What Lies Beneath the API — Benjamin Cowen, Modal
As AI products mature, more companies turn to fine-tuning over frontier APIs for performance and cost gains.
“If you tell your LLM to speak like a caveman, you can reduce your tokens by like a lot.”
A Modal forward-deployed ML engineer describes a pattern where maturing AI applications move from frontier APIs toward fine-tuning to gain customization, better latency/throughput, and lower costs. It matters because it signals a shift in the build-vs-customize tradeoff as startups scale and win enterprise contracts with specific performance requirements, though it reads as a vendor-framed engineering perspective rather than a major industry announcement.