DigitalOcean Serverless Inference: A Deep Dive

The Problem: Inference Gets Hard at Scale If you’ve shipped an AI feature to production, you already know: the hard part isn’t making a model respond to a prompt. The hard part is making it respond more reliably, at scale, across multiple models, without burning through your budget. The moment real users show up, you’re dealing with GPU resource contention, traffic unpredictability (a single enterprise customer can 10x your request volume overnight), latency-cost tradeoffs that shift constantly, and multi-model orchestration across text, vision, image, video, and audio — each with different AP