IndexableAI provider comparisonLast reviewed 2026-05-21
OpenAI API vs Azure OpenAI: data use and retention questions for SaaS teams
Choose the evidence path that matches the actual model route. Direct OpenAI API commitments should cite OpenAI platform sources; Azure OpenAI commitments should cite Microsoft Foundry, Azure terms, and Microsoft DPA sources.
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OpenAI API vs Azure OpenAI
Review areas
4 side-by-side areas
Source links
4 official sources
Side-by-side review table
Use this table to decide which evidence path supports customer-facing statements. It is not a vendor ranking.
- Review area
- Contract path
- OpenAI API
- Usually reviewed through OpenAI platform terms and OpenAI DPA for direct API use.
- Azure OpenAI
- Usually reviewed through Microsoft Azure terms, Microsoft DPA, and Foundry documentation.
- Review note
- Do not list the wrong provider in a customer DPA exhibit.
- Review area
- Model training
- OpenAI API
- Start with OpenAI platform data controls for API business data.
- Azure OpenAI
- Start with Microsoft Foundry data, privacy, and security documentation.
- Review note
- Keep product and tenant scope in the customer answer.
- Review area
- Retention
- OpenAI API
- Review OpenAI feature scope and your own application logs.
- Azure OpenAI
- Review Microsoft service scope plus Azure logs, diagnostics, and downstream storage.
- Review note
- A single retention number is risky unless every data copy is accounted for.
- Review area
- Subprocessors
- OpenAI API
- Use OpenAI subprocessor and DPA sources.
- Azure OpenAI
- Use Microsoft contract sources and the Azure service path.
- Review note
- Customer lists may need Microsoft, OpenAI, or both depending on real routing.
| Review area | OpenAI API | Azure OpenAI | Review note |
|---|---|---|---|
| Contract path | Usually reviewed through OpenAI platform terms and OpenAI DPA for direct API use. | Usually reviewed through Microsoft Azure terms, Microsoft DPA, and Foundry documentation. | Do not list the wrong provider in a customer DPA exhibit. |
| Model training | Start with OpenAI platform data controls for API business data. | Start with Microsoft Foundry data, privacy, and security documentation. | Keep product and tenant scope in the customer answer. |
| Retention | Review OpenAI feature scope and your own application logs. | Review Microsoft service scope plus Azure logs, diagnostics, and downstream storage. | A single retention number is risky unless every data copy is accounted for. |
| Subprocessors | Use OpenAI subprocessor and DPA sources. | Use Microsoft contract sources and the Azure service path. | Customer lists may need Microsoft, OpenAI, or both depending on real routing. |
Where each option is commonly used
- Direct OpenAI API for product features, assistants, embeddings, and application workflows.
- Azure OpenAI for teams already standardized on Azure, Microsoft agreements, and tenant controls.
- Hybrid setups where one feature uses direct OpenAI and another uses Azure OpenAI.
What to ask before choosing
- Which contract path will customer data sit under?
- Which region, tenant, model, and logging settings are required?
- Which customer-facing AI training and retention statements already exist?
What to monitor after choosing
- OpenAI platform data controls or Microsoft Foundry data privacy sources.
- DPA and product terms updates.
- Application logs, diagnostics, files, fine-tuning, and downstream storage.
Source links
The comparison points are review prompts tied to official sources. Confirm product scope before changing customer-facing language.
Data controls in the OpenAI platformhttps://platform.openai.com/docs/guides/your-dataOfficial sourceOpenAI Data Processing Addendumhttps://openai.com/policies/data-processing-addendum/Official sourceData, privacy, and security for Models sold by Azure in Microsoft Foundryhttps://learn.microsoft.com/en-us/azure/ai-foundry/responsible-ai/openai/data-privacyOfficial sourceMicrosoft Products and Services Data Protection Addendumhttps://www.microsoft.com/licensing/docs/view/Microsoft-Products-and-Services-Data-Protection-Addendum-DPAOfficial source
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AI vendor risk assessment templateUse this template to document the review questions that matter before customer data is sent to an AI vendor. It focuses on commitments and evidence, not abstract scoring.Vendor retention review worksheetUse this worksheet to separate vendor retention from your own retention. Many customer answers fail because prompts, tickets, recordings, or logs are copied outside the vendor path.Vendor DPA review worksheetUse this worksheet to connect a vendor DPA to your customer-facing promises. It helps separate contract evidence from product behavior, which often needs a separate source.
Related vendor pages
AI Vendor Packet organizes review packet evidence, comparison prompts, and review workflow support. It does not provide legal advice or decide which vendor your company should choose.
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