PromptUnit

PromptUnit pitches itself as the simplest way to slash your LLM bill: change one URL and let an engine pick the cheapest adequate model for each call. That promise holds up well in practice, especially for diversified traffic patterns. We've seen the 14-day observation mode be a decisive trust-builder—you see the numbers before any routing or charge. The x-promptunit-feature header for per-feature cost attribution is genuinely useful; most proxies don't offer that granularity. When to pick this: You're already using multiple LLM providers or a premium model like GPT-4o for everything, your monthly AI spend is north of $500, and you want a zero-effort cost reduction without retooling your codebase. The quality regression alerts give real peace of mind—you're warned before a cheaper model degrades your output. When to pass: If you need on-premise deployment (PromptUnit is a cloud proxy), if your latency tolerance is below 10ms (median adds 41ms), or if you use only one cheap model with no cost pressure—the savings will be negligible. Also not for teams requiring advanced prompt engineering or LLM evaluation features beyond quality scoring. Compared to Helicone (observability) or Log10 (evaluation), PromptUnit is narrower but more aggressive on cost. Helicone gives you per-call tracing but doesn't automatically route; PromptUnit does. The lack of a flat subscription is a differentiator—you pay only when you save. One caveat: the 20% fee feels fair when savings are large, but on smaller bills it might sting proportionally. Still, the zero-risk trial makes it easy to evaluate. Real-world usage notes: the GitHub Action for CI/CD integration is handy for cost-cap enforcement in pipelines. The anomaly detection for cost spikes and encrypted key storage show attention to