One Key, Six Vendors: How Per-Provider Logging Rules Differ When You Switch Models

When you call a language model through a unified API, your request travels through multiple layers: your application, the aggregator, and the model provider. Each layer may log data for different purposes. Understanding how each provider handles logging and training opt-outs is key to matching models with your data sensitivity.

Why request logging matters

Every API call can be logged at several points:

• Your own application (for debugging, analytics, or compliance)
• The API aggregator (for billing, abuse prevention, and troubleshooting)
• The model provider (for service improvement, safety, and sometimes training)

The provider's policies determine whether your prompts and completions are stored, for how long, and whether they can be used to train future models. If you handle sensitive data, you need to know these details before sending requests.

General patterns in provider logging

Most commercial providers offer some form of zero-retention or no-training option, but the specifics vary:

• Claude (Anthropic): By default, inputs and outputs may be retained for a limited period for trust and safety. Anthropic offers enterprise options with stricter data handling. Training on customer data is opt-in for some services.
• GPT (OpenAI): The API does not use data for training by default. Requests are logged for abuse monitoring and may be retained for a set period. Zero-retention options exist for eligible customers.
• DeepSeek: Data may be used to improve services unless you opt out. The opt-out process and retention periods differ from Western providers.
• Qwen (Alibaba): Logging and training practices are governed by Alibaba's cloud policies. Data may be stored within specific regions. Opt-out mechanisms depend on the service tier.
• GLM (Zhipu AI): Similar to other Chinese providers, data may be used for service improvement. Enterprise agreements can include stricter terms.
• Kimi (Moonshot AI): Logging and retention policies are defined by Moonshot AI. Training opt-outs may be available for business accounts.

Because policies change, always check the provider's latest documentation for definitive answers.

How an aggregator fits in

When you use an API aggregator that routes to multiple providers, the aggregator itself may log metadata (like timestamps, model used, and token counts) for billing and operational purposes. However, the aggregator does not control the provider's logging. Your prompts and completions are subject to the provider's policies once the request is forwarded.

Some aggregators offer features like request redaction or the ability to disable logging on their side, but the provider's logging still applies. It's important to understand both layers.

Choosing a model for sensitive data

If your data is sensitive, consider these steps:

• Review provider policies: For each model you plan to use, read the provider's data usage and retention policies. Look for explicit statements about training opt-outs and retention periods.
• Use zero-retention options if available: Some providers offer zero-retention for enterprise customers. Check if you qualify.
• Minimize sensitive data: Where possible, avoid sending personally identifiable information (PII) or confidential data. Use redaction or tokenization before sending requests.
• Consider self-hosted or private deployments: For highly sensitive workloads, self-hosting open-weight models (like some versions of Qwen or GLM) may be an option, but this requires technical expertise.
• Leverage aggregator features: Some aggregators provide logging controls or data processing agreements. Understand what they log and how long they retain it.

Practical comparison checklist

When evaluating providers, ask:

• Does the provider use API data for training by default? Can you opt out?
• How long are logs retained? Are there different retention periods for different data types?
• Is there a zero-retention option? What are the requirements?
• Where is data stored? Are there regional restrictions?
• What metadata does the aggregator log? Can you disable it?
• Are there compliance certifications (e.g., SOC 2, GDPR) that apply?

Conclusion

No single provider is universally best for all data sensitivity levels. Claude and GPT tend to offer more explicit no-training defaults for API users, while DeepSeek, Qwen, GLM, and Kimi may have different defaults and opt-out processes. Always verify current policies and combine technical controls (like redaction) with contractual ones (like enterprise agreements). Using a unified API aggregator simplifies access but does not eliminate the need to understand each provider's logging rules.