NuExtract3, a 4B parameter vision language model, was released as a self-hostable alternative for document understanding and structured data extraction tasks including markdown conversion and OCR. The model addresses a specific operational constraint: document processing pipelines currently reliant on closed API endpoints face latency, cost, and data residency friction. A capable self-hostable VLM reduces vendor lock-in and enables batch processing on private infrastructure without per-token billing or external API dependencies. For teams operating document extraction at scale, this shifts economics from variable API costs to fixed inference infrastructure. The 4B parameter size targets edge deployment and cost-efficient serving on modest hardware, making it viable for organizations previously priced out of vision-language workflows. Operators can now evaluate moving extraction pipelines from APIs like Claude's vision or GPT-4V to local inference, trading API flexibility for deterministic latency and cost predictability. The practical constraint becomes whether OCR and markdown extraction quality matches closed-model performance across their specific document types—a validation problem, not an architectural one.