spaturzu vs Langfuse
Langfuse is an LLM engineering platform for tracing, prompt management, and evaluations — built to make a model's answers correct, with a first-class agent observation model. spaturzu answers a different question: which agent spent the money, and how do we keep it under a hard cap — with no proxy in the request path and no prompt content ever leaving your servers.
Side by side
| Capability | spaturzu | Langfuse |
|---|---|---|
| Primary job | Per-agent cost + budgets + fallback | LLM eng. platform (traces, prompts, evals) |
| Request path | In-process SDK; calls go direct to provider | Async SDK observer; never in the request path |
| Prompts stored on tool's backend | Never — only token counts + cost | Yes by default; opt-outs (SDK mask function, server-side masking) |
| Agent attribution model | run() frames; agentPath propagates to nested calls — designed for cost rollup | First-class agent observation type, auto-detected for many frameworks |
| Budget cap before the call | Typed BudgetExceededError, in-process, no proxy | Not in scope — Langfuse is not in the request path |
| Cross-provider fallback | In-process, explicit pairwise translators across 5 providers (20 directional pairs) | Not in scope — docs recommend pairing with a separate gateway |
| Full prompt/response inspection | Not in scope, by design | Core feature |
| Prompt mgmt, evals, datasets | Not in scope | First-class: prompt mgmt, LLM-as-judge + human + custom evals, datasets |
Reflects publicly documented behaviour as of May 2026.
Which should you choose?
Choose spaturzu when…
- →The question you need answered is which agent spent the money — cost rollups per agent and run, not eval scores.
- →You want budget caps enforced before the call; Langfuse is an async observer and not in the request path.
- →You want token counts and cost to leave your process — never the prompt or response text itself.
Choose Langfuse when…
- →Your core problem is output quality: you need traces, prompt management, datasets, and LLM-as-judge or human evals.
- →You want rich, auto-detected agent traces across popular frameworks for debugging, not primarily for cost.
- →You're fine storing prompt and response content (with optional masking) on the tool's backend.
Can I use both?
They're complementary, not overlapping: Langfuse for quality (evals, prompts, traces) and spaturzu for cost attribution and hard budget caps. Langfuse's docs even recommend pairing it with a separate gateway for routing — spaturzu sits in that cost-and-control gap without proxying your traffic.
Questions
Is spaturzu a Langfuse alternative?
For the cost question — which agent and run spent what — spaturzu is a focused alternative, and it adds budget caps Langfuse doesn't enforce. For prompt management, datasets, and evals, Langfuse is the deeper tool; many teams run both.
Does spaturzu do tracing and evals like Langfuse?
No, and by design. spaturzu is about cost attribution and control, not output quality. It records token counts, cost, agents, and runs — not prompt management, datasets, or LLM-as-judge evaluations.
Can spaturzu enforce a budget cap? Langfuse can't.
Yes. spaturzu can throw a typed BudgetExceededError in-process, before the provider call, when a hard-cap budget is breached. Langfuse is an async observer that never sits in the request path, so cost enforcement is out of its scope.
Can I run spaturzu and Langfuse together?
Yes — they're complementary. Use Langfuse for quality (evals, prompts, traces) and spaturzu for per-agent cost and budget caps. Langfuse's own docs suggest pairing it with a separate gateway for routing; spaturzu fills the cost-and-control gap without proxying traffic.
These claims reflect publicly documented Langfuse behaviour as of May 2026. Spot a mistake? Let us know and we'll fix it.
See which agent spent the money.
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Also compare: spaturzu vs Helicone.
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