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]
What is the difference between Langfuse and Prefactor?
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.
Langfuse or Prefactor, in one table
| Decision factor | Langfuse | Prefactor |
|---|---|---|
| Where it fits | Tracing and analysing LLM calls | Knowing whether the agent did its job |
| What it evaluates | A call or trace, often against a dataset | The outcome of a production run, per agent |
| Evaluation style | Trace-attached evals you assemble and run | A quality score per run, out of the box |
| On drift | Shows the scores; you spot the trend | Flags drift against a baseline automatically |
| On a risky action | Records it after the fact | Holds or escalates it before it reaches a user |
| Use them together? | Trace and manage prompts with Langfuse | Evaluate outcomes with Prefactor |
What each one is for
- 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.
- 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.
Side by side, by lifecycle stage
| Capability | Langfuse | Prefactor |
|---|---|---|
| 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 | ||
| Evaluation | Trace-attached, you assemble it | Quality 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 agent | Per trace | ✓ |
| Audit trail for a decision | Partial | ✓ |
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.
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.
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.
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.
Langfuse records the action after the fact. Prefactor holds or escalates it for review before it reaches a user.
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.
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
- Langfuse homepage
- Langfuse documentation
- Langfuse is joining ClickHouse (announcement)Acquisition announced January 2026; referenced for the post-acquisition questions discussed on the page.
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.
Does Prefactor replace Langfuse?
Langfuse already has evals. How is Prefactor different?
Does Prefactor work with Langfuse traces?
Does the ClickHouse acquisition change this?
Do I still need evaluation if I have tracing?
Find out what to fix
Book a demo and we will evaluate a live agent on a fleet like yours: quality per run, drift after a change, and cost per agent and version.
Unified performance platform for agents, authentication, and risk management