14 posts tagged with "multimodal"

Most teams add multimodal capabilities to an existing LLM pipeline without running the cost math first. They prototype with a few test images, it works, they ship — and then the first billing cycle arrives. The number is somewhere between embarrassing and catastrophic, depending on volume.

The problem isn't that multimodal AI is expensive in principle. It's that each modality has a distinct token arithmetic that compounds in ways that text-only intuition doesn't prepare you for. A single configuration parameter — video frame rate, image resolution mode, whether you're re-sending a system prompt every turn — can silently multiply your inference bill by 10x or more before you've noticed anything is wrong.

Adding vision to an LLM application looks deceptively simple. You swap a text model for a multimodal one, pass in an image alongside your prompt, and the demo works brilliantly. Then you push to production and discover that half your invoices get the total wrong, tables in PDFs lose their structure, and low-quality scans produce confident hallucinations. The debugging is harder than anything you faced with text-only systems, because the failures are visual and the LLM will not tell you it cannot see clearly.

This post covers what actually goes wrong when you move multimodal LLM inputs from prototype to production, and the architectural decisions that prevent those failures.