Cutting AI Costs Without Cutting Quality

The biggest waste in AI spend is using a flagship model for every task. Route simple jobs—classification, short summaries, formatting—to cheaper, faster models, and reserve large models for reasoning that genuinely needs them. A simple rule-based router that picks the model by task type often cuts costs 40-60% with no visible quality loss.

Cache aggressively and trim your tokens. Prompt caching reuses your system prompt and shared context instead of re-billing it on every call, and tightening verbose instructions shaves recurring input cost. For images and video, generate at draft resolution while iterating, then render the final high-resolution version only once you have approved the concept.

Measure before you optimize: log cost per request, tokens per call, and which model served each job, so you can see where the money actually goes. On B4AI you can compare multiple models side by side and switch to a cheaper one the moment its output is good enough, turning cost control into a routine rather than a cleanup.