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For sensitive workloads, the strongest non-Chinese AI API shortlist is Microsoft Foundry models sold by Azure, OpenAI API, Anthropic Claude API, Amazon Bedrock, and Cohere. None is automatically compliant merely because the vendor is outside China. You must verify where prompts are processed, where data is stored, how long each feature retains it, which company is the processor, and whether your exact model, endpoint, region, and contract provide the controls your data requires.
Azure and Bedrock are often the easiest choices for organizations already governed by Microsoft or AWS identity, networking, key-management, and audit systems. OpenAI and Anthropic document strong no-training or zero-data-retention options, but eligibility and feature exceptions matter. Cohere is particularly useful when you need a private-cloud or customer-VPC deployment rather than a direct vendor endpoint.
What “data-sensitive” should mean when choosing an AI API
A sensitive application may handle personal data, health information, payment records, legal work product, source code, trade secrets, credentials, or confidential business plans. The relevant question is not simply whether an API is Chinese or non-Chinese. It is whether you can prove a defensible data boundary for the specific request path you will operate.
Evaluate six separate dimensions before comparing model quality or token prices:
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- Processing geography: the country or region where inference, safety checks, routing, and transient processing occur.
- Storage geography: where prompts, outputs, files, logs, backups, and account records are stored at rest.
- Retention: default retention, deletion timing, abuse-monitoring retention, and the behavior of stateful features.
- Processor and contract: whether the model vendor or a cloud platform processes the data, and which DPA, BAA, or other terms apply.
- Security integration: identity controls, private networking, customer-managed keys, audit logs, and regional policy enforcement.
- Compliance scope: the exact service, model, endpoint, region, and feature covered by GDPR, HIPAA, ISO, SOC, or another commitment.
Only after those conditions are acceptable should you compare capability, latency, context limits, throughput, and total cost.
Shortlist at a glance
| API or platform | Documented data controls | Important boundaries to verify | Best fit |
|---|---|---|---|
| Microsoft Foundry models sold by Azure | Prompts, completions, embeddings, and training data are not used to train generative AI foundation models without permission or instruction. Standard deployments process prompts and responses within the customer-specified geography; stored data is encrypted at rest there and can be deleted by the customer. | Global and DataZone deployments can process across broader geographies. Abuse-monitoring controls and preview features have separate conditions. | Organizations already using Microsoft identity, policy, regional controls, and audit tooling. |
| OpenAI API | API inputs and outputs are not used to train models by default. Qualifying organizations can configure retention controls, including zero data retention, and eligible customers can select data residency at rest and supported US or European processing regions. | Zero-retention and residency availability depends on the project, endpoint, model, region, and organization eligibility. | Teams that need a direct API with documented residency and retention options and can satisfy eligibility requirements. |
| Anthropic Claude API | Organization-level zero-data-retention arrangements are available for eligible use. Anthropic documents inference geography and feature-specific retention behavior. | Standard Messages API calls may qualify for ZDR, while files, batches, managed agents, and code execution have documented retention. Flagged content may be retained for safety or legal reasons. | Applications that can remain stateless or can isolate features with different retention rules. |
| Amazon Bedrock | Uses AWS shared-responsibility controls and supports API and user-activity logging through CloudTrail. It integrates with AWS IAM, VPC, KMS, and regional governance. | Model-specific terms, deployment geography, and logging configuration must be checked for the selected model and workflow. | AWS-governed environments that need centralized identity, network, key, and audit controls. |
| Cohere | Offers a public SaaS API data opt-out and deployments through Amazon Bedrock, Azure AI Foundry, Oracle Cloud Infrastructure, and customer AWS VPC environments. | Controls differ between the direct SaaS service and each cloud or VPC deployment; confirm the processor and contract for the chosen route. | Teams prioritizing private networking or alignment with an existing cloud contract. |
Microsoft Foundry models sold by Azure
Why Azure is attractive for confidential workloads
Microsoft states that customer prompts, completions, embeddings, and training data are not available to other customers or model providers and are not used to train generative AI foundation models without the customer’s permission or instruction. For standard deployments, prompts and responses are processed within the geography selected by the customer. Stored data is encrypted at rest in the customer-designated geography and can be deleted by the customer.
That combination is valuable when your organization already enforces Microsoft Entra identity, Azure Policy, private connectivity, centralized logging, and regional data controls. The API becomes part of an existing governance plane instead of a separate vendor exception.
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Geography choices require close reading
Do not treat an Azure region label as proof that every operation stays there. Standard deployments are the narrowest geography option described by Microsoft. Global and DataZone deployments can process requests across a broader geography. Select the deployment mode only after documenting the countries or zones that may receive prompts, completions, safety signals, and transient data.
Retention and exceptions
Customer-deletable stored data and encrypted storage do not mean every internal system is instantly erased. Abuse-monitoring controls can have separate conditions, and preview features may follow different terms from generally available services. Record which features are enabled, whether they create server-side state, and how deletion requests are executed and verified.
When to choose Azure
- Your security team requires Microsoft identity, policy, private networking, or regional controls.
- Your procurement process already has Microsoft contractual coverage and an established data-processing agreement.
- You can use a standard regional deployment instead of a global or DataZone mode.
- You need a managed service with customer-controlled deletion and at-rest encryption documented by the provider.
OpenAI API
Default training policy
OpenAI states: “We don’t train our models on your organization’s data by default.” That is a useful baseline, but it is not a complete retention or residency guarantee. Training use, operational retention, abuse monitoring, and stateful product features are separate questions.
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Zero data retention and residency
Qualifying organizations can configure retention controls that include zero data retention. Eligible customers can also select data residency at rest and supported United States or European processing regions. These options are not universal account settings: eligibility, endpoint, model, project configuration, and region all matter. Obtain written confirmation for the exact project that will receive sensitive prompts.
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- Is your organization approved for zero data retention, and does that approval cover every endpoint you plan to call?
- Does the chosen model support your selected US or European processing region?
- Are files, batches, assistants, or other stateful features included in the retention arrangement?
- Which logs are generated by your application, gateway, observability tools, and the API service itself?
- What deletion workflow applies if a request, project, or organization must be removed?
When to choose the direct OpenAI API
It is a practical choice when you need a direct API, can meet the organization’s eligibility requirements, and are prepared to pin each project to approved residency and retention settings. Build a configuration test that proves the selected endpoint and model inherit those settings before sending regulated data.
Anthropic Claude API
Zero-retention arrangements are feature-specific
Anthropic documents organization-level zero-data-retention arrangements. Standard Messages API calls can be eligible, but ZDR does not automatically cover every Claude feature. Files, batches, managed agents, and code execution have documented retention behavior that must be assessed separately.
Inference geography and processor changes
Anthropic publishes inference-geography information for its API. If you invoke Claude through Amazon Bedrock or Google Cloud, the cloud provider is the data processor, and that platform’s controls, regions, logs, and contract govern the request path. The same model name therefore can have a different compliance profile depending on whether you use Anthropic directly or through a cloud marketplace.
Safety and legal exceptions
Anthropic states that content flagged for safety or legal reasons may be retained. A zero-retention arrangement should therefore be described accurately as a feature and organization control, not as an unconditional promise that no copy can ever be kept under any circumstance.
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- Your workload can use stateless Messages API calls and avoid retained files, batches, agents, or code execution.
- You need Claude capabilities but prefer cloud-provider governance through Bedrock or another supported platform.
- Your legal and security teams can document the handling of flagged content and the processor for each deployment route.
Amazon Bedrock
AWS-native governance
Bedrock operates under AWS’s shared-responsibility model. It supports API and user-activity logging through CloudTrail and fits naturally with AWS IAM, VPC networking, KMS keys, and regional governance. For an AWS-controlled environment, these integrations can be more important than the model vendor’s standalone policy because identity, network egress, key ownership, and audit evidence remain in established administrative systems.
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What Bedrock does not decide for you
Bedrock is a platform for multiple models, not a single universal retention or geography policy. Check the terms and deployment geography for the selected model. Confirm whether prompts and outputs are logged by CloudTrail or application services, whether your VPC path is private end to end, and which party is the processor under your contract.
When Bedrock is the safer operational choice
- Your organization already restricts workloads with AWS IAM roles, VPC endpoints, security groups, and KMS key policies.
- Auditors expect CloudTrail evidence for API activity and administrative actions.
- You need to change models without redesigning the surrounding network and identity controls.
- Your regional policy requires all selectable model deployments to remain in approved AWS regions.
Cohere
Deployment flexibility
Cohere documents a public SaaS platform with an API data opt-out. It also supports deployment through Amazon Bedrock, Azure AI Foundry, Oracle Cloud Infrastructure, and customer AWS VPC environments. That range makes Cohere relevant when the key requirement is private networking or alignment with an existing cloud contract rather than a direct vendor endpoint.
Compare the route, not just the model
A direct Cohere SaaS call and a Cohere deployment through Bedrock or a customer VPC can involve different processors, regions, logs, support access, and deletion procedures. Obtain the terms for the exact route. Confirm whether data remains inside your cloud account, which service handles inference, and whether the opt-out applies to the deployment you selected.
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When Cohere fits
- You need an API that can be placed inside an existing AWS VPC or cloud governance boundary.
- Your procurement team prefers an AWS, Azure, or Oracle contractual relationship.
- Private networking and deployment control outweigh the convenience of a single direct vendor endpoint.
Processing geography, storage geography, and transient data
These are related but different controls. A provider may store data at rest in Europe while routing inference through another approved zone. Conversely, inference may occur in a permitted region while application logs or backups are stored elsewhere.
| Question | Evidence to request | Common mistake |
|---|---|---|
| Where is inference performed? | Deployment mode, region list, routing policy, and any global or data-zone behavior. | Assuming the billing region is the physical inference location. |
| Where is data stored at rest? | Residency setting, storage region, backup location, and disaster-recovery policy. | Treating storage residency as proof that transient processing stays there. |
| Where do logs go? | Provider logs, CloudTrail or equivalent audit logs, gateway logs, telemetry, and support records. | Checking only the model API while ignoring your own observability pipeline. |
| Can routing expand geography? | Documentation for standard, global, data-zone, failover, and capacity-routing modes. | Enabling a broader mode to reduce latency without updating the data map. |
Document every geographic hop in a data-flow diagram. Include the client, API gateway, redaction service, model endpoint, safety systems, storage, backups, monitoring platform, and human support path.
Retention: stateless calls are not the same as a stateless product
Ask for retention behavior at the feature level. A simple request-and-response call may have a different policy from a file upload, batch job, assistant, managed agent, code interpreter, conversation thread, or fine-tuning workflow.
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Build a retention inventory
- Prompt and completion: default operational retention and whether zero retention is available.
- Files and attachments: upload duration, indexing copies, deletion command, and backup expiry.
- Batches: result storage, failed-job records, and maximum documented retention.
- Agents and assistants: conversation state, tool traces, memory, and server-side instructions.
- Abuse monitoring: conditions under which content can be reviewed or retained for safety or legal reasons.
- Your systems: API gateways, traces, prompt-debug logs, error reports, analytics, and support tickets.
For each item, record the retention period or state that the provider does not specify one, the deletion trigger, the person or system authorized to delete it, and the evidence produced after deletion.
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Processor, contract, and compliance scope
Identify the processor before procurement signs off. With a direct vendor API, the model provider may be the processor. With Bedrock, Azure, or another cloud-hosted route, the cloud platform may process the request under its own terms even when another company supplies the model. Anthropic explicitly notes this distinction for Claude used through Amazon Bedrock and Google Cloud.
Request the current DPA and, where relevant, BAA, subprocessors list, international-transfer mechanism, incident-notification terms, deletion commitments, and support-access rules. Match every promise to the exact service and model. A provider’s enterprise compliance page does not automatically cover a preview endpoint, a third-party model, or a separate agent feature.
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Identity and least privilege
Use workload identities or short-lived credentials where available. Separate development, test, and production projects. Give each service only the models, regions, and operations it needs, and revoke unused keys.
Private networking and egress
Prefer private endpoints, VPC or virtual-network integration, and explicit egress rules when the platform supports them. Ensure that DNS, proxies, certificate inspection, and service-mesh telemetry do not copy prompts into an unapproved system.
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Verify encryption in transit and at rest, then determine whether customer-managed keys are supported for the data that matters. Key control is useful only if your organization also manages rotation, access policy, recovery, and revocation.
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Capture caller identity, model, endpoint, region, policy decision, request identifier, and deletion event without recording sensitive prompt content in ordinary logs. AWS customers can use CloudTrail for Bedrock API and user-activity evidence; Microsoft and other providers have their own logging and monitoring controls that must be configured and scoped.
A practical selection procedure
- Classify the data. Separate public, internal, confidential, personal, regulated, and secret data. Define whether prompts may contain identifiers, raw documents, credentials, or customer records.
- Set geographic constraints. List permitted countries or regions for inference, storage, support access, backups, and subprocessors. Reject global or data-zone modes that exceed the list.
- Choose the processor path. Decide between a direct vendor endpoint and a cloud-hosted deployment. Record the processor, subprocessors, contract, and transfer mechanism for that path.
- Map every feature. Start with stateless text or multimodal calls. Add files, batches, agents, code execution, or fine-tuning only after documenting their separate retention behavior.
- Verify eligibility in writing. Ask the provider to confirm residency, zero retention, deletion, and compliance coverage for your organization, project, endpoint, model, and region.
- Configure the controls. Apply regional deployment, retention settings, IAM policies, private networking, KMS or equivalent keys, audit logging, and prompt-redaction rules.
- Test the data path. Send synthetic records, inspect provider and internal logs, confirm the observed region and endpoint, exercise deletion, and verify that backups and monitoring do not retain the test data unexpectedly.
- Put drift detection in place. Alert when someone changes deployment mode, region, model, endpoint, logging destination, or enabled stateful features.
- Reassess on change. Repeat the review when the provider introduces a preview feature, changes routing, updates terms, or your application begins sending a new data category.
Decision guide by common scenario
Your company is Microsoft-first
Start with a standard regional Microsoft Foundry deployment. Use existing Microsoft identity, policy, networking, encryption, and audit controls, and avoid Global or DataZone modes unless their broader processing geography is approved.
Your company is AWS-first
Evaluate Amazon Bedrock first because IAM, VPC, KMS, CloudTrail, and regional governance can be applied consistently. Select the model only after reviewing its model-specific terms and geography.
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OpenAI and Anthropic both document zero-retention pathways, but neither is universal. OpenAI requires qualifying organizations and supported project configurations. Anthropic’s ZDR is feature-specific, with documented retention for files, batches, managed agents, and code execution. Keep the implementation within the eligible feature set or obtain explicit coverage for each exception.
You need private-cloud or customer-VPC deployment
Consider Cohere through a supported cloud platform or customer AWS VPC environment, then compare the resulting processor, region, network path, and contract with a direct SaaS deployment.
You have regulated data but no approved contract
Do not send production records yet. Use synthetic or irreversibly de-identified data while procurement obtains the DPA or BAA, confirms the service scope, and documents residency, retention, deletion, and incident obligations.
Operational checklist before the first sensitive request
- Approved provider, model, endpoint, deployment mode, and region are recorded.
- Processor, subprocessors, DPA or BAA, and transfer terms are approved.
- Training-use policy and operational-retention policy are both documented.
- Zero-retention eligibility, if required, is confirmed for the exact project and features.
- Files, batches, agents, code execution, and other stateful features are disabled or separately approved.
- Private networking, identity, key management, and least-privilege access are configured.
- Prompt content is excluded from routine logs, traces, analytics, and error reports.
- Deletion and access-review procedures have been tested with synthetic data.
- Region, endpoint, model, and policy changes generate an audit event and alert.
- A named owner reviews provider terms and deployment settings on a scheduled basis.
Bottom line
The best alternative is determined by your required data boundary, not by a country label or a model leaderboard. Choose Azure when Microsoft governance and standard regional processing fit your controls; Bedrock when AWS identity, network, key, and audit integration are decisive; OpenAI or Claude when their documented residency and zero-retention options cover your exact project and features; and Cohere when private or cloud-aligned deployment is the priority. In every case, prove the processor, geography, retention, deletion path, and compliance scope before production data leaves your environment.
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