5 Powerful Techniques to Slash Your LLM Costs
Building AI apps doesn’t have to break the bank. We have 5 tips to trim down your LLM costs while keeping performance sharp—because we also hate hidden expenses.
Building an AI app might seem straightforward — with the promise of powerful models like GPT-4 at your disposal, you’re ready to take the world by storm.
But as many developers and startups quickly discover, the reality isn’t so simple. While creating an AI app isn’t necessarily hard, costs can quickly add up, especially with models like GPT-4 Turbo charging 1 to 3 cents per 1,000 input/output tokens.
Sure, you could opt for cheaper models like GPT-3.5 or an open-source alternative like Llama, throw everything into one API call with excellent prompt engineering, and hope for the best. However, this approach often falls short in production environments.
AI’s current state means that even a 99% accuracy rate isn’t enough; that 1% failure can break a user’s experience. Imagine a major software company operating at this level of reliability—it’s simply unacceptable.
