TL;DR Route narrow, testable prompts to a small model first, fall back to a large model...
TL;DR
Route narrow, testable prompts to a small model first, fall back to a large model only on an explicit quality signal, and move delay-tolerant work into a batch lane. The cheapest architecture for a SaaS app is the one that minimizes cost per accepted result while still meeting its latency, quality, and US or EU data-handling SLOs.
Treat every route as an operational policy, not a clever prompt.
Measure accepted work.
How should a SaaS app reduce its LLM API bill with prompt routing?
Most teams I talk to run every LLM call through one frontier model, then act surprised when the...
Prompt bloat slows LLM apps, raises costs, and hurts quality. Learn the root causes and how context engineering with Redis can…
Short answer: don't choose an LLM API for a SaaS app from a public token-price table alone; replay a...
Why LLM cost optimization should start with auditable call groups and task-specific evaluation.
Token economics, cache_control prompt caching, the Batches API, and cheap-to-strong model routing — controlling LLM spend in Java…
Token economics, cache_control prompt caching, the Batches API, and cheap-to-strong model routing — controlling LLM spend in…
