Original Summary

When trying to analyze video hooks beyond simple Whisper text transcripts, the inference costs hit a wall fast. Analyzing video dynamically means dealing with three competing variables: Full-video token consumption (feeding 30–60 seconds of high-FPS video into Vision APIs will drain credits immediately). Pacing extraction (scene changes and audio beat alignment require frame sampling rather than continuous processing). Latency expectations from end users who expect results in under 5 seconds. The architecture that ended up being sustainable without bleeding cash: Strip the audio track first locally via FFmpeg to get loudness spikes and silences (costs almost 0 compute). Sample keyframes strictly at scene cut boundaries instead of uniform 1 FPS sampling. Run OCR and text-detection on the raw frames locally before sending anything to external LLMs. By running the visual extraction heuristics before making the API call, inference token counts dropped by nearly 70% per run. For those building multimodal pipelines: are you handling pre-processing on the server layer before the model call, or has model pricing dropped enough recently that you just pass raw streams directly to Gemini or Claude?   submitted by   /u/Redhead7624 [link]   [comments]

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  • 信息来源：Reddit · SaaS
  • 发布时间：2026/9/19 09:46:15