One page: the 25 cost levers for AI in a video product, grouped into five families — process less video (frame sampling, downscale, crop, event-gating, prompt/output trimming); run a smaller model (routing, cascade, small open model, distillation, quantization); serve more efficiently (continuous batching, KV-cache reuse, speculative decoding, a purpose-built runtime, right-sizing); pay less per unit (batch API at 50% off, prompt caching at 90% off, semantic caching, spot GPUs, reserved capacity); and architect & govern (serverless scale-to-zero, right provider + egress control, hybrid edge+cloud, precompute once, per-feature cost budgets). Includes the worked-example waterfall ($135K → $4.7K, ~29×) and the pre-optimization questions. The lead-magnet asset for Phase 8.
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The book · Volume 7 of 7
AI for Video: A Complete Guide to Intelligence in Video Products: From Real-Time Pipelines and Agents to Serving, Cost, Compliance, and Verticals
This checklist lists 25 cost levers for video AI, from frame sampling and model routing to batching, caching and spot GPUs, with a $135K to $4.7K worked example. Volume 7 covers this in depth: serving and inference infrastructure with vLLM, Triton and TensorRT, cost per stream-hour, and the real-time pipelines where those levers apply. Written by Nikolay Sapunov, CEO at Fora Soft.
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