Fine-tuning is back in 2026, but now it's a cost-engineering play

微调回来了，但理由变了：2026 LLM Fine-tuning 从能力增强到成本工程

Engineering teams in 2026 are fine-tuning again—not to make models smarter, but to slash inference costs on high-volume narrow tasks. FermiSense fine-tuned Qwen3.5-9B for e-commerce review, cutting cost from tens of dollars to $0.50 per 1k calls. Intercom's customer-support small model hit 73.1% resolution rate at one-fifth the cost of GPT-5.4. On the vision side, a DINOv3 classifier workflow trained a zero-API-cost local classifier with only 839 reviewed samples, reaching AP 0.9731. The article provides a decision matrix: fine-tuning pays off above ~50k daily requests with automatically verifiable outputs; below that, Prompt Caching plus RAG is the better bet. Most vendor-reported high scores lack third-party reproducible test sets, so hybrid routing remains the pragmatic middle ground.

Why it matters: A well-argued engineering trend piece with concrete numbers from FermiSense, Intercom, and a DINOv3 classifier workflow. It earns featured by making a clear, counterintuitive case backed by data. Held at 78 rather than higher because it's a synthesis/observation piece, not a f...