LLMs are fast becoming a ubiquitous part of many modern applications - powering key functionality across various industries. But as powerful as they are - they are very expensive to run. Research and Markets reports that global spend on LLMs surpassed $6.4B in 2024, and is expected to surpass $30B by 2030.

In practice, many companies struggle with the unit economic impact of leveraging LLMs for their AI features. While users love these new capabilities, the cost of supporting them may be unsustainable. In one prominent example, it’s been reported that every subscriber to Microsoft GitHub’s Copilot feature costs Microsoft up to $80 per month. Given the subscription is only $20 per month - that could mean up to -300% negative gross margins. That is obviously unsustainable.

As focus shifts from developing impressive AI features to making these features sustainable, developers are shifting their focus to finding ways to optimize LLM consumption and reduce the cost burden. The key challenge is doing this without sacrificing performance & accuracy. In this guide, we have collected several key techniques for doing just that. We have supported companies in using these techniques to reduce their LLM costs by up to 90% without any impact on accuracy. We believe most companies can easily adopt these techniques to deliver more cost-effective AI.

This guide showcases 5 key techniques we have used to radically reduce LLM inference costs. Not all of these techniques are applicable to every scenario, but it is more than likely that at least 1-2 of these will be applicable for your application.