Crowkis vs fine-tuning your way to cheaper inference
Fine-tuning a smaller model is a months-long bet on cheaper tokens. Caching is a five-minute bet on zero tokens. One of these compounds weekly.
The fine-tuning cost play goes: distill your workload onto a smaller model, accept slightly worse quality, pocket the per-token spread. Sometimes it's right! But tally the true invoice — data curation, training runs, eval suites, regression monitoring, re-tuning every time the base model or your product shifts. It's a standing engineering program whose savings cap out at the small model's price, which is still a price on every single call.
Caching attacks the same bill from a different axis: the repeated fraction of traffic stops costing anything at all. No training data, no quality trade — the cached answer is the good model's answer — and the 'program' is a docker pull. Savings start the first hour and grow as the corpus warms.
The upgrade is a workflow, not a leap of faith.
They compose, too, and in the right order: cache first, so you only consider distilling the genuinely novel residue; then Crowkis's Enterprise arbitrage router can send easy residual queries to your small model and hard ones upstream, with the quality bar enforced per intent.
The bottom line
Months of ML engineering or five minutes of deployment — start with the one that pays this week, then decide if you still need the other. Most teams find the residue too small to bother.