Zambo vs Langfuse and Helicone for proving AI work to clients.
Langfuse and Helicone are observability tools: they trace, evaluate, and cost-track your LLM calls for your own team, and they do that job well. Review the receipt and actual deliverable before accepting a handover.
The comparison is fair only if each tool is judged on its own job. Langfuse gives engineering teams traces, prompt management, and evaluations, with an open-source core many teams self-host. Helicone gives developers a fast proxy view of LLM traffic: costs, latency, and request logs with minimal setup. If your problem is understanding and improving your own calls, both are serious tools, and this page's advice is to use them.
Client proof fails on observability data for reasons of audience, not quality. The record lives in your workspace. It narrates calls, not deliverables. It contains your internals, which you may not want to hand over. And it records no act by the client: no acceptance, no decision, nothing that ends a dispute. The handover inverts each property: one link, the job as delivered, source states shown, receipts recomputable at a public page, and the client's decision on the same object.
The practical stack for a client-facing team is therefore both layers: observability for yourselves while the work runs, the verifiable record for the delivery. The decision rule mirrors the LangSmith comparison in this cluster because the underlying split is the same: tools that face your team versus records that face your checker.
Table: observability vs client proof
Criterion | Langfuse | Helicone | Zambo. Primary job | Traces, evals, prompts | LLM cost and traffic view | Proving finished work. Primary reader | Your engineers | Your engineers | Your client. Client opens it with no account | No | No | Yes. Record of the deliverable as delivered | Calls, not jobs | Requests, not jobs | Steps of the job. Third-party recompute | No | No | Yes, public verify. Client acceptance recorded | No | No | Yes. Keep the observability you have; add the proof layer where work leaves the building.
Worked example
The proof side, opened by a non-engineer: https://zambo.dev/handover/508a46e2-8d6e-4099-bd12-7ae461c72c55 shows a four-step job addressed to its recipient, readable without any observability account. That is the artifact a client-facing team sends; the traces stay home where they belong.
Frequently asked questions
Can I export observability data and call it proof?
You can send it, and it supports your account. Proof in the strong sense adds two properties exports lack: the record was fixed at execution in a format a third party can recompute, and the client's decision lands on the same object. Exports persuade; the record settles.
We self-host Langfuse for data control. Does Zambo conflict with that?
No. The layers are independent: your traces stay in your infrastructure, and the delivery record lives at the handover link you choose to share, containing the job's steps rather than your internal telemetry.
Which should a solo freelancer buy first?
The proof layer, because your bottleneck is client trust and payment, not call analytics. Add observability when you are operating agents at a scale where debugging and cost per call are real problems.
By Brennan Zambo, founder of Zambo.
Last verified: 2026-10-17.
Try a handover
A handover gives your client one proof link with verifiable receipts to review, then Accept or Flag.
try it: say 'use zambo to hand this over to <client>' in your ai