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.
In plain words. Observability tells you the story of your spend. Crowkis edits the story so most of the expensive chapters never happen.
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.
flowchart TD Q1["'how do refunds work?'"] --> L1["model call · $$ · 2s"] Q2["'what's the refund window?'"] --> L2["model call · $$ · 2s"] Q3["'refund timeline?'"] --> L3["model call · $$ · 2s"] L1 --> A["the same answer, three times"] L2 --> A L3 --> A style A fill:#fbe9e8,stroke:#d62221,stroke-width:2.5px
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.