Building AI apps isn’t as easy (or cheap) as you think
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.
The hidden cost of AI workflows
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.
Whether you’re wrestling with bloated API bills or struggling to balance performance with affordability—there are effective strategies to tackle these challenges. Here’s how you can keep your AI app costs in check without sacrificing performance.
We published the 5 top tips to slash your LLM cost:
- Optimize your prompts
- Implement response caching
- Use task-specific, smaller models
- Use RAG instead of sending everything to the LLM
- Use LLM observability tools.
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