My AI Integration Cost Too Much Until I Changed My Approach
I loved my AI summarization feature until the bill arrived.
Last month, I built a tool to summarize long articles. I used GPT-4 with a simple prompt. It worked perfectly. Users loved the quality.
Then the bill came. One month of usage cost me over $1,200. I had to fix this or kill the feature.
I tried several things to fix it:
- I switched to GPT-3.5-turbo. The cost went down, but the quality dropped. The summaries became vague.
- I tried prompt engineering. Adding "be specific" did not help enough.
- I tried reducing input size using extractive libraries. This helped, but costs remained high.
I realized I was using a sledgehammer for a small nail.
The solution is a two-step pipeline. You combine two different methods to get the best results.
Step 1: The Extractive Phase Use a cheap, fast tool like TextRank to pick the top 5 to 10 sentences from the article. This removes 90% of the extra text.
Step 2: The Abstractive Phase Send only those few sentences to a small, cheap model like GPT-3.5-turbo. Ask it to rewrite those sentences into a clean 3-bullet summary.
This approach cut my costs by 80%. The quality stayed close to GPT-4 because the model only processed the most important data.
Other tips for your AI builds:
- Use caching. Store results by article hash so you do not pay for the same summary twice.
- Use layers. Break complex tasks into smaller, cheaper sub-tasks.
- Set a fallback. If an article is too complex, use a high-quality model like GPT-4 only for those specific cases.
Stop sending huge blocks of text to expensive models. Shrink the data first.
How do you balance AI quality and cost in your products? Do you use different models for different tasks?