Data retention
Data retention refers to the policies and practices governing how long user-submitted content and related data are stored by a platform after upload or generation. This includes text prompts, uploaded images, generated outputs, metadata, and account activity logs. Retention policies typically specify storage duration, conditions under which data is deleted, whether deletion is automatic or requires user action, and whether any data is used for purposes beyond providing the immediate service, such as model training.
For users of AI adult-content platforms, data retention has direct privacy implications given the sensitive nature of the material involved. Key practical questions include how long uploaded images or conversations remain on servers, whether users can request permanent deletion, whether backups persist after a deletion request, and whether content may be used to train future models. The availability and clarity of an opt-out mechanism for training use is particularly relevant, since default settings vary between platforms and are not always disclosed prominently.
This site scores data retention as one of five weighted axes, carrying the highest weight at 25 percent of the overall evaluation. The scoring specifically examines what happens to uploaded content: stated retention periods, deletion procedures, whether data is used for model training, and whether a clear opt-out exists. Platforms with short, clearly defined retention periods, accessible deletion tools, and explicit opt-out options for training use score more favorably on this axis than those with vague, indefinite, or undisclosed retention practices.
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