The Model Overspend Problem
GPT-4o costs $2.50 per 1M input tokens and $10.00 per 1M output tokens. GPT-4o-mini costs $0.15 and $0.60 respectively — that’s a 94% reduction. Yet most teams route all traffic through GPT-4o because it’s the default and “just works.”
The result? Engineering teams overspend by 30–50% on AI API costs every month.
Identifying Swap Candidates
Not every API call needs the most powerful model. Three categories of tasks are prime swap candidates: classification and labelling (sentiment analysis, content moderation, intent detection), data extraction (parsing structured data from unstructured text, entity recognition), and summarisation (condensing documents, generating abstracts, creating TL;DRs).
These tasks typically achieve 95%+ accuracy with smaller, cheaper models.
The 95% Quality Rule
CostLayer analyses your API usage and identifies calls where a cheaper model produces output that’s at least 95% as good as the expensive model. This threshold ensures quality remains high while costs drop significantly.
Expected Savings
Teams using CostLayer’s model swap recommendations typically see 30–50% reduction in their monthly AI API bill. For a team spending $5,000/month on AI APIs, that’s $1,500–$2,500 in monthly savings.