How I Slashed LLM API Costs by 70% with Batching (No Magic)
📰 Dev.to AI
Reduce LLM API costs by 70% using batching to optimize requests
Action Steps
- Identify opportunities to batch similar requests to the LLM API
- Implement batching by grouping requests and sending them in a single API call
- Configure the batch size to balance cost savings and latency
- Test and monitor the batching approach to ensure it meets performance requirements
- Optimize the batching strategy based on the results of testing and monitoring
Who Needs to Know This
Developers and engineers working with LLM APIs can benefit from this approach to reduce costs and improve performance. This technique is particularly useful for teams handling large volumes of data or requests.
Key Insight
💡 Batching can significantly reduce LLM API costs by minimizing the number of requests and leveraging economies of scale
Share This
💡 Slash LLM API costs by 70% with batching! No magic, just optimization
Key Takeaways
Reduce LLM API costs by 70% using batching to optimize requests
Full Article
A few months ago, I was building a feature to automatically categorize thousands of customer support tickets. The obvious approach? Use an LLM to read each ticket and output a label. But when I started sending requests one by one to the API, the costs climbed fast and the latency made my dev server feel like a dial-up connection. I tried everything: compressing prompts, using smaller models, even switching to a regex-based solution (which worked for about 60% of cases, but the edge cas
DeepCamp AI