What are the latency and cost considerations for multimodal AI in production?

Multimodal is heavier than text on every axis. Images and especially video expand into many tokens or large encoder computations, so input processing is slower and pricier — a high-resolution image can cost as much as a long text prompt. Generation (diffusion for images/video) is many-step and GPU-intensive, giving seconds-to-minutes latencies you must design around with async jobs, progress feedback, and queues rather than blocking requests. Storage and bandwidth for media and their embeddings are significant. Levers: downsample or cap image resolution, sample video keyframes instead of every frame, cache embeddings and generated assets, batch work, and route to smaller models when quality allows. Set realistic latency budgets — real-time multimodal (live video understanding) is still genuinely hard and expensive.