete architecture and an exception list.

Assuming the DPF equals GDPR compliance

The adequacy decision addresses a transfer route for covered data to participating organizations. It does not replace the processor contract, security assessment, purpose limitation or other GDPR duties.

Comparing accelerator labels instead of delivered results

Different software stacks, memory limits, queue policies and interconnects can change outcomes. Use the same workload and acceptance test.

Accepting a corporate certificate without checking scope

Verify that the report covers the selected service, region, control plane and processing activity, and that its period is current.

Leaving exit until after production

Proprietary APIs, embeddings and data-transfer fees can make a nominally portable model expensive to move. Test export before signing a long commitment.

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Confusing guidance with certification

NIST AI RMF is voluntary guidance, and the proposed CADA assurance levels are not an operative certification. Use both as structured question sets, then verify the provider’s actual controls.

Assuming the infrastructure provider owns every AI Act obligation

Map the real parties and uses. An overseas provider can still face obligations when output is used in the EU, but the applicable duties depend on its role and the system’s facts.

Account for strategic dependency without misusing forecasts

A 2025 European Commission communication projected that US hyperscalers could drive 65% of European data-centre demand by 2028. That is a forecast, not an observed 2028 market-share result. It can inform a dependency discussion, but it should not replace provider-specific evidence about ownership, legal control, capacity or exit options.

For a deployment that must operate through policy or supply disruptions, document an alternate region or operator, maintain exportable artifacts, and rehearse the switch. The objective is not to eliminate every cross-border dependency; it is to understand, price and control the dependencies your risk appetite permits.

Maintain the decision after deployment

Approval is not permanent. Establish quarterly or risk-based checks for DPF status, subprocessors, region changes, administrator-access paths, assurance-report renewals, accelerator availability, service-level performance, new AI Act guidance and material pricing or egress changes. Re-run the data-flow review whenever you add a model, logging service, support channel or replication target.

Keep the comparison matrix, test scripts, benchmark results, contract versions, counsel’s transfer analysis and exit-test evidence together. When a provider cannot answer a new question, record the gap, assign a deadline and decide whether the workload can continue while it is unresolved.

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