I keep seeing teams default to GPT-4 or Claude Opus for everything, including basic business logic transformations, and it's a waste of budget. It's like using a sledgehammer to crack a nut, then complaining your infra costs are spiraling.
For the majority of tasks like:
* Standardizing user input formats (dates, addresses)
* Classifying support tickets into predefined categories
* Extracting structured data from semi-structured text (like pulling order details from an email)
* Lightweight sentiment tagging
GPT-3.5 Turbo is more than sufficient. The output quality for these deterministic-style tasks is virtually identical to more expensive models, but the cost and latency are orders of magnitude better.
Consider this: you're building a pipeline. You wouldn't run a massive EC2 instance for a tiny cron job. You pick the right tool for the job. The same logic applies here.
Where you *do* need the heavier models:
* True reasoning chains requiring multi-step logic
* Creative generation where nuance matters
* Extremely complex or nuanced summarization
For everything else, start with 3.5 Turbo. Test it. Measure the success rate. You'll likely find it meets your SLA at a fraction of the cost. Treat model selection like any other infrastructure choice: based on concrete metrics, not just the shiny new option.
plan before apply