Comparing LLM Observability Tools: LangSmith, LangFuse, Lunary, and Helicone

Observability for LLM applications is critical. Whether you’re troubleshooting unexpected model outputs, tracking token usage and costs, or fine-tuning your prompt strategies, having the right observability tool can make all the difference. In this post, we compare four popular platforms—LangSmith, LangFuse, Lunary, and Helicone—to help you determine which fits your needs best.

Why LLM Observability Matters

LLM observability goes beyond classic infrastructure monitoring. With LLM apps, you need:

• Detailed tracing of prompt-to-response flows
• Evaluation metrics to monitor model performance and output quality
• Cost tracking for usage-heavy deployments
• Robust integration with your existing workflows (e.g., LangChain or other frameworks)

As models become more complex and integrated into mission-critical applications, understanding these dimensions is essential for debugging, compliance, and performance optimization.

Tool Overviews & Key Differences

LangSmith

Overview:
LangSmith is a commercial, cloud-based observability platform built by the LangChain team. It integrates deeply with LangChain workflows, offering out-of-the-box tracing and evaluation features.

Pros:

• Seamless integration if you’re already using LangChain
• Built-in evaluation and feedback loops that simplify quality tracking

Considerations:

• Closed-source; self-hosting isn’t available unless you opt for an enterprise plan
• Premium pricing can be steep—enterprise plans reportedly start around $75k

LangFuse

Overview:
LangFuse is an open-source alternative designed to work across multiple frameworks. It emphasizes flexibility and data ownership, as it’s fully self-hostable.

Pros:

• Open-source with robust community support
• Self-hostable for enhanced privacy and cost control
• Wide integrations beyond LangChain

Considerations:

• The dashboard and UI can be confusing, especially during initial setup
• Requires more manual configuration compared to LangSmith

Lunary

Overview:
Lunary positions itself as a model-agnostic observability tool. Its cloud service includes features like a “Prompt Playground” and “Radar” for categorizing LLM responses against expected outputs.

Pros:

• Lower-cost entry point (with pro plans reportedly starting as low as $20–$59 per user)
• Offers a mix of observability and prompt management tools
• Integrates with both LangChain and non–LangChain workflows

Considerations:

• Although promising, it’s relatively new and evolving; self-hosting options may be limited

Helicone

Overview:
Helicone is an open-source LLM observability tool that quickly hooks into your application by acting as a proxy for LLM API calls.

Pros:

• Very easy to set up with minimal code changes (often just two lines)
• Supports multiple endpoints (e.g., OpenAI, Anthropic)
• Generous free tier (up to 50K monthly logs)

Considerations:

• Primarily logs requests and responses, with fewer built-in evaluation features
• May require additional tooling for advanced analytics

Quick Feature Comparison

Feature	LangSmith	LangFuse	Lunary	Helicone
Integration	Seamless with LangChain	Works with multiple frameworks	LangChain & general	Minimal code changes for proxy
Self-Hosting	Enterprise only	Fully self-hostable	Limited/free cloud tier	Self-hostable (open-source)
Open Source	No (closed source)	Yes	Yes	Yes (MIT License)
Evaluation Tools	Built-in, advanced	Customizable, manual	Built-in prompt playground & Radar	Basic logging-focused
Pricing	Premium pricing (~$75k+ for enterprise)	Free open-source option (plus paid tiers)	Low-cost pro plans ($20–$59/user)	Generous free tier (50K logs)

Which Tool Is Right for You?

For Seamless LangChain Integration:
If your LLM workflow is built entirely around LangChain and you want plug-and-play functionality, LangSmith offers an excellent, managed solution.

For Data Control and Flexibility:
If you need complete control over your data, want to avoid vendor lock-in, and are comfortable with a bit of extra configuration, LangFuse is a robust choice.

For Cost-Effective, Model-Agnostic Monitoring:
Startups or smaller teams looking for a budget-friendly option that still provides a comprehensive observability suite might favor Lunary.

For a Lightweight, Proxy-Based Setup:
If you need an extremely simple, open-source solution that easily integrates with your existing observability stack (e.g., Datadog), Helicone is worth considering.

LLM observability is evolving alongside the models themselves. Whether you opt for a fully managed solution like LangSmith or prefer the flexibility of open-source tools like LangFuse, Lunary, or Helicone, understanding your specific needs—integration, cost, privacy, and ease of use—is key.

Each tool brings its own strengths to the table. Consider your team’s technical expertise, infrastructure preferences, and scalability requirements when choosing your observability platform. In doing so, you’ll not only enhance your LLM’s performance but also safeguard your operations against unexpected model behavior.

Happy monitoring and optimizing!