Langfuse vs LangSmith: Full Comparison

If you're building LLM-powered applications in 2026, observability is no longer optional. Production LLM systems behave unpredictably. Prompts that work perfectly in testing fail silently in production. Agent workflows take unexpected paths. Costs spiral without warning. You need visibility into what's happening inside your application.

Langfuse and LangSmith have grown as the two leading platforms for LLM observability, evaluation, and debugging. Both solve similar problems, but they approach them differently and serve different developer workflows. 

Let’s break down what each tool does well, where each falls short, and which one is the better choice for your team.

What is Langfuse?

Langfuse is an open-source LLM engineering platform built for teams that want full control over their observability stack. The company graduated from Y Combinator (W23) and has grown to over 19,000 GitHub stars with an active community on Discord and GitHub Discussions.

The platform provides tracing, prompt management, evaluation workflows, and analytics. You can self-host the entire system using Docker or Kubernetes at no cost, or use their managed cloud offering. Langfuse is framework-agnostic by design. It works with LangChain, LlamaIndex, OpenAI SDKs, Anthropic, and dozens of other integrations through native SDKs and OpenTelemetry support.

Typical users include AI/ML teams at startups and enterprises who need production-grade observability without vendor lock-in. Its MIT-licensed open-source model works well for teams that want control over their data or need to adapt the platform to their workflows.

What is LangSmith?

LangSmith is the commercial observability and evaluation platform built by the LangChain team. It provides tracing, debugging, prompt management, dataset creation, and evaluation tools designed specifically for LLM applications.

The platform integrates deeply with LangChain and LangGraph. If you're already using these frameworks, enabling tracing requires just one environment variable. LangSmith understands LangChain's internals and surfaces debugging views that make sense for chain and agent workflows.

It is primarily a hosted SaaS product, though enterprise customers can self-host. According to LangChain's data, 84.3% of LangSmith users work with LangChain frameworks and 84.7% use Python.

Quick Setup Comparison

Both platforms offer straightforward setup for OpenAI tracing. Here's how they compare:

Langfuse uses a decorator-based approach with an OpenAI wrapper:

from langfuse import observe
from langfuse.openai import openai  # Drop-in replacement

@observe()
def story():
    return openai.chat.completions.create(
        model="gpt-4o",
        messages=[{"role": "user", "content": "What is Langfuse?"}],
    ).choices[0].message.content

LangSmith offers a similar wrapper pattern:

from langsmith.wrappers import wrap_openai
import openai

client = wrap_openai(openai.Client())
client.chat.completions.create(
    messages=[{"role": "user", "content": "Hello, world"}],
    model="gpt-4o"
)

Both approaches require minimal code changes. The key difference: Langfuse's @observe() decorator traces any Python function regardless of framework, while LangSmith's @traceable decorator works similarly but shines brightest with LangChain components where tracing becomes fully automatic.

Langfuse vs LangSmith: Feature-by-Feature Comparison

Both platforms address observability and evaluation for LLM applications, but they model data and workflows differently. The differences matter once applications grow beyond simple prompt calls and move into multi-step or production environments.

1. Tracing Depth and Structure

Tracing is the basis of LLM observability. It allows you to inspect prompts, model calls, tool usage, retrieval steps, and agent behavior across a request.

Area	Langfuse	LangSmith
Trace structure	Traces, observations, sessions	Run tree
Nesting support	Yes	Yes
Framework dependency	Framework-agnostic	LangChain-centric
Metadata flexibility	Configurable	Predefined with extensions

Langfuse:

Langfuse structures observability around three main entities:

• Traces, which represent a single request or operation

• Observations, which are the individual steps inside a trace

• Sessions, which group related traces, such as a user conversation

Observations can represent LLM generations, custom spans, or discrete events. The system supports nested observations, which is useful for multi-agent workflows or pipelines with multiple retrieval and processing stages.

Each observation records inputs, outputs, latency, token usage, and optional metadata. A timeline view shows execution order and duration. Traces can be filtered by environment, user identifier, session, or custom tags.

Langfuse builds on OpenTelemetry, which allows traces from different systems or frameworks to appear in the same view, provided they are instrumented accordingly.

LangSmith:

LangSmith uses a run-based model that aligns with LangChain’s execution structure. Each step in a chain or agent becomes a run, and runs are organized into a parent-child tree.

A parent run typically represents an entire chain or agent execution, while child runs correspond to prompt formatting, tool calls, LLM calls, and output parsing. Inputs, outputs, token usage, cost, timing, and errors are recorded for each run.

For LangChain-based applications, tracing is largely automatic. The UI allows inspection of each run in the tree. When an evaluation identifies an issue, the associated run can be opened directly for inspection.

2. Real-Time Monitoring and Alerting

Production LLM applications need monitoring beyond simple logging. You want to catch performance degradation, cost anomalies, and quality issues before users complain.

Langfuse provides dashboards for tracking latency, cost, token usage, and error rates over time. You can segment metrics by environment, user cohort, model, or custom attributes. The platform supports score aggregation for quality monitoring from LLM-as-judge evaluators, user feedback, or manual labeling.

Langfuse's native alerting capabilities are more limited compared to dedicated monitoring tools. Teams often export metrics to external systems like Datadog or Grafana for alerting workflows. The OpenTelemetry foundation makes this integration straightforward.

LangSmith provides live dashboards for cost, latency, and response quality with built-in alerting when issues occur. You can drill into root causes directly from alerts. The "Threads" feature clusters similar conversations, helping you understand user patterns and identify systemic issues rather than one-off failures.

3. Evaluation Workflows (Online and Offline)

Evaluation helps determine whether application outputs meet defined expectations.

Offline evaluation runs the application against fixed datasets. Online evaluation attaches scores to live production traffic.

Langfuse provides flexible evaluation infrastructure supporting LLM-as-a-judge, user feedback collection, manual labeling, and custom evaluation pipelines through APIs and SDKs. For offline evaluation, you create datasets and run your application against them. Online evaluation attaches scores to production traces.

Langfuse integrates with evaluation frameworks like Ragas for RAG-specific metrics. You can track retrieval relevance, answer faithfulness, and context completeness for retrieval-augmented generation pipelines. The platform recently added tracing for LLM-as-judge executions, letting you debug why evaluators give particular scores.

LangSmith provides comprehensive evaluation infrastructure for both workflows. The platform supports multiple evaluator types: human review through annotation queues, heuristic checks (like "is the response empty?"), LLM-as-judge scoring against custom rubrics, and pairwise comparisons between outputs.

A key workflow connects evaluation to improvement: add failing production traces to your dataset, create targeted evaluators, validate fixes with offline experiments, and redeploy. This flywheel accelerates iteration cycles significantly.

4. Prompt Engineering and Playground Tools

Prompt engineering requires rapid iteration. You need to test variants, compare outputs, and version successful prompts without touching production code.

Langfuse includes prompt management for centrally managing and version-controlling prompts. Strong caching on server and client side means you can iterate on prompts without adding latency to your application. The LLM Playground tests prompts against different models and configurations. When you spot bad results in traces, you can jump to the playground to iterate directly.

LangSmith's Prompt Hub stores versioned prompts with Git-like commit hashes. You can organize prompts with tags, push updates from code, and pull specific versions into applications. The Playground supports side-by-side comparison of prompts and configurations. Prompt Canvas uses AI assistance for optimization. 

The Playground integrates directly with evaluation, so you can test prompts against datasets and see scores before committing changes to production.

Integration Capabilities

Integration flexibility matters once an LLM application moves beyond a single framework or provider. The two platforms take different approaches here, which affects how easily they fit into existing stacks.

Langfuse Integrations

Langfuse takes a broad, framework-agnostic approach. According to their GitHub, supported integrations include:

• Agent Frameworks: LangChain, LlamaIndex, Haystack, AutoGen, CrewAI, DSPy, Mastra, smolagents, Flowise, Langflow

• Model Providers: OpenAI, Anthropic, Amazon Bedrock, Ollama, LiteLLM (supporting 100+ LLMs including Azure, Cohere, Sagemaker, HuggingFace, Replicate)

• Developer Tools: Vercel AI SDK, Instructor, Dify, OpenWebUI, Gradio, LobeChat, Vapi

The OpenTelemetry foundation means any OTEL-instrumented library flows into the same traces. You can send data to multiple destinations simultaneously.

LangSmith Integrations

LangSmith is designed primarily for the LangChain ecosystem. LangChain and LangGraph applications can enable tracing with minimal setup, often via a single environment variable. The SDK supports Python and JavaScript or TypeScript with typed interfaces.

Beyond LangChain, LangSmith works with any framework through its REST API and the wrap_openai wrapper for OpenAI calls. The tradeoff is clear: LangSmith provides the best experience for LangChain users but requires more manual instrumentation for other frameworks.

Which Tool Suits Your Needs?

The choice between Langfuse and LangSmith depends on your stack, workflow, and operational requirements. Both are capable, but each has contexts where it fits more naturally.

When to Choose Langfuse

• Self-hosting requirements: The MIT-licensed open-source version runs free on your infrastructure using Docker Compose, Kubernetes (Helm), or Terraform templates for AWS/Azure/GCP.

• Multiple frameworks: If your stack includes LangChain, LlamaIndex, and custom code, Langfuse's framework-agnostic approach provides unified observability.

• Cost-consciousness at scale: Usage-based pricing without per-seat fees. Self-hosting eliminates observability costs entirely.

• Custom workflows: The comprehensive API and OpenAPI spec let you build bespoke LLMOps pipelines.

When to Choose LangSmith

• LangChain-native development: If LangChain or LangGraph is your primary framework, LangSmith provides the deepest integration and best debugging experience.

• Managed experience: LangSmith's UI is polished and works immediately for LangChain workflows.

• Strong evaluation infrastructure: Comprehensive workflows connecting tracing, datasets, and improvement cycles.

• Enterprise requirements: SOC 2 Type II, HIPAA support, SSO, and self-hosting on enterprise plans.

Pricing Breakdown

Both platforms offer free and paid tiers. Langfuse supports self-hosting and usage-based scaling, while LangSmith is seat-based with extra costs for high-volume traces. Enterprise plans for both include self-hosting and SSO.

Langfuse Pricing

Tier	Cost	Included
Hobby	Free	50k units/month, 30-day retention, 2 users
Core	$29/month	100k units/month ($8/100k additional), 90-day retention, unlimited users
Pro	$199/month	500k units/month, unlimited history, enterprise SSO
Self-hosted	Free	Full platform on your infrastructure (MIT license)

Discounts available for early-stage startups (50% off first year), education (up to 100% off), and open-source projects ($300 credits/month).

LangSmith Pricing

Tier	Cost	Included
Developer	Free	1 seat, 5k traces/month, 14-day retention
Plus	$39/user/month	Up to 10 seats, 10k traces/month, email support
Enterprise	Custom	Unlimited users, self-hosting, SSO, dedicated support

Additional trace costs: $0.50 per 1k base traces (14-day retention), $5 per 1k extended traces (400-day retention). Traces with feedback automatically upgrade to extended retention. A 10-person team on Plus pays $390/month before trace costs.

Which One Should You Use?

The choice depends on your stack, requirements, and preference for open-source versus managed services.

Choose Langfuse if you value open-source flexibility, need self-hosting capabilities, work with multiple frameworks, or want to avoid per-seat pricing at scale. The platform is production-ready with strong community support and active development.

Choose LangSmith if you're building primarily with LangChain or LangGraph and want native integration. The evaluation workflows are comprehensive, and the debugging experience for LangChain applications is excellent.

Both offer free tiers. Run them in parallel during development to see which workflow fits your team. For non-negotiable data control, Langfuse's self-hosted option provides full sovereignty. For minimal operational overhead with deep framework integration, LangSmith delivers.

Getting Started

Both Langfuse and LangSmith help you observe, evaluate, and improve LLM applications, but they suit different workflows. Langfuse is flexible, framework-agnostic, and works well for self-hosted or custom stacks. 

LangSmith offers a managed experience tightly integrated with LangChain, with ready-to-use dashboards and evaluation tools. Which one you pick depends on your infrastructure, the frameworks you use, and how much control you want over your setup.

You can also connect to our experts at Leanware to assess your LLM observability and evaluation needs and identify the best solution for your workflows.

Frequently Asked Questions

What's the latency overhead for Langfuse vs LangSmith?

Both platforms use async, non-blocking trace submission with overhead in the low-milliseconds range. Neither blocks your application's main execution path. Langfuse sends events in background batches. LangSmith uses an async callback handler. Self-hosted Langfuse can reduce network latency by keeping data local.

How do I set up custom evaluation metrics for RAG pipelines?

RAG pipelines require metrics for retrieval relevance, answer faithfulness, and context completeness. In Langfuse, integrate with frameworks like Ragas or define custom evaluators through APIs. In LangSmith, create datasets with expected outputs and use LLM-as-judge prompts or code-based checks. Both track metrics over time for regression detection.

What happens to traces during service outages?

Both platforms handle outages gracefully. SDKs buffer events locally and retry submission. Your application continues running normally. For maximum control over availability, self-hosted Langfuse eliminates external dependencies entirely.

Can I export trace data to Datadog/Grafana?

Yes. Langfuse is built on OpenTelemetry, enabling export to any OTEL-compatible backend including Datadog, New Relic, and Grafana. LangSmith added OpenTelemetry support for similar integrations. Both support API-based exports to existing monitoring infrastructure.

How do I implement A/B testing for prompt variants?

In both platforms, use prompt versioning and tag traces with variant metadata. Segment analytics by variant to compare performance metrics. Neither provides built-in statistical significance testing, so implement that logic separately or use external experimentation tools.

What's the maximum trace payload size?

Langfuse handles large payloads through S3-compatible blob storage. LangSmith supports arbitrary binary files including images, audio, and PDFs. Both may truncate very large payloads. For frequently large files, consider capturing storage references rather than embedding full content in traces.