Langfuse vs LangSmith vs LangChain (2025): Which One Do You Actually Need?

Daya Shankar

daya-shankar

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If you’re googling “langfuse vs langsmith vs langchain”, you’re probably trying to make a concrete decision:

• What do I build my LLM app with?

• What do I use to debug, monitor, and evaluate it?

• Do I really need all three of these things?

Short answer: they’re not three competing tools. They sit at different layers of your stack:

• LangChain → framework for building LLM/agent apps

• LangSmith → hosted platform for tracing, observability, evaluation & deployment (from the LangChain team)

• Langfuse → open-source LLM engineering / observability platform (traces, evals, prompts, metrics), self-hostable or cloud

In this guide we’ll:

• Build a clear mental model of where each fits

• Compare features, hosting, data control, and lock-in

• Show how a single LangChain app looks with LangSmith vs Langfuse

• End with an opinionated decision tree you can use today

TL;DR – Quick Recommendations

If you’re in a hurry, here’s the punchline:

• Use LangChain to build your LLM or agent application. It’s the framework / library that wires together models, tools, memory, and workflows.

• Use LangSmith when:

  • You’re already all-in on LangChain/LangGraph

  • You want turnkey observability & evals closely integrated with that ecosystem

  • SaaS or enterprise self-hosting of a closed-source platform is fine for you

• Use Langfuse when:

  • You want open-source, self-hosted observability with full data control

  • You care about framework-agnostic traces & evals across LangChain, LangGraph, custom SDKs, etc.

  • You’re comfortable operating a service (Docker/K8s/VMs) or happy with their managed cloud

Very rough defaults:

• Small startup / MVP, heavy LangChain usage → LangChain + LangSmith

• Enterprise / regulated data, infra team available → LangChain + Langfuse (self-hosted)

• Polyglot stack (multiple frameworks / custom SDKs) → Langfuse as your main observability layer

• Obs-obsessed experimentation team → LangChain + LangSmith or LangChain + Langfuse, depending on SaaS vs OSS preference

We’ll unpack all of this in detail.

A Simple Mental Model: 3 Layers of the Same Stack

Before we dive into feature checklists, it helps to see where each tool lives in a typical LLM stack:

    [ Your Product / API ]
          │
          ▼
[ LLM Orchestration Framework ]
          └──>  LangChain (and/or LangGraph)
                    │
                    ▼
[ Observability / Evaluation / Prompt Ops ]
          ├──>  LangSmith  (SaaS / enterprise, closed source)
          └──>  Langfuse   (open-source, self-hosted or cloud)

• LangChain is the code you write – chains/agents, tool calls, RAG flows, etc.

• LangSmith and Langfuse are platforms your app sends data to:

  • They ingest traces, tokens, prompts, responses, latencies, tool calls to help you debug and improve your app.

So you’re not choosing one of the three. You’re choosing:

1. Your framework (LangChain vs alternatives), then

2. Your observability & eval layer (LangSmith vs Langfuse vs others)

What Is LangChain?

LangChain is an open-source framework for building LLM-powered applications and agents. It gives you:

• Building blocks: prompt templates, chains, tools, retrievers, memory, etc.

• Integrations: with models (OpenAI, local LLMs), vector DBs, APIs, and more

• Agent & workflow abstractions: via LangChain itself and LangGraph for more controllable agent workflows

The LangChain team positions the ecosystem as:

• Open-source frameworks → LangChain + LangGraph

• Agent engineering platform → LangSmith (observability, evaluation, deployment)

Key thing: LangChain is not your observability tool. It’s the orchestration layer that generates events (prompts, calls, failures) that you then send to LangSmith, Langfuse, or something similar.

What Is LangSmith?

LangSmith is a hosted platform (with enterprise self-hosting) built by the LangChain team to help you:

• Trace & debug LLM applications

• Evaluate prompts, chains, and agents (offline & online)

• Manage datasets (for evaluation)

• Gather human feedback & run annotation queues

• Monitor production apps with observability dashboards

Notably:

• It’s framework-agnostic – you can integrate it via Python/TS SDKs even without LangChain – but the tightest, smoothest integration is obviously with LangChain and LangGraph.

• It is a closed-source commercial product, although it can be self-hosted under an enterprise license.

From their own messaging, LangSmith is designed to be your “agent engineering platform” for observability, evaluation, and deployment on top of LangChain.

What Is Langfuse?

Langfuse is an open-source LLM engineering platform for:

• Observability & tracing (inputs, outputs, tool calls, retries, token counts, latencies, costs)

• Evaluation (quality metrics, offline & online eval workflows)

• Prompt management (versioning, history, playgrounds)

• Metrics & dashboards

• Annotations / feedback

If you want a hands-on walkthrough of these observability features, check out our detailed guide: Langfuse Tutorial: Complete LLM Observability Guide (2025).

Important differences vs LangSmith:

• It’s open source (MIT on GitHub) and can be self-hosted using Docker, Kubernetes, or VMs, using the same code as their cloud offering.

• It’s framework- and model-agnostic, and recent docs emphasize integration via OpenTelemetry-style tracing to work with a wide range of LLM stacks.

Think of Langfuse as the open-source, infra-friendly alternative that can sit under many frameworks and custom pipelines, not just LangChain.

Side-by-Side: Langfuse vs LangSmith vs LangChain

Here’s a high-level comparison to anchor everything:

At a Glance

Philosophies in One Sentence

• LangChain: “Use our open-source framework to build agents and LLM apps quickly.”

• LangSmith: “Use our platform to develop, debug, evaluate, and deploy those apps (especially if you use LangChain).”

• Langfuse: “Use our open-source platform to trace, evaluate, and manage prompts across any LLM stack, with self-hosting as a first-class option.”

How They Actually Work Together

Common patterns in the wild:

1. LangChain + LangSmith

   • Easiest “default” for teams already deep in LangChain.

   • Often used to ship agents quickly, with observability + evals turned on via environment variables and callbacks.

2. LangChain + Langfuse

   • Popular when teams want open-source + self-hosted observability, or are sensitive about sending traces to a SaaS.

3. Multi-framework stack + Langfuse

   • When you have a mix of LangChain, custom orchestration, maybe some other agents or in-house tooling, Langfuse acts as the central observability hub.

4. Hybrid: LangChain + LangSmith + Langfuse

   • Less common, but sometimes teams:

     • Use LangSmith to leverage tight integration + eval UI while

     • Also using Langfuse for open-source tracing in self-hosted environments

   • This is more advanced and usually for bigger orgs.

Hands-On: One LangChain App, Three Ways

To make this concrete, imagine a tiny LangChain app:

• User asks a question

• We call an LLM to answer it

(I’ll keep the code schematic and high-level — you’d plug in your actual models and secrets.)

1. Plain LangChain (No Observability)

from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate

model = ChatOpenAI(model="gpt-4.1-mini")
prompt = ChatPromptTemplate.from_messages(
    [
        ("system", "You are a helpful assistant."),
        ("human", "{question}"),
    ]
)
chain = prompt | model
response = chain.invoke({"question": "Explain RAG like I'm 12"})
print(response.content)

You get an answer, but:

• No trace of what happened

• No record of inputs, outputs, latencies, token usage, etc.

2. LangChain + LangSmith

With LangSmith, you typically:

• Set environment variables like LANGCHAIN_TRACING_V2 and LANGCHAIN_PROJECT

• Or configure the SDK directly

Then LangChain sends traces to LangSmith automatically.

Conceptual example:

import os
from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate

os.environ["LANGCHAIN_TRACING_V2"] = "true"
os.environ["LANGCHAIN_PROJECT"] = "rag-demo"
os.environ["LANGCHAIN_API_KEY"] = ""
model = ChatOpenAI(model="gpt-4.1-mini")
prompt = ChatPromptTemplate.from_messages(
    [
        ("system", "You are a helpful assistant."),
        ("human", "{question}"),
    ]
)
chain = prompt | model
response = chain.invoke({"question": "Explain RAG like I'm 12"})
print(response.content)

Now every call becomes a trace in LangSmith with:

• Prompt + variables

• Response

• Timings, tokens, costs (if configured)

• Any nested tool calls / retriever calls, etc.

From there you can:

• Create datasets and evaluation runs

• Add human feedback via annotation queues

• Compare experiments across different prompts or models

3. LangChain + Langfuse

With Langfuse, you typically:

• Install the SDK and/or use their LangChain callback integration

• Configure a Langfuse client with your keys / host

• Attach the callback handler to your chain or app

Docs show that Langfuse captures detailed traces (inputs, outputs, tokens, tool calls, etc.) and uses an OpenTelemetry-like model under the hood.

Conceptual example:

from langfuse import Langfuse  
from langfuse.callback import CallbackHandler  
from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate


lf = Langfuse(
    secret_key="",
    public_key="",
    host="https://cloud.langfuse.com" 
)
callback = CallbackHandler(langfuse=lf)
model = ChatOpenAI(model="gpt-4.1-mini", callbacks=[callback])
prompt = ChatPromptTemplate.from_messages(
    [
        ("system", "You are a helpful assistant."),
        ("human", "{question}"),
    ]
)
chain = prompt | model
response = chain.invoke({"question": "Explain RAG like I'm 12"}, config={"callbacks": [callback]})
print(response.content)

Now each call shows up in Langfuse with:

• A timeline of spans (prompt, model call, tools, retrievers…)

• Inputs/outputs, errors, retries

• Token usage, latencies, costs (if configured)

• The ability to run evaluations, track metrics, and play with prompts in their UI

The exact APIs evolve, but the idea doesn’t: you attach Langfuse callbacks, and your LangChain app becomes observable and evaluable.

How to Choose: A Practical Decision Tree

Let’s turn this into a concrete “what should I do on Monday?” guide.

Step 1 – Are You Using LangChain Already?

• If no

  • You can still use LangSmith (framework-agnostic SDK) or Langfuse (SDK & OpenTelemetry-style tracing) with whatever framework or custom code you have.

  • But this article assumes you’re at least considering LangChain, so the rest focuses there.

• If yes

  • Perfect. You’re in the sweet spot for both LangSmith and Langfuse.

Step 2 – Do You Need Observability Right Now?

• Just prototyping / hackathon level

  • Start with LangChain alone.

  • Add observability once prompts and flows start to matter (e.g., you’re shipping a pilot or handling real users).

• Building something used by real users / customers

  • You absolutely want tracing & evaluation from the start.

  • Choose at least one of LangSmith or Langfuse before you go to production.

Step 3 – Data Sensitivity & Compliance

Ask yourself:

“Am I okay sending conversational traces, prompts, and outputs to a third-party SaaS?”

• If yes (e.g., early-stage startup, no strict industry regs yet)

  • LangSmith SaaS is very attractive: deep integration, minimal setup, solid UI.

• If no (finance, healthcare, internal data, strong privacy posture)

  • Self-hosted Langfuse is a natural fit: same codebase as their cloud, under your own infra.

  • LangSmith can also be self-hosted, but as a closed-source enterprise product with licensing requirements.

Step 4 – Team & Infra Reality

Scenario A – Small dev team, no infra people

• Priority: “Ship quickly, don’t manage more infra than needed.”

• Recommendation:

  • LangChain + LangSmith SaaS

  • Add Langfuse later only if you hit data-control or framework-sprawl issues.

Scenario B – You already run K8s, Postgres, observability stacks

• Priority: “We control infra, want open source, comfortable self-hosting.”

• Recommendation:

  • LangChain + self-hosted Langfuse

  • Consider LangSmith only if its specific evaluation or deployment UX gives you major benefits.

Scenario C – Multi-framework / multi-language stack

• Priority: “We have several LLM apps built differently; want a unified obs layer.”

• Recommendation:

  • Make Langfuse your main observability and evaluation layer (SDKs across stacks, OpenTelemetry-style integration).

  • Use LangSmith only where you lean heavily on LangChain/LangGraph and want extra polish.

Step 5 – How Much Do You Care About Lock-In?

• Lock-in concerns low

  • Using a proprietary platform like LangSmith is often fine and can speed you up.

• Lock-in concerns high

  • Open-source Langfuse, with your own infra and direct access to your observability data, reduces long-term platform risk.

You can even log traces in a vendor-neutral way (e.g., structured events, OpenTelemetry) and feed them into Langfuse plus other observability tools, as some comparison articles highlight.

Recommended Stacks by Use Case

To make this even more concrete, here are “default” stacks for common situations.

1. Minimal Startup Stack (MVP / Seed Stage)

• Framework: LangChain (and maybe LangGraph as you get fancy)

• Observability: LangSmith SaaS

• Why:

  • Fastest to set up, you’re inside the same ecosystem

  • Great for quick iterations and A/B testing of prompts & models

Later, if compliance or vendor risk becomes an issue, you can backfill with Langfuse and migrate traces gradually.

2. Enterprise / Regulated Stack

• Framework: LangChain (or mix of frameworks)

• Observability: Self-hosted Langfuse (optionally complement with internal tools)

• Why:

  • You keep sensitive traces on your own infra

  • Open-source gives you control over schema, retention, export, etc.

If your org is fine with closed-source but wants on-prem, evaluating enterprise self-hosted LangSmith is also reasonable.

3. Polyglot, Experimentation-Heavy Team

• Framework: Multiple (LangChain, custom agent libs, maybe internal frameworks)

• Observability: Langfuse as the central LLM observability & evaluation plane

• Why:

  • Consistent traces and metrics across all apps

  • OpenTelemetry-style, framework-agnostic integration keeps your stack from fracturing.

You can still plug individual LangChain projects into LangSmith if the UX is valuable enough.

Common Misconceptions (FAQ)

“Is Langfuse a LangSmith alternative or do I use both?”

Both compete in the LLM observability & evaluation space, but they differ in:

• Open source vs closed source

• Self-hosting model and infra expectations

• Tight coupling to LangChain vs broader framework agnosticism

You can absolutely treat Langfuse as a LangSmith alternative, but some teams run both for different apps or phases.

“Can I self-host LangSmith?”

• Yes, but only under a paid enterprise license; LangSmith itself is not open source.

• If you want free, open-source self-hosting, Langfuse is the more straightforward option.

“Is LangChain competing with LangSmith or Langfuse?”

No.

• LangChain is a framework (code library) for building LLM apps.

• LangSmith and Langfuse are platforms for tracing, evaluation, and monitoring those apps.

You typically use LangChain + one (or both) of the platforms, not “choose between” all three.

“Which is cheaper in the long run?”

It depends on:

• Your scale (requests, tokens, projects)

• Whether you run self-hosted Langfuse on your own infra

• Your internal DevOps cost vs the convenience of managed SaaS

Rough intuition:

• For low to moderate scale, LangSmith SaaS may be cheaper and faster to adopt.

• As you scale, or if you already have strong infra, self-hosted Langfuse can become more cost-efficient and give you stronger data control, as several comparison posts highlight the trade-off between SaaS convenience and infra costs.

Always check current pricing and run your own TCO calculation.

Final Checklist: Future-Proofing Your LLM Stack

Before you pick Langfuse, LangSmith, or both, answer these five questions:

1. What’s my primary framework today?

   • Mostly LangChain → LangSmith is the easiest add-on.

   • Many frameworks / custom code → Langfuse gives you a more neutral observability plane.

2. How sensitive is my data?

   • Comfortable with SaaS → LangSmith cloud is fine.

   • Need strict data control → strongly consider self-hosted Langfuse (and/or enterprise self-hosted LangSmith).

3. Do I have infra capacity?

   • No → SaaS (LangSmith, or Langfuse Cloud) keeps things simple.

   • Yes → OSS + self-host (Langfuse) maximizes control and flexibility.

4. How much do I care about vendor lock-in?

   • Low → LangSmith is great; move fast.

   • High → Langfuse’s OSS & self-hosting are attractive.

5. What do I actually need in the next 3–6 months?

   • If the answer is “ship something reliable and learn,” the safe, pragmatic pattern is:

     • Start with LangChain + one platform

     • Bake observability & evals into your workflow early

     • Re-evaluate tools once you understand your traffic, team, and constraints better