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I Tested 9 Serverless GPU Providers for AI Inference in 2026. Here's What I'd Actually Use

heckno 2026年06月09日 05:10 4 次阅读 来源:Dev.to

TL;DR If you're shipping AI inference and tired of babysitting GPUs, serverless is the way out. You deploy the model, the platform scales it from zero to hundreds of GPUs and back, and you only pay for the time you actually use. If I'm picking one to start with, it's DigitalOcean . It's got the widest GPU lineup of any serverless provider (RTX 4000 Ada all the way up to NVIDIA Blackwell B300 and AMD's MI350X), one API and one bill instead of five, and it's simple enough to ship on without a sales call. (More on why that one's personal for me below.) Below I compare 9 providers across the things that actually matter: GPU specs, per-hour pricing, cold-start latency, model support, and how nice they are to build on. DigitalOcean, RunPod, Modal, Koyeb, Together AI, Replicate, Baseten, Fal, and Cloudflare Workers AI each win at something different, from cheap experimentation to global edge inference. Contents Why I ran this The field at a glance How I evaluated these providers Per-provider analysis: DigitalOcean RunPod Modal Koyeb Together AI Replicate Baseten Fal Cloudflare Workers AI Why I keep coming back to DigitalOcean The short version Questions I actually get asked Why I ran this Quick note on why this exists. At work I get a front-row seat to a lot of people shipping an AI model into production for the first time: students, first-time founders, my own team. And lately the same question keeps coming up: where do I actually run this thing? I was tired of answering with a shrug and "it depends," so I did the homework myself. Signed up, read the pricing pages, ran the comparisons, and wrote it all down. Nobody's a real expert at this yet, me included, so I'd rather share my notes and get corrected than pretend I've got it figured out. And here's the thing about AI inference in 2026: demand blew past what the old way of provisioning GPUs can handle. Teams that used to wait weeks for dedicated hardware now need a model live in minutes. The ground moved. And the stuff t

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