This article is part of VentureBeat’s special issue, “The Real Cost of AI: Performance, Efficiency and ROI at Scale.” Read more from this special issue.
AI has become the holy grail of modern companies. Whether it’s customer service or something as niche as pipeline maintenance, organizations in every domain are now implementing AI technologies — from foundation models to VLAs — to make things more efficient. The goal is straightforward: automate tasks to deliver outcomes more efficiently and save money and resources simultaneously.
However, as these projects transition from the pilot to the production stage, teams encounter a hurdle they hadn’t planned for: cloud costs eroding their margins. The sticker shock is so bad that what once felt like the fastest path to innovation and competitive edge becomes an unsustainable budgetary blackhole – in no time.
This prompts CIOs to rethink everything—from model architecture to deployment models—to regain control over financial and operational aspects. Sometimes, they even shutter the projects entirely, starting over from scratch.
But here’s the fact: while cloud can take costs to unbearable levels, it is not the villain. You just have to understand what type of vehicle (AI infrastructure) to choose to go down which road (the workload).
The cloud story — and where it works
The cloud is very much like public transport (your subways and buses). You get on board with a simple rental model, and it instantly gives you all the resources—right from GPU instances to fast scaling across various geographies—to take you to your destination, all with minimal work and setup.
The fast and easy access via a service model ensures a seamless start, paving the way to get the project off the ground and do rapid experimentation without the huge up-front capital expenditure of acquiring specialized GPUs.
Most early-stage startups find this model lucrative as they need …