How to Lower Your AI Costs When Scaling Your Business

As AI adoption grows, technological maintenance isn’t the only component you need to keep up with; your budget also requires a watchful eye. Especially when inference workloads can scale data—and costs—quickly. Your AI inference bill comes down to three things: the hardware you use, the scale you need, and how fast it generates output. If you’re curious how you can lower LLM inference spending, here are three tips to reduce your overall AI costs as you scale: 1. Diversify your hardware Hardware is a major reason AI has historically been expensive: the only processing units available to run these workloads are GPUs, and demand exceeded supply (driving up costs). This is true for consumer-grade GPUs, where it's not uncommon to see prices two or three times above MSRP, and data center GPU scarcity is even worse. For a long time, NVIDIA held a large market share with its physical hardware and compute unified device architecture (CUDA) -only frameworks. AMD has since introduced open-source