That is a fantastic point, and you've perfectly articulated the core trade-off we're facing: Accuracy vs. Cost.

You are 100% right. For the serious user (researcher, data analyst, etc.) the lack of an official subtitle is a non-starter. Relying solely on official captions severely limits the available corpus.

The suggestion to use powerful models like Gemini for high-accuracy, custom transcription is excellent, but as you noted, the costs can spiral quickly, especially with bulk processing of long videos.

Here is where we are leaning for the business model:

We are committed to keeping the Bulk Download of all YouTube-provided subtitles free, but we must implement a fair-use limit on the number of requests per user to manage the substantial bandwidth and processing costs.

We plan to introduce a "Pro Transcription" tier for those high-value, high-volume use cases. This premium tier would cover:

Unlimited/High-Volume Bulk Requests.

LLM-Powered Transcription: Access to the high-accuracy models (like the ones you mentioned) with custom context injection, bypassing the "no official subs" problem entirely—and covering the heavy processing costs.

We are currently doing market research on fair pricing for the Pro tier. Your input helps us frame the value proposition immesnely. Thank you for pushing us on this critical commercial decision!