As I dive deeper into building an agentic application, I’ve hit a roadblock: how to reduce the cost of AI model calls. I’m not alone in this struggle, and I’m sure many of you have faced similar challenges.
To combat this, I’ve been exploring various strategies to minimize costs without sacrificing performance. Here are some ideas I’ve gathered so far:
## 1. Restrict Output with Pydantic
I’ve started using Pydantic to restrict output, which has helped reduce the number of LLM calls. This is a simple yet effective way to optimize your application.
## 2. Cache Previous Queries
Caching previous queries is another approach I’ve taken. This way, I can reuse previous results instead of calling the LLM model repeatedly.
## 3. Leverage MLFlow for Cost Tracking and Optimization
MLFlow has been a game-changer for tracking costs and optimizing prompts. If you haven’t tried it, I highly recommend it.
## 4. Explore Possible RAG Systems
I’m still exploring possible RAG (Retrieval-Augmented Generation) systems, which can help reduce the cost of LLM calls. If you have any experience with RAG systems, I’d love to hear about it.
## 5. Create Examples for Few-Shot Learning
Creating a few examples using LLMs and then using them for few-shot learning with transformers can help eradicate simple agents. This approach has shown promising results.
## 6. Fine-Tune Open-Source Models (When Possible)
While I don’t have the data to fine-tune an open-source model right now, I know it’s an effective way to reduce costs in the long run.
## 7. Plan for Long-Term App Development
Finally, I’ve realized that the key to reducing AI application costs is to plan for long-term development. By leveraging data and working on multiple LLM models, I can eventually eradicate the usage of LLMs and reduce costs.
If you have any additional ideas or experiences to share, I’d love to hear them. And if you’ve built a scalable AI app, let’s connect and learn from each other!