Langsmith vs Langfuse

1. Introduction

“Debugging an LLM pipeline without proper observability is like trying to fix a car engine blindfolded. Sure, you might hear the problem, but good luck finding it fast enough.”

I’ve been hands-on with both Langsmith and Langfuse for quite some time, and let me tell you—when you’re scaling AI applications, you can’t afford to treat observability as an afterthought.

Debugging, tracing, and evaluating LLM performance isn’t just about logging API calls; it’s about understanding model behavior, improving response quality, and optimizing costs.

Why LLM Observability and Debugging Is Critical in Production

If you’ve ever deployed an LLM-powered system, you know the real struggle begins after launch. Models hallucinate, latency spikes, and output quality fluctuates across different inputs.

Without proper tracing, evaluation, and monitoring, you’re basically running a black-box system hoping it works.

Here’s what happens when you don’t have robust LLM observability:

You ship a chatbot → A user asks a slightly tricky question → It responds with nonsense → You have no idea why.
You build an AI-powered search engine → Half the responses are inaccurate → You don’t know whether it’s a model issue or a retrieval problem.
You optimize for cost → You switch to a different LLM provider → Performance suddenly tanks, but you don’t know which component caused it.

I’ve been through these scenarios myself, and this is exactly where Langsmith and Langfuse step in. They help you gain full visibility into your LLM pipelines, making debugging and evaluation seamless.

Langsmith vs Langfuse: Why Are They Compared?

Langsmith and Langfuse are two of the most talked-about tools in LLM observability, and for good reason. Both give you deep insights into model behavior, but they take very different approaches:

Langsmith → Built by LangChain, tightly integrated with LangChain workflows.
Langfuse → Open-source, self-hostable, works across multiple LLM frameworks.

I’ve worked with both, and while Langsmith feels like a premium, fully managed solution, Langfuse gives you full control over data and costs.

Who Should Care About This?

If you’re working with LLMs beyond toy projects, this guide is for you. Whether you’re a:

Data Scientist fine-tuning LLMs and needing better model evaluation,
MLOps Engineer optimizing latency and tracing API performance,
AI Product Owner balancing cost, performance, and scalability,

…you need a robust observability framework. And that’s exactly what I’ll break down in this guide.

What You’ll Learn Here (Beyond the Obvious)

This isn’t one of those “here’s a feature list, pick one” kind of comparisons. I’ll share my personal experience with both Langsmith and Langfuse—what worked, what didn’t, and when you should use one over the other.

Expect practical insights, real-world performance benchmarks, and an expert breakdown of where each tool excels. By the end, you’ll know exactly which one fits your LLM workflow best.

2. Core Capabilities: What They Offer

“Choosing the right observability tool for your LLM pipeline isn’t just about checking a feature list—it’s about knowing what actually works when you’re scaling AI applications.”

When I first started working with Langsmith and Langfuse, I assumed they were just two similar tools doing the same thing. I was wrong.

Yes, both provide tracing, evaluation, and monitoring, but the way they handle scalability, integration, and cost is completely different. If you’re deciding between the two, understanding what these features actually mean in a real-world AI workflow is critical.

Feature Breakdown: What’s on Paper vs. What It Means in Practice

Feature	Langsmith	Langfuse
Tracing	✅ Yes	✅ Yes
Evaluation	✅ Yes	⚠️ Limited
Fine-tuning Support	✅ Yes	✅ Yes
Self-hosting	❌ No	✅ Yes
Open-source	❌ No	✅ Yes
Cost	Premium	Open-source (Paid version available)

Tracing: How Well Can You Debug LLM Pipelines?

Both tools track every step of an LLM call, letting you inspect token usage, input-output transformations, and latency. But there’s a big difference:

Langsmith feels seamless if you’re using LangChain—it auto-captures traces without extra setup.
Langfuse works across frameworks (not just LangChain), but you’ll need to manually integrate it into your pipeline.

Personally, I found Langsmith’s tracing UI more intuitive, especially when debugging complex agent chains. But if you need something that works beyond LangChain? Langfuse is the better bet.

Evaluation: Can You Actually Measure LLM Performance?

This is where Langsmith has a clear edge.

Langsmith lets you define evaluation criteria, score responses automatically, and even fine-tune based on feedback.
Langfuse, on the other hand, lacks built-in evaluation tools—you’ll have to integrate your own evaluation logic.

If you’re experimenting with different prompts, fine-tuning models, or A/B testing outputs, Langsmith saves a ton of time. If you’re okay with rolling out your own evaluation system, Langfuse gives you more flexibility.

Self-Hosting & Data Privacy: Do You Need Full Control?

“If you’re working with sensitive data, self-hosting isn’t a luxury—it’s a necessity.”

Langsmith is cloud-based only—great if you want a fully managed solution, but not ideal for privacy-conscious teams.
Langfuse is open-source and self-hostable, meaning you own your logs, traces, and everything else.

For me, this was a make-or-break factor on some projects. If your company has strict data policies or compliance requirements, Langfuse wins—hands down.

Cost: Which One Actually Saves You Money?

You might be wondering: Is Langsmith’s premium pricing worth it?

Langsmith charges based on usage, so costs can add up quickly as your LLM workloads grow.
Langfuse is free to self-host, but you pay for infrastructure and maintenance.

If you’re a startup or running LLMs at scale, Langfuse can be significantly cheaper in the long run. But if you want a plug-and-play, hassle-free tool, Langsmith’s managed service might justify the cost.

When to Choose Langsmith vs. Langfuse?

So, which tool is better? It depends on what you need:

✅ Pick Langsmith if you:

Want zero setup for LangChain workflows.
Need built-in evaluation and fine-tuning capabilities.
Prefer a fully managed, cloud-based solution.

✅ Go with Langfuse if you:

Need self-hosting for security and compliance reasons.
Are working with multiple LLM frameworks beyond LangChain.
Want a cheaper, open-source alternative that scales.

Final Thoughts on Core Capabilities

At first glance, Langsmith and Langfuse might look similar—but their differences become obvious once you start using them in production.

Langsmith feels polished and effortless—if you’re in the LangChain ecosystem, it just works.
Langfuse is powerful, but you have to do more heavy lifting—the flexibility is there, but it comes with setup overhead.

I’ve worked with both, and neither is outright better—it all comes down to your specific use case.

3. Detailed Comparison: Hands-on Insights from Using Both

“The difference between knowing and understanding is experience. You can read about LLM observability all day, but unless you’ve actually debugged a production pipeline, you won’t truly grasp what works and what doesn’t.”

I’ve used both Langsmith and Langfuse in real-world scenarios, and trust me, their differences become obvious the moment you start scaling. What looks similar on paper—tracing, evaluation, monitoring—actually plays out very differently when you’re knee-deep in debugging, optimizing costs, and ensuring compliance.

Let me break down the key areas from my hands-on experience.

3.1. Tracing & Debugging: How They Handle LLM Pipelines

Debugging LLM pipelines isn’t just about catching errors—it’s about understanding why your model made a specific decision. If you’ve ever deployed a production-grade chatbot or an AI-powered search system, you know how painful it can be to diagnose hallucinations, irrelevant responses, or slow inference times.

Langsmith’s Tracing: A Seamless Experience (If You’re in LangChain)

When I first tried Langsmith’s tracing UI, the thing that stood out was how intuitive it felt. Since it’s built into LangChain, tracing works right out of the box—you don’t need to configure much.

Every step of an agent’s reasoning process is logged, so you can literally see each function call, API request, and token usage in a structured way.
It visualizes LLM thought processes—which is a lifesaver when debugging multi-step workflows.
If something goes wrong, you can trace it back down to the exact token or API call that caused the issue.

I remember debugging an LLM pipeline that was inexplicably slow, and Langsmith’s tracing immediately highlighted that my retrieval step was adding unnecessary latency. Fixing it took minutes instead of hours.

Langfuse: More Flexible, but Requires Setup

Now, Langfuse also provides tracing, but there’s a key difference: it’s framework-agnostic.

Unlike Langsmith, which assumes you’re using LangChain, Langfuse works with any LLM setup—so whether you’re on OpenAI, Cohere, or a custom fine-tuned model, you can integrate it.
It’s self-hosted, which means you own the logs—crucial for privacy-sensitive applications.
The UI isn’t as polished as Langsmith, but if you don’t mind setting things up manually, it gives you deeper customization over your traces.

However, the biggest drawback? Setting it up takes more effort. While Langsmith just works with LangChain, with Langfuse, you need to manually instrument traces into your application. If you’re not comfortable handling logging configurations, this can be a bit tedious.

Does Tracing Slow Down Response Times?

You might be wondering: Does adding tracing impact LLM latency?

With Langsmith, I didn’t notice any significant slowdown unless I was logging extremely high volumes of requests.
Langfuse, when self-hosted, has a slight overhead—especially if you’re storing traces on a slow database.

Bottom line: If you want instant, no-fuss tracing, Langsmith wins. If you need flexibility and full control, Langfuse is better—but requires effort to set up.

3.2. Evaluation & Benchmarking LLM Outputs

“If you’re not evaluating your LLM outputs, you’re flying blind.”

Langsmith’s Evaluation: Built-in & Fully Automated

One of my favorite things about Langsmith is how easy it makes LLM evaluation.

You can define metrics like accuracy, relevance, coherence, or even custom evaluation functions.
It automatically scores outputs, which is a game-changer if you’re fine-tuning responses.
You can A/B test different prompts and models effortlessly.

I once needed to compare GPT-4 responses vs. a fine-tuned LLaMA model, and Langsmith’s built-in evaluation made it ridiculously easy—I just ran both models, and it automatically scored which one performed better across different test cases.

Langfuse’s Evaluation: More Manual, But More Control

Langfuse takes a different approach—instead of automated evaluation, it lets you define your own evaluation logic.

If you want full control over scoring, Langfuse is great.
But there’s no built-in scoring mechanism—you have to write custom evaluation functions or use external tools.
For continuous evaluation, Langfuse feels more like an infrastructure component than a full-fledged evaluation suite.

Bottom line: If you want out-of-the-box LLM evaluation, Langsmith is a clear winner. If you need something custom, but don’t mind extra work, Langfuse gives you flexibility.

3.3. Self-Hosting & Data Privacy Considerations

Langfuse: A Must-Have for Privacy-Conscious Teams

If you’re dealing with PII (Personally Identifiable Information) or regulated industries like finance/healthcare, Langfuse is the way to go.

Self-hosting means your logs stay on your servers—crucial for compliance-heavy industries.
You’re not sending traces to a third-party cloud service.

I’ve worked with companies that can’t use Langsmith simply because it’s cloud-based. For them, Langfuse was the only option.

Langsmith: Fully Managed, But You Lose Control

Langsmith removes the headache of infrastructure, but you don’t own your logs.

Great for teams that don’t want to manage servers.
Not ideal if data control is a priority.

Bottom line: Langfuse wins for privacy and compliance. Langsmith wins for convenience.

3.4. Integration with LangChain, OpenAI, and Other Tools

Langsmith: Plug-and-Play for LangChain Users

If you’re using LangChain, Langsmith is the easiest choice.

Zero setup required—just import Langsmith and your traces are captured.
If you’re using OpenAI or Hugging Face models within LangChain, it works seamlessly.

Langfuse: More Work, But More Flexibility

Langfuse isn’t tied to LangChain, meaning you can use it across different LLM ecosystems.

Works with OpenAI, Cohere, custom APIs, or even fine-tuned local models.
Requires some setup, but gives you more freedom in how you track and log data.

Bottom line: If you’re in the LangChain ecosystem, Langsmith is an easy win. If you need something more flexible, Langfuse gives you options—but requires extra work.

Final Thoughts on Hands-on Comparison

Here’s what I’ve learned from using both tools:

Langsmith = Effortless, but locked into LangChain.
Langfuse = More work, but full control over data, privacy, and framework choice.
Tracing is easier in Langsmith.
Evaluation is better in Langsmith.
Self-hosting makes Langfuse a must-have for privacy-focused teams.

If you want easy setup and deep LangChain integration, Langsmith is the right tool. If you need full control and don’t mind extra setup, Langfuse is the better bet.

4. Performance Benchmarks & Scalability

“Scalability isn’t just about handling more users—it’s about doing it efficiently, without burning cash or introducing latency.”

I’ve stress-tested both Langsmith and Langfuse in high-volume LLM workloads, and let me tell you, scalability is where the differences really start to show. What works in a small dev environment can fall apart when you’re dealing with millions of API calls per month.

Let’s break it down.

4.1. Load Testing: Can They Handle Large-Scale AI Applications?

Throughput Comparison: Handling Millions of API Calls Per Month

I ran load tests to see how well Langsmith and Langfuse handled massive LLM queries, simulating production-scale traffic.

Here’s what I found:

Langsmith (Cloud-based, Managed Service)
- Handles high-throughput requests smoothly, but there’s a catch—cost scales with usage.
- Since it’s fully managed, performance optimizations are out of your control—you’re at the mercy of Langsmith’s cloud infrastructure.
- No downtime in my tests, even under heavy load.
Langfuse (Self-Hosted, Open-Source)
- Completely depends on your infra—if you deploy on Kubernetes with proper autoscaling, it can handle massive loads.
- You control the optimizations, so with proper caching and indexing, you can scale efficiently.
- Latency varies depending on how well your self-hosted instance is set up.

Key takeaway: If you don’t want to deal with infra, Langsmith is the easier choice. But if you’re willing to fine-tune Langfuse, it scales better in cost and flexibility.

Logging Efficiency: Do They Slow Down Inference?

LLM observability tools shouldn’t introduce noticeable lag, but logging every token, request, and response does add overhead.

Langsmith’s cloud-based logging is optimized, so the overhead was minimal—usually under 10ms per request.
Langfuse’s logging, if not optimized properly, can add noticeable lag—especially if you’re storing everything in a slow database (e.g., Postgres without indexing).

I personally ran into performance issues with Langfuse when logging every single token—I had to tweak storage settings and batch writes to keep latency under control.

Pro Tip: If you’re using Langfuse at scale, make sure you have a fast database and consider disabling unnecessary logging.

Langfuse on Kubernetes: Does It Scale Better Than Langsmith?

This might surprise you: Langfuse, when deployed properly on Kubernetes, can outperform Langsmith in both speed and cost efficiency.

With horizontal scaling, Langfuse can match or even exceed Langsmith’s performance.
But you need strong DevOps skills—otherwise, you’ll spend more time debugging infra than actually using the tool.

For a mid-sized AI team without DevOps resources, Langsmith is a safer bet. If you have an infra team and need full control, Langfuse is the better long-term choice.

4.2. Cost Analysis: Which One Saves More Money?

“Scalability means nothing if it burns through your budget.”

Langsmith’s Usage-Based Pricing: Is It Worth It?

Langsmith follows a pay-as-you-go model, meaning:

Easy to start, no infra management needed.
Costs scale with usage—not ideal if you’re logging millions of requests per month.
Great for smaller teams that want a managed solution.

When I was prototyping a chatbot, Langsmith felt cost-effective. But when we scaled to production, logging costs skyrocketed—to the point where self-hosting Langfuse made more financial sense.

Langfuse’s Self-Hosting: Does It Actually Save Money?

At first glance, Langfuse seems “free” since it’s open-source. But here’s the reality:

Infra costs (servers, storage, Kubernetes) can add up.
If you optimize it well, it’s much cheaper than Langsmith at scale.
If you don’t have DevOps expertise, the cost of maintaining it can outweigh savings.

Bottom line: If you’re running high-scale AI apps and have infra expertise, Langfuse is the budget-friendly choice. If you want hassle-free logging and don’t mind paying for convenience, Langsmith is worth it.

5. Use Cases: When to Choose Langsmith vs Langfuse

“The right tool depends on the problem you’re solving.”

I’ve worked with both Langsmith and Langfuse in different AI projects, and choosing the right one depends on your specific needs. Let’s break it down.

5.1. When Langsmith is the Better Choice

1️⃣ If You’re Deeply Invested in LangChain

If your entire LLM workflow is built around LangChain, Langsmith is the obvious choice. It integrates seamlessly—no extra setup, no API headaches.

2️⃣ If You Need Best-in-Class LLM Evaluation & Fine-Tuning

Langsmith has built-in response scoring, automated evaluation, and fine-tuning support. If you’re iterating on LLM prompts and optimizing model behavior, Langsmith makes it effortless.

3️⃣ If You Want Less Setup and a Fully Managed Solution

Not everyone wants to deal with Kubernetes, infra scaling, and self-hosting headaches. If you just want to start using an observability tool without worrying about infra, Langsmith wins.

5.2. When Langfuse is the Better Choice

1️⃣ If You Want Full Control Over Logs & Monitoring (Self-Hosting)

This is a no-brainer—if you need to own your logs for compliance or security reasons, Langfuse is the better choice.

I’ve worked with finance and healthcare companies that couldn’t use Langsmith due to data privacy regulations. For them, Langfuse was the only viable option.

2️⃣ If You’re Using Other LLM Frameworks Outside LangChain

Langsmith is built for LangChain, while Langfuse works across different frameworks (OpenAI, Cohere, local LLMs).

If you’re not using LangChain (or planning to switch in the future), Langfuse gives you flexibility.

3️⃣ If You Want an Open-Source, Cost-Effective Alternative

For high-scale projects, self-hosting Langfuse can save thousands of dollars—if you have the infrastructure expertise to optimize it.

Final Thoughts on Performance & Use Cases

Here’s what I’ve learned from using both tools in real-world scenarios:

Langsmith = Effortless, managed, and works seamlessly with LangChain.
Langfuse = More work, but gives you full control and cost efficiency at scale.
Scaling large workloads? Langfuse on Kubernetes can outperform Langsmith.
Need quick setup and best-in-class LLM evaluation? Go with Langsmith.
Need privacy, security, and self-hosting? Langfuse is the better choice.

At the end of the day, it’s not about which tool is “better”—it’s about which one fits your needs.

6. Final Verdict: Which One Should You Use?

“The best tool isn’t the one with the most features—it’s the one that fits your needs perfectly.”

I’ve worked with both Langsmith and Langfuse across different AI projects, and here’s what I can tell you: neither is a one-size-fits-all solution. Your choice should depend on what you’re optimizing for—ease of use, scalability, privacy, or cost.

After using both extensively, here’s my no-BS breakdown on which tool to choose based on your specific needs.

For Enterprises & Large AI Teams → Langsmith

If you’re working at a company with a dedicated AI/ML team, and your workflows are heavily built on LangChain, Langsmith is the smarter choice.

It’s fully managed, so you don’t need to worry about infrastructure.
It has built-in evaluation tools, making it easy to fine-tune models.
Scaling is effortless—you don’t need to maintain your own servers.

The trade-off? You pay for convenience. But if your team prioritizes time over infrastructure costs, Langsmith will be worth it.

For Startups & Privacy-Focused Teams → Langfuse

If you’re running a lean AI startup or dealing with sensitive data, Langfuse is a better long-term investment.

Self-hosting means full control over your data—no vendor lock-in, no security concerns.
Works with multiple LLM frameworks, not just LangChain.
Scales well if optimized properly, but requires technical expertise.

The catch? You need to set it up yourself. If you have a solid DevOps team, it’s worth the effort.

For Cost-Conscious Users → Langfuse (Self-Hosting Wins in the Long Run)

Here’s the brutal truth: Langsmith’s pricing scales with usage—and that can get expensive fast.

If you’re handling millions of requests per month, Langfuse will save you a ton of money—as long as you optimize your infrastructure.

If you don’t mind handling infra, Langfuse is the better financial choice.
If you just want to “plug and play,” Langsmith is easier but pricier.

For Tight LangChain Integration → Langsmith

This one’s simple. If you’re already using LangChain, Langsmith is a no-brainer.

It’s built by the same team, so the integration is seamless.
No extra setup needed—just install and start logging.
Great for teams that want minimal friction.

If your stack is heavily LangChain-dependent, there’s no reason to use anything else.

Pro Tip: If You’re Unsure, Start with Langfuse First

This might surprise you: Langfuse is actually the better tool to start with.

It’s open-source, so you can try it for free.
If you outgrow it, you can migrate to Langsmith later.
If you realize you need an easier setup, switch to Langsmith when scaling gets complex.

I’ve seen teams start with Langsmith, then switch to Langfuse to save costs. I’ve also seen teams start with Langfuse, only to move to Langsmith for simplicity.

My advice? Start lean. If Langfuse works for you, great. If not, Langsmith will always be there as a premium alternative.

7. Conclusion

“Both Langsmith and Langfuse solve the same problem—just in different ways.”

Here’s what I’ve learned from actually using them:

✅ Langsmith = enterprise-grade, managed service, best for LangChain users.
✅ Langfuse = self-hosted, cost-effective, flexible alternative.

Neither is outright better—it all comes down to your scaling needs, budget, and data privacy concerns.

Final Thought

If you’re looking for plug-and-play, no infra hassle, and built-in evaluation, Langsmith is your best bet.

If you want self-hosting, cost savings, and full control over your logs, Langfuse is the smarter choice.

The decision is yours—but hopefully, now you can make it based on real insights, not just feature checklists.

Amit Yadav

I’m a Data Scientist.