Crowkis vs LangSmith: tracing the waste vs deleting it
LangSmith shows you every span of every chain, beautifully. The spans are still billed. There's a component whose job is making the spans not happen.
LangSmith is a polished developer experience: traces, evals, prompt playgrounds, a microscope for LLM applications, priced per seat and per trace at unicorn-company rates. Use it to debug; it's good at that. But a microscope examines the waste in exquisite detail without removing a token of it. Every repeated query it traces was still purchased.
There's also the platform gravity to weigh: deep LangSmith adoption couples your observability to one framework's ecosystem and one vendor's cloud, with traffic metadata leaving your network as a feature. Reasonable trade for some teams; involuntary for none, ideally.
- 1'how do refunds work?'
- 2model call · $$ · 2s
- 3'what's the refund window?'
- 4model call · $$ · 2s
- 5'refund timeline?'
- 6model call · $$ · 2s
- 7the same answer, three times
Every paraphrase is a fresh bill, unless the cache understands meaning.
Crowkis approaches the same traffic from the savings side. The live verdict feed is observability with consequences: each line is a hit that cost nothing, a refusal that protected a user, or a miss that, uniquely, was worth paying for. Top-miss analytics double as a to-do list. And it exports Prometheus and OTel, so your existing dashboards inherit everything.
The bottom line
Trace with whatever delights you. But the chart your CFO wants isn't 'requests observed', it's 'requests eliminated.' Only a cache draws that one.