Choose an AI infrastructure provider for EU–US workloads by proving eight things for the specific system: workload performance, data locations and access, lawful transfer mechanisms, operational sovereignty, security and resilience, AI-governance support, portability, and total cost. A region selector or a “sovereign” label is only a starting claim. Require architecture diagrams, contracts, audit evidence, service measurements, and a tested exit plan before approving production use.
Start with a workload-specific decision, not a provider shortlist
“AI infrastructure” can mean GPU or accelerator instances, managed training platforms, object storage, databases, networking, observability, support, or a combination. The right choice depends on the workload and on which data and people touch it. Write a one-page deployment profile before requesting quotes.
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- Models and lifecycle: list the foundation models, fine-tuning jobs, retrieval components, inference endpoints, batch pipelines, and evaluation tools.
- Data classes: identify personal data, confidential business data, regulated records, prompts, outputs, training sets, checkpoints, logs, telemetry, backups, and derived data.
- Performance target: specify accelerator type or capability, memory, storage IOPS, network bandwidth, throughput, latency percentile, concurrency, and scaling behavior.
- Availability target: state required uptime, recovery-point objective (RPO), recovery-time objective (RTO), maintenance windows, and acceptable regional failover.
- Geography: define where processing, storage, support, security monitoring, backups, and administrative access may occur in the EU, the United States, or elsewhere.
- Compliance role: document whether your organization is controller, processor, deployer, provider of an AI system, or more than one of these for each feature.
Give every candidate the same profile. Otherwise, a lower quote may simply omit logging, redundancy, support, or data-movement costs that another candidate included.
Compare the dimensions that can change the decision
| Dimension | Questions to answer | Evidence to request |
|---|---|---|
| Workload performance | Can the required models run with the needed memory, throughput, latency and concurrency? Is capacity available in both regions? | Service-specific benchmark methodology, accelerator configuration, test results, capacity commitments and service-level terms. |
| Geography and data flows | Where are prompts, datasets, logs, backups, support records and derived data stored, processed and accessed? | Region architecture, data-flow diagram, subprocessor list, transfer paths and administrative-access description. |
| Transfer and contracts | What legal mechanism covers EU personal data sent to a US organization? Does the processor agreement cover all services and subprocessors? | Applicable EU–US Data Privacy Framework (DPF) listing or alternative transfer documentation, processor terms, onward-transfer clauses and rights-assistance commitments. |
| Sovereignty and control | Who owns and controls the operating entities, systems, keys and software? Which laws and government-access processes may apply? | Ownership and control disclosures, key-management architecture, legal-process policy, software-maintenance locations and continuity plan. |
| Security and resilience | How are identity, vulnerabilities, incidents, backups and recovery handled? | Current independent audit reports or certifications, incident terms, vulnerability process, recovery design and availability commitments. |
| AI governance | Can you document, evaluate, monitor and audit the AI service throughout its lifecycle? | Access controls, evaluation records, monitoring interfaces, incident workflows, model and dataset documentation, and audit export capability. |
| Portability and exit | Can workloads, data and models move without proprietary dependencies or prohibitive egress costs? | Export formats, migration runbook, egress pricing, dependency inventory, recovery tests and estimated exit timeline. |
| Total cost | What is the cost of the complete workload, including movement, redundancy, support and migration? | Comparable quote with compute, storage, networking, support, reserved capacity, redundancy, data transfer and one-time migration items separated. |
Prove workload fit and performance
Specify the engineering requirement
Translate model names into measurable requirements. Record parameter size, precision, context length, peak and sustained request rate, maximum response time, batch size, training-epoch duration, checkpoint frequency, storage growth, and inter-node communication needs. For retrieval or multimodal systems, include vector-search latency, object access patterns, and media-transcoding requirements.
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Demand comparable measurements
Ask candidates to run your representative prompts, datasets, and concurrency profile, or to provide a benchmark with enough methodology to reproduce it. Require the accelerator model, memory allocation, software versions, compiler settings, quantization, input and output lengths, warm-up period, and percentile calculations. A headline tokens-per-second number without these conditions is not a comparison. The available evidence does not establish performance for any particular provider, so treat unverified claims as hypotheses.
Check capacity and scaling
Confirm that the required accelerator family is available in the approved EU and US regions at the same time. Ask how quota is obtained, how long scale-out takes, what happens during regional shortages, and whether a reservation is binding. For training, test multi-node networking and checkpoint recovery; for inference, test autoscaling delays and cold-start behavior. Put the tested limits and support response into the order or service agreement.
Map geography, access and every data flow
A data-center region is not the same thing as a data boundary. Draw the path for each data class from ingestion to deletion. Include training and fine-tuning data, prompts, completions, embeddings, model weights, logs, telemetry, backups, support tickets, abuse monitoring, billing records and disaster-recovery copies.
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Separate four location questions
- Storage location: the physical region holding a persistent copy.
- Processing location: where CPUs, accelerators and managed control planes handle the data.
- Access location: where provider employees, contractors, support tools and security operations can view or administer systems.
- Legal and operational location: the entities that contract, operate the service, receive government requests, develop software and control subprocessors.
Ask whether telemetry or support diagnostics leave the selected region, whether encryption keys remain under your control, and whether backups replicate across the Atlantic. Obtain a subprocessor map that names each service function and country, not merely a list of corporate entities. Require advance notice and an objection or termination process for material subprocessor changes.
Understand what the EU–US Data Privacy Framework does—and does not do
The European Commission adopted its EU–US Data Privacy Framework adequacy decision on 10 July 2023. For covered personal data and a US organization participating in the framework, the decision permits transfers to that recipient. The European Data Protection Board describes the effect of an adequacy decision 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, architecture, or deployment complies with the GDPR. Verify that the actual contracting recipient appears on the current DPF list, that the relevant services and data are within its scope, and that onward transfers are addressed. If DPF coverage is absent or insufficient, require documentation for another valid transfer route and have privacy counsel assess the facts.
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Keep the processor contract separate from the transfer analysis
A controller-to-processor relationship still requires a contract for personal data processed on the controller’s behalf, whether or not the processor participates in the DPF. Review processing instructions, confidentiality, security measures, deletion or return, audit rights, assistance with data-subject requests and breach obligations. Check every subprocessor and onward-transfer provision; a DPF listing does not replace these terms.
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Residency asks where data and workloads are stored or processed. Sovereignty adds questions about ownership, control, personnel access, applicable law, keys, software independence and the ability to operate through disruption or legal pressure. A workload can reside in the EU while being controlled by a non-EU entity and administered from outside the EU.
The European Commission’s proposed Cloud and AI Development Act (CADA), described in a 3 June 2026 proposal page, offers a useful vocabulary rather than a current certification. Its four assurance levels progress from EU-based processing and storage toward stronger independence, control and software-supply-chain transparency. Because CADA is a proposal, do not treat an assurance level as an operative legal test or claim that a provider is certified under it.
Turn sovereignty language into evidence
- Request the ownership chain and the entities that control operations, keys, software releases and incident decisions.
- Identify which jurisdictions’ laws can compel access and how the provider handles and reports such requests.
- Ask where software is developed, signed, updated and supported, and how dependencies are inventoried.
- Require options for customer-held or customer-controlled keys, dual control and emergency access review.
- Examine continuity if a parent company, region, license or cross-border route becomes unavailable.
Assign EU AI Act responsibilities to the real parties
Do not assume the infrastructure provider is automatically the regulated provider of your AI system. Map the customer, infrastructure operator, model developer, application owner and deployer for each use case. The Commission’s Article 50 guidance indicates that obligations can reach providers outside the EU when system output is used in the EU; determine whether that condition applies to your system, which duties attach, and the dates in force under current official guidance.
Ask candidates what evidence they can supply for your role: system and model documentation, logging, access control, evaluation results, human-oversight mechanisms, incident handling, transparency notices and retention controls. Put responsibilities in writing so that a managed service does not leave you unable to produce records required by your own risk or regulatory process.
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Independent assurance
Request current independent audit reports or certifications relevant to the exact service, region and control scope. Check the reporting period, exceptions, complementary customer controls, subservice organizations and whether the report covers the AI control plane as well as basic infrastructure.
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Identity, keys and privileged access
Confirm support for federated identity, least privilege, hardware-backed key protection where required, separate production and training accounts, administrator approval, session logging and rapid credential revocation. Ask who can access plaintext prompts, checkpoints and logs, under what approval, and how access is recorded.
Incidents and vulnerabilities
Review detection coverage, severity definitions, notification deadlines, forensic cooperation, customer communication, patch timelines and vulnerability-disclosure channels. Clarify whether provider personnel can disable controls during an incident and who authorizes that action.
Backup and recovery
Require architecture for immutable or logically isolated backups, encryption, cross-region copies, restore testing and dependency recovery. Validate the stated RPO and RTO with a witnessed or documented test. A multi-region design is not resilient if identity, keys, registries or model artifacts remain single-region.
Structure AI governance with a recognized risk vocabulary
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 evidence requests, not a provider certification.
Use its risk-management perspective to ask how the service supports:
- Governance: ownership, policies, roles, records and escalation.
- Mapping: intended use, affected groups, data provenance, limitations and foreseeable misuse.
- Measurement: accuracy, robustness, bias, privacy, security, explainability and drift evaluations using documented methods.
- Management: controls, monitoring, incident response, remediation and retirement.
Request machine-readable logs and exportable evaluation records. Establish who retains prompts and outputs, how long they remain available for investigations, and whether provider personnel may use customer content to improve shared services. If a candidate cannot answer these questions for a managed model endpoint, plan to supply the missing controls yourself or select a different architecture.
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Test portability and the exit plan before production
Portability is more than downloading objects. Inventory model weights, tokenizer versions, container images, infrastructure-as-code, feature stores, vector indexes, secrets, schemas, prompts, evaluation sets, logs and dashboards. Mark dependencies on proprietary APIs, scheduling, networking, accelerators, observability and identity services.
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Run an exit exercise
- Export a representative dataset, model checkpoint, configuration and audit history in documented formats.
- Recreate the workload in the alternate region or a second environment, including keys, identities and network policy.
- Measure transfer time, egress charges, re-indexing, model warm-up, accuracy changes and downtime.
- Revoke the original provider’s access, verify deletion and capture evidence of retained backups and legal holds.
- Record the people, approvals and supplier dependencies needed to complete the move within the approved RTO.
Repeat the exercise after major architecture changes. Obtain price schedules for egress and migration assistance; a nominally portable design can still be uneconomic to move.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Compare total cost for the same workload
Ask for a quote that separates recurring and one-time costs. Include accelerator time, CPU and memory, storage tiers, snapshots, backup capacity, inter-region replication, internet and private-network transfer, load balancing, observability, support, reserved or committed capacity, licenses, security services, migration, testing and exit. Model both normal and peak demand, plus the cost of maintaining EU and US resilience.
Rates, discounts and service commitments change frequently and were not established here. Use dated, written quotes and state the currency, region, billing unit, commitment term, minimum spend, overage treatment and tax assumptions. Compare cost per completed training run or per million production requests only after each candidate uses the same quality, latency and availability targets.
Send every candidate the same evidence request
- Data-flow and subprocessor map: include storage, processing, telemetry, support, backups and onward transfers.
- Transfer documentation: provide the applicable DPF listing or the alternative mechanism and its scope.
- Processor terms: include instructions, security, rights assistance, deletion, audits, subprocessors and onward-transfer controls.
- Region architecture: show control planes, access paths, key management, isolation and failover.
- Independent assurance: supply current reports, scope, exceptions and customer responsibilities.
- Service measurements: provide accelerator configuration, latency, throughput, capacity and availability evidence for the proposed AI services.
- Governance support: describe documentation, evaluation, monitoring, incident and audit exports.
- Portability and recovery: provide export formats, egress terms, migration runbook, restore tests and deletion evidence.
Have privacy counsel validate the transfer analysis for the actual data and contract. Have security, engineering, procurement and the system owner sign off on their respective evidence rather than allowing one “compliance” score to hide unresolved technical gaps.
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Weight criteria according to the workload. For example, a regulated inference service may make lawful data flows, privileged access and recovery gating criteria, while a short-lived public-data experiment may weight accelerator availability and portability more heavily. Score only documented evidence; mark unsupported claims as “not demonstrated,” not as a zero that implies a measured failure.
| Gate | Pass condition | Typical failure response |
|---|---|---|
| Data and transfer | Every personal-data flow has an approved mechanism, contract and subprocessor path. | Remove the data, redesign the flow, or obtain counsel-approved safeguards before testing. |
| Performance | Representative tests meet latency, throughput and capacity targets in approved regions. | Change accelerator, architecture or region; do not rely on an incomparable vendor benchmark. |
| Security | Control scope, access, keys, incidents and recovery satisfy the security baseline. | Add customer controls or reject the service if a mandatory control cannot be implemented. |
| Governance | Roles, records, monitoring and incident evidence support the intended regulatory duties. | Assign missing controls to a named party with deadlines and audit rights. |
| Exit | A timed export, restore and deletion exercise meets the approved RTO and budget. | Reduce proprietary dependencies, negotiate assistance or select another design. |
| Economics | Total cost is comparable for identical demand, resilience and support assumptions. | Rebase quotes and include migration, redundancy and data movement before approval. |
Common mistakes and how to correct them
“The data is in Europe, so it is sovereign.”
Correct this by documenting ownership, remote administration, key custody, software supply chain and applicable law in addition to region placement.
“DPF participation means GDPR compliance.”
Correct this by verifying the recipient and covered data, then separately executing and reviewing the controller–processor contract, security measures and onward transfers.
“A certification covers every service.”
Correct this by checking the report’s service, region, dates, exceptions and complementary customer controls. A certificate outside that scope is not evidence for your deployment.
“The infrastructure company owns the AI Act duties.”
Correct this with a role map for the actual model, application, deployer and use of outputs in the EU. Assign each documentation, transparency, monitoring and incident obligation explicitly.
“Multi-region automatically means resilient.”
Correct this by testing identity, keys, registries, checkpoints, networking, observability and restore procedures together. A region failover that cannot authenticate or load the model is not a recovery plan.
“Portability means an object-store export.”
Correct this with a timed, end-to-end exit exercise that includes model behavior, indexes, secrets, logs, costs, deletion and approvals.
Account for strategic dependency without overstating forecasts
A European Commission communication in 2025 projected that US hyperscalers could drive 65% of European data-center demand by 2028. That is a forecast, not an observed 2028 market-share result. Use it only to prompt questions about concentration, capacity, bargaining power and continuity. It cannot substitute for provider-specific availability, pricing or sovereignty evidence.
Decision checklist before signing
- The workload profile and acceptance tests are written and identical across candidates.
- EU and US processing, storage, support, backup and access paths are diagrammed.
- Privacy counsel has approved the transfer mechanism and processor terms for the actual data.
- Ownership, jurisdiction, keys, privileged access and software-control evidence is documented.
- Independent assurance reports, incident terms and recovery tests cover the selected services.
- AI-system roles and governance responsibilities are assigned contractually.
- Performance and availability have been tested in the intended regions.
- Export, migration, egress, restore and deletion have been exercised and budgeted.
- The total-cost model includes redundancy, support, data movement and exit.
- Evidence has an owner, date and renewal trigger so the decision can be revisited when laws or services change.
Recheck official regulator guidance and provider documentation at procurement time. Transfer status, AI Act implementation, proposed EU cloud policy, regions, prices and service commitments can change; the approval should record its assumptions and expiry date.
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