PrefactorvsLangfuse

Langfuse shows you the trace. Prefactor tells you what to fix.

Langfuse scores a call; Prefactor knows whether the whole run worked, and holds a risky action for review before it reaches a user.[1][2]

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support-agent v4 · one run, two layersexample
Illustrative run, showing what each layer tells you
Langfusesees
fetch_customer212ms · 1.2k tok
apply_refund1.4s · 3.1k tok
send_reply340ms · 0.8k tok
trace recorded, no verdict
Prefactoradds
Did its job✓ yes
Quality84 / 100
Cost$0.42 · in budget
Drift vs baselinenone
✓ a run that breaches its schema is held for review
§01 / THE SHORT ANSWERtl;dr: which, and when
TL;DR

Langfuse gives you open-source tracing, cost analytics, and evals you attach to traces yourself. Prefactor delivers a quality score per production run out of the box, flags drift against a baseline, and can hold an action for review. Trace with Langfuse, then add Prefactor when agents act for real users.

The short answer

Langfuse or Prefactor, in one table

Decision factorLangfusePrefactor
Where it fitsTracing and analysing LLM callsKnowing whether the agent did its job
What it evaluatesA call or trace, often against a datasetThe outcome of a production run, per agent
Evaluation styleTrace-attached evals you assemble and runA quality score per run, out of the box
On driftShows the scores; you spot the trendFlags drift against a baseline automatically
On a risky actionRecords it after the factHolds or escalates it before it reaches a user
Use them together?Trace and manage prompts with LangfuseEvaluate outcomes with Prefactor
§02 / HONEST CONTRASTscope: different jobs
Honest contrast

What each one is for

What Langfuse does well
  • Open-source tracing: detailed traces of LLM calls with self-hosting, so inputs, outputs, latency, and metadata stay under your control.
  • Cost analytics: token usage and cost tracked per trace and per user across an application.
  • Prompt management: version, deploy, and manage prompts from a built-in registry.
  • Trace-attached evals: model-based and human scoring with annotation queues, tracked over time.
  • User feedback: capture end-user feedback and tie it back to the trace that produced it.
  • Framework-agnostic: works with any model provider and agent framework through SDKs and an API.

Best for engineering teams that want open-source LLM tracing with cost tracking, prompt tooling, and data ownership.

What Prefactor does
  • Evaluates each run for outcome quality, cost, and whether the agent stayed in its approved scope.
  • A quality score per agent tracked across versions, so a regression shows up as a trend rather than a surprise.
  • Drift detection when behaviour shifts after a model update or a prompt edit, before a user hits it.
  • Holds or escalates a risky action for review before it reaches a user, not after the spend.
  • One record across frameworks: agents on any framework judged from the same place, with an audit trail per decision.

Best for teams running agents in production who need to know each one is doing its job, and prove it.

§03 / CAPABILITY MATRIXside by side: what each covers
Side by side

Side by side, by lifecycle stage

CapabilityLangfusePrefactor
Tracing and analytics
LLM call tracing✓—
Cost tracking per trace and user✓✓
Prompt management and versioning✓—
Open source and self-hosted✓—
Framework-agnostic✓✓
Evaluating agents in production
EvaluationTrace-attached, you assemble itQuality score per run, built in
Quality score per agent across versions—✓
Drift detection against a baseline—✓
Hold or escalate a risky action—✓
Across your stack
One queryable record per agentPer trace✓
Audit trail for a decisionPartial✓
§04 / THE QUALITY GAPour take: where it stops
Our take

Where Langfuse stops: whether the agent did its job

We sell the layer this section describes. Read it with that in mind.

Langfuse traces LLM calls, tracks their cost, and lets you attach evaluation scores to a trace. What it leaves to you is whether each agent did its job across a whole multi-step run, at acceptable quality and cost.

01
Evals you assemble yourself

Langfuse gives you the scoring primitives; you build the evals, run them, and read the results yourself. Prefactor delivers a quality score per run, judged against the agent's goal, out of the box.

02
Scores per trace, not per agent

Trace-attached scores describe a call. Prefactor tracks quality per agent across versions, so a regression shows up as a trend rather than a surprise.

03
Drift is yours to spot

The dashboards show the scores; noticing the shift after a prompt edit or model update is on you. Prefactor flags drift against a baseline before a user hits it.

04
No hold on risky actions

Langfuse records the action after the fact. Prefactor holds or escalates it for review before it reaches a user.

See it on your own agents

A working session on a fleet like yours: watch a run evaluated, catch a drift, walk the record.

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§05 / WHICH TO PICKdecide: by your stack
Which to pick

Which one fits

Stay with Langfuse alone if

  • Self-hosted, open-source tracing is a hard requirement.
  • Your workload is single-shot LLM calls rather than multi-step agents.
  • Prompt management and trace-level scores cover what you need today.

Add Prefactor when

  • Agents take multi-step actions for real users.
  • You need a quality score per agent, not a score attached to each trace.
  • A regression after a prompt or model change has to surface before a user hits it.
  • Someone asks you to prove an agent behaved.
§06 / HOW WE REVIEWEDsources: checked June 13, 2026
Methodology

How we reviewed this comparison

Reviewed against public product and documentation pages on June 13, 2026. If a vendor has changed a feature, product name, or positioning since then, send a correction and we will update it. Numbered source links in the page body point to the ordered sources below.

Sources reviewed

  1. Langfuse homepage
  2. Langfuse documentation
  3. Langfuse is joining ClickHouse (announcement)Acquisition announced January 2026; referenced for the post-acquisition questions discussed on the page.
Prefactor context

Methodology

  • Reviewed public product, documentation, and launch material visible at the time of writing.
  • Mapped each page to the primary buyer, control layer, and runtime capabilities each vendor describes publicly.
  • Prefer direct product and documentation pages over analyst summaries or reseller material.
§07 / QUESTIONSfaq: the common ones
Questions
Does Prefactor replace Langfuse?
No. Langfuse traces and scores LLM calls; Prefactor watches the whole production run and tells you whether the agent did its job. Many teams trace with Langfuse and evaluate outcomes with Prefactor, so they sit at different stages of the same lifecycle.
Langfuse already has evals. How is Prefactor different?
Langfuse gives you trace-attached evals that you assemble and run, often against a dataset. Prefactor delivers a quality score per production run, judged against the agent's goal, tracks it per agent across versions, and detects drift against a baseline, then holds a risky action before it reaches a user.
Does Prefactor work with Langfuse traces?
Yes. Prefactor reads the traces an agent already emits, through a native SDK, the core SDK, or OpenTelemetry ingest, and evaluates each run. There is no rebuild and no gateway in the request path.
Does the ClickHouse acquisition change this?
Langfuse announced in January 2026 that it was joining ClickHouse, and the open-source product continues. The open questions are about direction: how far the roadmap couples to ClickHouse-native architecture, and how self-hosting and data residency evolve. Keeping your quality scores and records independent of any single tracing backend is the practical hedge.
Do I still need evaluation if I have tracing?
Yes. Tracing records what an agent did; evaluation tells you whether it did its job. A hallucinated answer traces identically to a correct one, so the trace alone does not settle which you got.
Reviewed against public sources on June 13, 2026Suggest a correction

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