As AI applications continue to grow in complexity and popularity, one major concern that many developers face is managing costs. Whether you’re building an agentic application or working on a scalable AI project, reducing the cost of Large Language Model (LLM) calls is crucial for long-term sustainability.
In this post, we’ll explore some strategies and ideas for reducing costs in AI applications, including restricting output with pydantic, caching previous queries, and fine-tuning open-source models.
## Restricting Output with Pydantic
Pydantic is a powerful tool for restricting output and reducing the number of LLM calls. By defining a clear schema for your output, you can ensure that only necessary data is returned, reducing the cost of each call.
## Caching Previous Queries
Caching previous queries is another effective way to reduce costs. By storing the results of previous queries, you can avoid making duplicate calls to the LLM, reducing the overall cost of your application.
## Fine-Tuning Open-Source Models
Fine-tuning open-source models is a great way to reduce costs in the long run. By leveraging existing models and fine-tuning them for your specific use case, you can reduce the number of LLM calls and minimize costs.
## Tracking Costs with MLflow
MLflow is a great tool for tracking costs and optimizing prompts. By integrating MLflow into your application, you can gain insights into your costs and identify areas for optimization.
## Exploring Possible RAG Systems
RAG (Retrieval-Augmented Generation) systems are a promising approach for reducing costs in AI applications. By exploring possible RAG systems and integrating them into your application, you can reduce the number of LLM calls and minimize costs.
## Creating Examples with LLMs and Few-Shot Learning
Creating examples with LLMs and using few-shot learning is another strategy for reducing costs. By creating a few examples with LLMs and using them for few-shot learning, you can reduce the number of LLM calls and minimize costs.
## Connecting with Others
Finally, connecting with others who have built scalable AI applications can be a great way to learn from their experiences and gain insights into reducing costs.
If you have any inputs or ideas on reducing costs in AI applications, we’d love to hear from you. Share your experiences and strategies in the comments below!