My client recently saved over $10K on GPT-4 API Monthly cost.
To be clear, we didn’t have to cut corners or compromise the quality of the end results. After analyzing the usage patterns and implementing a simple strategic adjustment, the savings rolled in — much to our surprise. By the end of the month, a simple adjustment translated into more than $10K in savings.
You can do it too. This article will show you the exact strategy to efficiently make GPT-4 API calls without breaking the bank.
We will demonstrate it in a hypothetical example, as we can’t disclose the client’s private information.
Consider this hypothetical situation. Alex and Bella are developers solving a complex classification task using GPT-4 API to classify 30,000 documents monthly. Depending on your use case, the job can be summarization, QA, etc.
Now, let’s see how both Alex and Bella approached the problem.
Alex classifies 30,000 documents using GPT-4 API daily. He treats each classification task separately, so each call incurs its own overhead in terms of time and cost.
Here’s what Alex’s each call looks like:
Considering GPT-4 API costs $0.06 per 1000 tokens and his separate call has 275 tokens, he spends a whopping $14,580 monthly.
On the other hand, Bella followed an intelligent approach.
Similarly, Bella classifies 30,000 documents using GPT-4 API daily. However, Bella batches her classification queries into groups of 10 — considering 10 classification tasks fall into the context window of GPT-4. Instead of 30,000 separate classification calls, she only makes 3,000 batched calls daily.
Here’s what Bella’s batched calls look like: