What AI Plaza and Model APIs Actually Do With Prompt Data

Prompt privacy is a chain of responsibility

When a professional sends a prompt through an API, the data-handling question is not simply whether a model provider trains on it. The prompt may pass through an application or platform, a routing layer, and one or more model providers. Each can process different information for different purposes: inference, account operation, abuse detection, safety, billing, or legal compliance. A claim about one layer does not automatically describe the whole chain.

This distinction is central to responsible AI governance. Training use, operational processing, and retention are separate questions. A provider may not use API content to train models and still retain request logs temporarily for security or abuse monitoring. A platform may not operate its own foundation model and still process prompts to deliver the service. And a retention limit is not the same as a guarantee that data never leaves the user’s system.

The comparison below is limited to published API and developer-platform terms, plus AI Plaza’s privacy policy. It does not cover consumer chat accounts. The statements are snapshots of the cited documents; teams should check the applicable contract and current documentation before deployment.

The practical unit of review is the data path

For each workflow, identify what information enters the system, which services receive it, why they process it, and how long it may remain available. Prompts can contain personal data, confidential business material, source code, customer records, or regulated information even when the user did not intend to submit those categories. Outputs also require review: they can reproduce sensitive details from inputs or make inaccurate or biased claims that affect people.

A useful governance review separates at least four questions:

1. Training: Is prompt or response content used to train or improve models, and under what conditions?
2. Operational processing: Is the content processed for inference, abuse monitoring, safety, security, support, or service operation?
3. Retention: What default period applies, what exceptions exist, and who can approve shorter or zero-retention arrangements?
4. Control and responsibility: Which party selects the provider, configures the service, informs end users, and establishes a lawful basis for processing?

The published terms answer these questions differently. “Not used for training” should not be read as “not processed,” “not logged,” or “not shared with a downstream provider.”

How API and platform terms differ

Intermediary platform: AI Plaza

AI Plaza is an interface and routing service, not a model API. Its privacy policy says, “Guest chats are not retained as registered account history on our servers.” That statement is specifically about guest chats and registered account history; it does not mean that no data is processed during a request. The policy describes processing account, usage, and prompt content to operate the service, and says registered conversations, memory, billing, support, and security records may be retained as needed for service operation, abuse prevention, disputes, or legal obligations. Prompts and uploads may be sent to the selected or automatically routed provider. The policy also states that AI Plaza does not train its own foundation models on customer prompts, does not sell personal information, and does not load a separate marketing analytics SDK. Ordinary inference does not send account credentials to model providers. [1]

The operational implication is that a user must assess both the intermediary and the selected model provider. A routing platform’s privacy commitments do not replace the provider’s API terms, and the provider’s terms do not describe all processing performed by the routing platform.

OpenAI API

OpenAI’s API documentation states, “We don’t train our models on your organization’s data by default.” API data is not used to train models unless the customer opts in. That is a training-use statement, not a promise of zero retention: abuse-monitoring logs may be kept for up to 30 days. Zero data retention is a separate control and requires OpenAI approval. [2]

For a deployment review, the relevant distinction is between the default training position and the operational logging configuration. Teams should verify eligibility and approval for any stricter retention control rather than assuming that an API account automatically has zero data retention.

Anthropic API

Anthropic’s API documentation says retained data is not used for model training without express permission. Its documentation also treats zero data retention as a separate arrangement, rather than an automatic consequence of using the API. Trust-and-safety flags may be retained longer than ordinary API data. [3]

This makes the retention exception important to document: a general retention period may not cover material associated with a safety investigation. Teams handling sensitive content should understand which data categories the applicable arrangement covers and whether an exception applies.

Paid Gemini API

For paid Gemini API services, Google’s terms say, “Google doesn’t use your prompts … or responses to improve our products.” The distinction between paid and unpaid API use matters: unpaid Gemini API quota may be treated differently and may permit use of prompts or responses to improve products. [4]

Accordingly, procurement and engineering controls should verify which service tier is actually being used. A project that begins on one quota or billing configuration and later changes configuration should reassess the applicable data terms rather than carry forward an assumption based on the original setup.

xAI API

xAI’s API security documentation says, “xAI does not train on this data.” It also states that API requests and responses are stored for 30 days by default for suspected-abuse auditing and are automatically deleted after that period. [5]

The practical distinction is again between model training and temporary security retention. Organizations should account for the default period in their data inventory and determine whether prompts submitted to the API are appropriate for that retention profile.

Alibaba Cloud Model Studio

Alibaba Cloud Model Studio’s disclosure says it “will never use your data for model training,” while the specified API-platform position is that customer business data is not used to develop or improve models without explicit consent. [6] The associated platform terms place responsibilities on customers to screen submitted information and comply with applicable requirements. The documentation also warns that personal information in an input may appear in generated output. [6]

That output possibility matters in professional settings: prompt minimization alone does not eliminate the need to review generated material before it is stored, sent, or used in a decision.

DeepSeek Open Platform

DeepSeek Open Platform’s terms govern the API and describe collection and processing of personal information under its privacy policy. They also require customers to disclose processing practices to end users and obtain consent or another lawful basis where applicable. The Open Platform terms do not publish the same default no-training sentence found in some of the other API materials discussed here. That difference should be recorded as a difference in published disclosure—not converted into a claim that the API trains on customer content. Consumer chatbot privacy statements are not a substitute for this API contract. [7]

For professional deployments, the terms make notice, minimization, and data-location review particularly relevant. Teams should establish what data is sent, what end users are told, and which legal basis and cross-border assessments apply to the specific deployment.

Choosing controls for a professional workflow

Compare terms without collapsing them into a single score

A side-by-side comparison is useful only if it preserves the distinctions among providers. The available documents describe different combinations of training defaults, monitoring, retention, exceptions, and customer obligations:

The table is not a ranking. A shorter retention period or a no-training statement does not alone determine whether a service is suitable. The decision also depends on the sensitivity of inputs, contractual coverage, available controls, jurisdiction, and the organization’s ability to enforce its own handling rules.

Apply minimization and review at the workflow level

A practical workflow example is a team using AI Plaza to route different professional tasks to current models such as GPT-6 Sol, Claude-Opus-5.5, Gemini-3.8-Flash, or Grok-4.7. The model choice does not change the underlying governance task: the team should identify the selected provider for each request, avoid placing unnecessary personal or confidential data in prompts, and check the provider’s applicable API terms. Catalog availability can change, so model names should be verified at the time of use. [1]

Useful controls include removing direct identifiers when they are not needed, restricting access to conversation histories, setting retention rules for internal records, and requiring human review before outputs affect customers, employees, or consequential decisions. Where a workflow includes personal information, teams should also document what users are told and what lawful basis supports the processing. These controls reduce exposure but do not replace contractual, security, or legal review.

Long-term governance implications

Privacy commitments do not resolve bias or accountability

Data terms help define how prompt content is handled; they do not establish that a generated answer is accurate, fair, or appropriate. A system may satisfy a no-training commitment and still produce biased or misleading outputs. Professional deployment therefore requires separate evaluation of output quality and potential impact, particularly where responses inform decisions about people or are reused without review.

The organization deploying the system remains responsible for deciding what information to submit, how outputs are checked, and whether affected users receive appropriate notice. A provider’s terms define part of the operating environment, not the organization’s complete ethical framework.

Treat documentation as an operational control

API terms and data pages can change. Governance teams should preserve the versions reviewed during procurement, record service tier and retention settings, and reassess when a provider, route, account configuration, or data category changes. This is especially important in multi-provider systems, where a single user-facing workflow can involve distinct platform and model-provider terms.

For each production use case, maintain a concise data-flow record: the prompt categories permitted, services that receive them, training and retention positions, applicable exceptions, user notices, and output-review responsibilities. Such a record makes it possible to distinguish a published commitment from an internal assumption and to revise controls when the underlying service configuration changes.

This comparison is informational, not legal or financial advice. Organizations should review current terms and obtain qualified advice for their specific obligations.

References

[1] https://aiplaza.app/privacy [2] https://developers.openai.com/api/docs/guides/your-data [3] https://platform.claude.com/docs/en/manage-claude/api-and-data-retention [4] https://ai.google.dev/gemini-api/terms [5] https://docs.x.ai/developers/faq/security [6] https://www.alibabacloud.com/help/en/model-studio/qwen-and-wan-training-data-disclosure [7] https://cdn.deepseek.com/policies/en-US/deepseek-open-platform-terms-of-service.html