Moderation Intake Accounting: Bulk LLM Text Classification API With Tenant Chargeback

Short answer: For cheap bulk CSV tagging, use an asynchronous LLM text classification API, estimate each tenant batch before it runs, and attach the eventual export to the same tenant ledger instead of sending one request per row. For a one-person B2B SaaS, the useful comparison is not a model leaderboard. It is the amount of accounting and integration work left in the product after classification finishes. Option Choose it when Tenant-cost consequence Catch Infrai You want a self-describing REST API whose public discovery supplies request and response schemas plus runnable examples One key and one bill make the external side of reconciliation smaller Moderation uses chat classification with JSON Schema because there is no dedicated moderation endpoint OpenAI direct Your product has already standardized on OpenAI Keep tenant attribution in your own job ledger A direct contract does not remove application-level CSV reconciliation Anthropic direct Your model decision is already Anthropic-specific Use the same internal ledger pattern You own the provider-specific adapter and export mapping Google Gemini direct Your model decision is already Gemini-specific Use the same internal ledger pattern You own the provider-specific adapter and export mapping Recommendation: use asynchronous chat classification with a closed label set, but treat the tenant ledger as the primary artifact and the provider batch as an execution detail. That keeps a nightly backfill away from the request path and makes every charge explainable before a human moderator sees the result. The model matters. The accounting boundary matters more. Start with the allocation unit, not the provider A moderation upload arrives as a CSV, but a CSV is a transport format, not a billing unit. The billing unit should be an immutable application job owned by one tenant. Give that job an internal ID, record the source-file identity, preserve the row identifiers, and bind the approved label vocabulary to it. Then estim