Comparison
Optimaq vs. observability (Helicone, Langfuse, LangSmith)
Helicone and Langfuse show you how much you spend and how your LLM calls behave; Optimaq takes that real traffic and decides which cheaper configuration keeps quality. It doesn’t replace your observability: it consumes it and adds the decision layer you do by hand today.
What observability solves
Helicone, Langfuse and LangSmith capture every call, compute cost and latency and give you traces to debug. They’re the source of truth for "what’s happening".
Where it falls short
They show the spend, but don’t answer what to do with it: drop a model? switch provider? tweak the prompt? what’s the risk? That decision is left to your team, by hand.
What Optimaq adds
It re-runs your real production traffic against cheaper or alternative configurations and returns a verdict: what to change, how much you save and the quality impact —certified by its real outcome in your product—, with a regression-risk estimate. If you already use Helicone or Langfuse, Optimaq stacks on top without replacing anything.
Looking for an "alternative to Helicone"?
If you want another observability tool, Langfuse, Helicone or LangSmith are comparable options. But if your real goal is to lower cost per request without losing quality, none solves it alone. Optimaq isn’t an observability alternative: it’s the decision layer on top.
FAQ
Do I have to drop Helicone/Langfuse to use Optimaq?
No. Optimaq consumes that traffic and adds the decision on top. You keep your current observability.
How does it integrate?
With one line of SDK (Python or Node), without rewriting code or touching production. OpenTelemetry-compatible.
See it on your own prompts — savings + proof that quality holds.
Try /audit →