LangSmith if you are all in on LangChain, Langfuse if you want open-source self-hosting. Neither alone answers whether the agent did its job; that is where Prefactor sits on top of, or instead of, both.[1][3]
Pick LangSmith if you build on LangChain or LangGraph and want first-party agent detail; pick Langfuse for open-source tracing you can self-host, on any framework. Both stop short of a verdict on the whole agent session, which is the layer Prefactor adds on top of either.
| Decision factor | Langfuse | LangSmith |
|---|---|---|
| Ecosystem coupling | Framework-agnostic SDKs, no preferred framework | Works anywhere, strongest with LangChain and LangGraph |
| Open source / self-host | MIT-licensed core, self-hostable | Proprietary, self-hosted on enterprise plans only |
| Tracing depth | Strong traces, sessions, multi-modal logging | Deepest LangGraph agent detail, step-level debugging |
| Evals | LLM-as-judge evaluators, annotation queues, scores on traces | Datasets, experiments, LLM and heuristic evaluators, online evals |
| Cost attribution | Token and cost per trace and user, per-agent is yours to build | Token and cost per run, per-agent is yours to build |
| Pricing model | Open source free, cloud platform fee plus usage per unit | Per seat plus per trace, retention-based tiers |
| Direction risk | Acquired by ClickHouse in January 2026, roadmap questions in flight | Tied to LangChain's commercial trajectory and its ecosystem bets |
Langfuse is the strongest open-source option in LLM observability. The core platform, tracing, sessions, prompt management with versioning, cost analytics, evaluators, and annotation queues, is MIT-licensed and self-hostable, which makes it the default for teams with data-residency requirements or an aversion to per-trace metering. The SDKs are genuinely framework-neutral: LangChain, LlamaIndex, the OpenAI SDK, or raw HTTP all instrument cleanly, and OpenTelemetry compatibility keeps exit costs low.
Limits, fairly stated: the evaluation features are trace-centric, good for scoring individual generations, lighter for multi-step task verdicts and eval-gated deploys. Dashboarding is functional rather than deep, and anything organisation-specific, such as per-customer cost rollups or business KPI joins, means exporting the data and building it yourself. Since January 2026, Langfuse is part of ClickHouse; the team has said the open-source product continues, and the open questions are roadmap coupling to a ClickHouse-native architecture and how cloud and residency options evolve. That is a reason to keep your quality layer portable, not a reason to leave.
LangSmith is the commercial observability and evaluation platform from the LangChain team. Its defining advantage is first-party depth: if your agents are built on LangChain or LangGraph, instrumentation is automatic and the trace view understands your agent's structure, graph nodes, tool calls, and retries, rather than showing generic spans. The evaluation tooling (datasets, experiments, online evaluators) and the prompt playground are polished, and the hosted product removes operational burden.
The reality check on LangChain coupling: LangSmith does work without LangChain, through OpenTelemetry ingestion and standalone SDKs, but most of what makes it better than alternatives assumes LangChain primitives. Adopting it from outside that ecosystem buys a good but not exceptional tracer at proprietary pricing. It is not open source, self-hosting is gated to enterprise contracts, and per-seat-plus-per-trace pricing needs modelling before you commit high-volume agent traffic to it.
Published list prices as of June 2026, and both vendors revise pricing, so treat these as order-of-magnitude and verify on their pricing pages. LangSmith meters per trace, at a list price around $0.50 per 1,000 base traces on 14-day retention and several times that for extended retention, plus per-seat fees on paid plans. At 1M base traces a month that is roughly $500 a month in trace charges before seats and retention upgrades.
Langfuse Cloud charges a platform fee per tier plus tiered usage per ingested unit, and an agent session typically emits several units. Comparable volumes usually land in the low hundreds of dollars a month on the published tiers. Self-hosted Langfuse has no usage bill at all: you pay in infrastructure, since it runs on ClickHouse and Postgres, and in the engineering time to operate it, which is rarely zero. At small volume the difference is noise; at serious agent volume, per-trace metering compounds fastest and self-hosting is cheapest in cash and most expensive in attention.
We sell the layer this section describes. Read it with that in mind.
Both products answer what your LLM application did, and they answer it well. Neither is built to answer whether each agent did its job, at acceptable quality and cost, with proof you can show.
A verdict for the whole multi-step agent session rather than per-generation scores, alongside evals that run on the traces you already collect.
Customer and business feedback captured where it happens and tied to the agent that caused it.
Cost attributed per agent, per version, and per customer rather than per trace, the basis of per-agent analytics.
Quality over time in a record you can hand to a customer or a regulator. Prefactor ingests traces from Langfuse, LangSmith, or any OpenTelemetry source, so whichever tracer you choose, you keep it.
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.
Prefactor adds completion verdicts, evals, feedback, and per-agent cost on top of the traces you already collect, from Langfuse, LangSmith, or any OpenTelemetry source. Book a demo to see it on a fleet like yours.
Prefactor helps teams observe, evaluate, and improve their AI agents in production, across every framework and provider.