Langfuse vs LangSmith: An Independent Verdict

Langfuse vs LangSmith: An Independent Verdict

The Langfuse vs LangSmith choice boils down to one philosophical split: open-source and framework-agnostic vs proprietary and LangChain-native. That decision shapes everything from where your data lives to how much you pay at scale.

Here's what makes this comparison different: we don't sell an observability tool. If you look at the other top results for this search, six out of ten are written by vendors with a financial stake, Langfuse comparing itself to LangSmith, or competitors like Lunary and Mirascope quietly pushing their own products. We're independent. Both tools have genuine strengths, and we'll be honest about where each one wins.

One important context: Langfuse v3 shipped in mid-2025 with a full OpenTelemetry-native architecture, which changes this comparison significantly. Most articles on the SERP were written before v3. This one wasn't. If you're trying to understand what LLM observability actually means before diving into tools, start there.

Quick Summary, Langfuse vs LangSmith at a Glance

Bottom line: Langfuse wins 5 categories, LangSmith wins 3, and 4 are ties. But the "right" choice depends heavily on your framework, data requirements, and budget. Read on for the details.

Tracing and Observability

Both platforms exist to answer the same question: "what happened inside my LLM call?" You want to see every input, output, latency, token count, and cost for every LLM interaction in your app. How they get there is different.

Langfuse Tracing Setup

# pip install langfuse openai
import os
from langfuse.openai import openai  # Drop-in replacement

os.environ["LANGFUSE_PUBLIC_KEY"] = "pk-..."
os.environ["LANGFUSE_SECRET_KEY"] = "sk-..."
os.environ["LANGFUSE_HOST"] = "https://cloud.langfuse.com"  # or your self-hosted URL

# That's it -- all OpenAI calls are now traced automatically
response = openai.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "Explain quantum computing simply"}]
)

For more control, use the @observe decorator:

from langfuse.decorators import observe

@observe()
def analyze_document(doc: str) -> str:
    # This entire function becomes a trace span
    summary = openai.chat.completions.create(
        model="gpt-4o",
        messages=[{"role": "user", "content": f"Summarize: {doc}"}]
    )
    return summary.choices[0].message.content

LangSmith Tracing Setup

# pip install langsmith openai
import os
os.environ["LANGSMITH_API_KEY"] = "lsv2-..."
os.environ["LANGSMITH_TRACING"] = "true"

# If using LangChain, tracing is automatic -- zero config
from langchain_openai import ChatOpenAI
llm = ChatOpenAI(model="gpt-4o")
response = llm.invoke("Explain quantum computing simply")

# If NOT using LangChain, use the @traceable decorator
from langsmith import traceable

@traceable
def analyze_document(doc: str) -> str:
    response = openai.chat.completions.create(
        model="gpt-4o",
        messages=[{"role": "user", "content": f"Summarize: {doc}"}]
    )
    return response.choices[0].message.content

What You See in Each Dashboard

Both dashboards show trace hierarchies, input/output pairs, latency breakdowns, and token counts. LangSmith's dashboard is more polished visually, the trace tree is easier to navigate and the filtering is more intuitive. Langfuse's dashboard is functional and improving with every release, but LangSmith has a head start on UI polish.

The critical technical difference: Langfuse v3 traces are OpenTelemetry spans under the hood. If your team already uses OTEL for backend observability (Jaeger, Grafana Tempo, Honeycomb), Langfuse traces slot right into your existing stack. LangSmith uses its own proprietary tracing format.

Verdict: Langfuse wins for framework-agnostic projects. LangSmith wins if you're all-in on LangChain. If you're using LangChain and want zero-config tracing, LangSmith is genuinely easier. For everything else, OpenAI SDK, Pydantic AI, Vercel AI SDK, custom setups, Langfuse is more flexible.

Evaluation and Evals

LLM evals answer the question: "is my LLM output actually good?" This is where the two platforms diverge most sharply.

LangSmith's evaluation system is genuinely more mature. You can create a dataset, run your agent against it, and get accuracy/relevance scores in a few lines of code. The evaluate() function integrates with CI/CD pipelines so you can block deployments when eval scores drop. For deeper coverage of evaluation approaches, see how LLM evaluation works.

Langfuse's approach is more DIY, you can score traces via the UI or API, set up annotation workflows for human review, and integrate external eval frameworks. It works, but it requires more glue code.

Verdict: LangSmith has a genuine edge in built-in evaluation tooling. If evals are your top priority, LangSmith makes it easier out of the box. Langfuse can get there, but you'll write more custom code.

Prompt Management

Both platforms let you version prompts, test them in a playground, and deploy changes without code deploys.

Langfuse offers prompt versioning with a playground that works with any provider. You store prompts in Langfuse, pull them via the SDK at runtime, and roll back if a new version underperforms. It's straightforward and framework-agnostic.

LangSmith has the LangChain Hub for sharing and versioning prompts, plus a playground with model-switching (try the same prompt against GPT-4o and Claude side-by-side). The playground is slightly more polished, with better auto-complete and formatting.

Both are capable for most teams. The choice here usually follows the broader platform decision.

Verdict: Both are capable. LangSmith's playground is slightly more polished; Langfuse's is more flexible with non-LangChain setups.

Framework Integrations, Beyond LangChain

This is where Langfuse pulls ahead decisively for teams not using LangChain.

If you're choosing between these frameworks in 2026, many teams are using Pydantic AI, Vercel AI SDK, or the OpenAI SDK directly, not LangChain. For those teams, Langfuse is the only platform with native integrations. LangSmith's @traceable decorator works with anything, but it requires manual instrumentation. For context on why framework choice matters here, see our LangGraph vs CrewAI comparison.

Verdict: Langfuse wins decisively for non-LangChain projects. If you're using LangChain, both work great. If you're not, Langfuse is the clear choice.

Self-Hosting and Data Sovereignty

This might be the most important section if you work at a company with compliance requirements.

Langfuse is MIT-licensed and fully self-hostable. You can run it with Docker Compose in about 30 minutes. Your LLM inputs and outputs, which often contain sensitive customer data, PII, or proprietary business logic, never leave your infrastructure. Infrastructure cost for a small team is minimal: a single VM with 2 vCPU, 4GB RAM, and a managed PostgreSQL instance.

LangSmith is cloud-first. Self-hosting is available only on the Enterprise plan, which means custom pricing (read: expensive). For most teams, LangSmith means your trace data, including every prompt and response, lives on LangChain's servers.

What this means in practice:

• GDPR compliance: If you're processing EU user data through LLMs, self-hosted Langfuse keeps that data in your EU region. LangSmith cloud routes through US servers unless you're on Enterprise.
• HIPAA: Healthcare companies using LLMs often can't send patient-related data to third-party clouds. Self-hosted Langfuse solves this.
• IP protection: If your prompts contain proprietary business logic, self-hosting means they never leave your network.

For an estimated infrastructure cost: a self-hosted Langfuse instance for a 10-person team runs on about $50-100/month of cloud infrastructure (small VM + managed Postgres). Compare that to cloud pricing below.

Verdict: Langfuse wins by a mile for self-hosting. If data sovereignty matters, there's no real alternative.

Pricing Deep-Dive, Real Costs at 3 Scales

Let's talk actual numbers. Both platforms have free tiers, but costs diverge quickly as you scale.

Pricing Models Explained

Langfuse charges per "observation" (each trace, span, or score = 1 unit). Cloud pricing: Free tier up to 50K units/month. Core plan at $29/month. Pro at $199/month. Overage: $8 per 100K units.

LangSmith charges per trace with tiered retention. Cloud pricing: Free Developer tier up to 5K traces/month. Plus plan at $39/seat/month with 10K base traces. Overage: $2.50 per 1K traces (14-day retention) or $5.00 per 1K traces (400-day retention).

Cost at Three Scales

Pricing verified April 2026. LangSmith costs assume 14-day retention; 400-day retention roughly doubles trace costs. Langfuse self-hosted costs are infrastructure only (VM + managed PostgreSQL).

The gap widens dramatically at scale. At 1M traces, Langfuse Cloud is roughly a third of LangSmith's cost, and self-hosted Langfuse is a fraction of both.

The Self-Hosted Cost Advantage

Self-hosting Langfuse is where the economics get interesting. Once you have the infrastructure running, the software itself is free (MIT license). Your only ongoing cost is the VM and database. For teams at 1M+ traces, self-hosting saves thousands per month compared to either cloud option.

Verdict: Langfuse is cheaper at every scale, and self-hosting makes it essentially free beyond infrastructure costs. LangSmith's per-seat pricing adds up fast for teams.

Langfuse v3 and OpenTelemetry, What Changed

Most Langfuse vs LangSmith comparisons on the web were written before Langfuse v3. Here's what changed and why it matters.

Langfuse v3 rebuilt the SDK around OpenTelemetry, the CNCF-backed open standard for distributed tracing. In practice this means:

• If your team already uses OTEL (Jaeger, Grafana, Datadog) for backend observability, Langfuse traces appear in the same tools. No separate dashboard for LLM traces.
• Better performance: OTEL's batched exporter is more efficient than the v2 SDK's custom transport.
• Wider integration surface: Any framework that produces OTEL spans can feed into Langfuse, not just frameworks with dedicated Langfuse integrations.
• Migration from v2: The @observe decorator API didn't change. Under the hood, it now produces OTEL spans. Most v2 code works unchanged.

LangSmith has limited OTEL support, you can export some data to OTEL-compatible backends, but it's not native. The tracing format is proprietary.

For teams with mature observability practices, this is a significant architectural advantage. Combining LLM traces with backend request traces in a single tool eliminates context switching and makes it easier to debug end-to-end latency issues.

Verdict: Langfuse v3's OTEL architecture is a significant advantage for teams with existing observability stacks.

Who Should Choose Which?, Decision Framework

Worth mentioning: Helicone, Braintrust, and Arize Phoenix are other players in this space. Helicone is a strong alternative for teams that want a proxy-based approach to observability. Braintrust focuses on evals. Arize brings ML observability expertise. Check our full ranking of AI observability platforms for a wider view.

Final Verdict

Overall: For most teams in 2026, Langfuse is the better default choice. It's open-source, cheaper, framework-agnostic, and self-hostable. The only scenario where LangSmith is clearly better: you're deeply embedded in LangChain/LangGraph AND you need LangSmith's superior evaluation tooling AND data sovereignty isn't a concern.

That said, both tools are developing fast. Langfuse's eval capabilities are improving, and LangSmith's framework support is widening. Try both free tiers before committing, Langfuse gives you 50K free observations per month, and LangSmith gives you 5K free traces. Run your actual workload through both and decide based on your experience, not just feature tables.

FAQ

Is Langfuse a good alternative to LangSmith?

Yes, for most use cases. Langfuse is open-source, cheaper at scale, framework-agnostic, and self-hostable. The main area where LangSmith still leads is built-in evaluation tooling. If evals are your primary concern, evaluate both. For everything else, Langfuse is a strong alternative.

What is the difference between Langfuse and LangSmith?

The core difference is philosophy: Langfuse is MIT open-source, framework-agnostic, and self-hostable. LangSmith is proprietary, optimized for LangChain/LangGraph, and cloud-first. Both provide LLM tracing, cost tracking, and prompt management, but they serve different engineering cultures.

Can I self-host Langfuse instead of using LangSmith?

Yes. Langfuse is MIT-licensed and has a Docker Compose deployment that takes about 30 minutes. You need a VM and a PostgreSQL database. Self-hosting means your LLM trace data (prompts, responses, user data) never leaves your infrastructure, critical for GDPR, HIPAA, and IP protection.

How does Langfuse pricing compare to LangSmith?

Langfuse is cheaper at every scale. At 100K traces/month, Langfuse Cloud costs about $69/month vs LangSmith's $420/month (5-seat team). At 1M traces, the gap widens further. Self-hosted Langfuse costs only infrastructure ($150/month), regardless of trace volume.

Does Langfuse work with frameworks other than LangChain?

Yes, that's one of its biggest advantages. Langfuse has native integrations for the OpenAI SDK, Pydantic AI, Vercel AI SDK, LlamaIndex, Anthropic SDK, and more. LangSmith works best with LangChain; other frameworks require manual @traceable instrumentation.

Which is better for LLM evaluation, Langfuse or LangSmith?

LangSmith has the edge. It offers built-in evaluators (accuracy, relevance, faithfulness), LLM-as-judge templates, native experiment tracking, and CI/CD integration via evaluate(). Langfuse's eval approach is more manual, annotation scoring and external eval integration that requires more custom code.

Is LangSmith open source?

No. LangSmith is proprietary software offered as a cloud service, with self-hosting available only on the Enterprise plan (custom pricing). Langfuse is MIT-licensed open-source, you can inspect, modify, and self-host the code freely.

Can I migrate from LangSmith to Langfuse?

Yes. Both platforms use similar concepts (traces, spans, scoring), so the mental model transfers. You'd need to swap SDK integrations (@traceable to @observe) and reconfigure environment variables. There's no one-click migration tool, but the effort is usually a few hours for a typical project.

What is Langfuse v3 and what changed?

Langfuse v3 rebuilt the SDK around OpenTelemetry (OTEL), the CNCF-backed tracing standard. This means better performance, native integration with existing OTEL tools (Grafana, Jaeger, Datadog), and a wider integration surface. The @observe decorator API stayed the same, so migration from v2 is straightforward.

Are there alternatives to both Langfuse and LangSmith?

Yes. Helicone is a proxy-based observability tool with a strong developer experience. Braintrust focuses heavily on evaluations and datasets. Arize Phoenix brings ML observability expertise to LLMs. Each has a different strength, check what matters most to your team before choosing.

Sources

• Langfuse Pricing
• Langfuse Self-Hosted Deployment, Docker Compose
• Langfuse Python SDK v3 -- OpenTelemetry GA
• LangSmith Pricing and Plans
• LangSmith Documentation
• What is OpenTelemetry?, opentelemetry.io
• langfuse/langfuse, GitHub Repository