Helicone Alternatives? Langfuse vs. Helicone
This article compares Helicone and Langfuse, two open source LLM observability platforms.
How do Helicone and Langfuse compare?
Helicone and Langfuse are both open source tools that offer comparable functionalities, including LLM observability, analytics, evaluation, testing, and annotation features.
Download and Usage Statistics
| Helicone | Langfuse | |
|---|---|---|
| GitHub Stars |  |  |
| Last Commit |  |  |
| PyPI Downloads |  |  |
Helicone AI
What is Helicone?
Helicone is an open source project for language model observability that provides a managed LLM proxy to log a variety of language models. Helicone offers their product as a managed cloud solution with a free plan (up to 10k requests / month).
What is Helicone used for?
- Logging an analysis of LLM outputs via the Helicone managed LLM proxy.
- Ingestion and collection of user feedback through the Helicone feedback API.
Read our view on using LLM proxies for LLM application development here.
Pros and Cons of Helicone
| ✅ Advantages: | ⛔️ Limitations: |
|---|---|
| Implementation: Simple and quick setup process for LLM logging. Managed Proxy: Monitoring though the Helicone managed proxy supporting caching, security checks, and key management. | Limited Tracing Capabilities: Provides only basic LLM logging with session grouping, lacking detailed traces. Lacks Deep Integration: Does not support decorator or framework integrations for automatic trace generation. Evaluation Constraints: Restricted to adding custom scores via the API with no support for LLM-as-a-judge methodology or manual annotation workflows. |
Langfuse
Example trace in our public demo
What is Langfuse?
Langfuse is an LLM observability platform that provides a comprehensive tracing and logging solution for LLM applications. Langfuse helps teams to understand and debug complex LLM applications and evaluate and iterate them in production.
What is Langfuse used for?
- Holistic tracing and debugging of LLM applications in large-scale production environments.
- High data security and compliance requirements in enterprises through best-in-class self-hosting options.
- Fast prototyping and iterating on LLM applications in small teams.
Pros and Cons of Langfuse
| ✅ Advantages: | ⛔️ Limitations: |
|---|---|
| Comprehensive Tracing: Effectively tracks both LLM and non-LLM actions, delivering complete context for applications. Integration Options: Supports asynchronous logging and tracing SDKs with integrations for frameworks like Langchain, Llama Index, OpenAI SDK, and others. Prompt Management: Optimized for minimal latency and uptime risk, with extensive capabilities. Deep Evaluation: Facilitates user feedback collection, manual reviews, automated annotations, and custom evaluation functions. Self-Hosting: Extensive self-hosting documentation of required for data security or compliance requirements. | Additional Proxy Setup: Some LLM-related features like caching and key management require an external proxy setup, such as LiteLLM, which integrates natively with Langfuse. Langfuse is not in the critical path and does not provide these features. Read more on our opinion on LLM proxies in production settings here. |
Core Feature Comparison
This table compares the core features of LLM observability tools: Logging model calls, managing and testing prompts in production, and evaluating model outputs.
| Helicone | Langfuse | |
|---|---|---|
| Tracing and Logging | Offers basic LLM logging capabilities with the ability to group logs into sessions. However, it does not provide detailed tracing and lacks support for framework integrations that would allow enhanced tracing functionalities. | Specializes in comprehensive tracing, enabling detailed tracking of both LLM and other activities within the system. Langfuse captures the complete context of applications and supports asynchronous logging with tracing SDKs, offering richer insights into application behavior. |
| Prompt Management | Currently in beta, it introduces latency and uptime risks if prompts are fetched at runtime without using their proxy. Users are required to manage prompt-fetching mechanisms independently. | Delivers robust prompt management solutions through client SDKs, ensuring minimal impact on application latency and uptime during prompt retrieval. |
| Evaluation Capabilities | Supports the addition of custom scores via its API, but does not offer advanced evaluation features beyond this basic capability. | Provides a wide array of evaluation tools, including mechanisms for user feedback, both manual and automated annotations, and the ability to define custom evaluation functions, enabling a richer and more thorough assessment of LLM performance. |
Conclusion
Langfuse is a good choice for most production use cases, particularly when comprehensive tracing, deep evaluation capabilities, and robust prompt management are critical. Its ability to provide detailed insights into both LLM and non-LLM activities, along with support for asynchronous logging and various framework integrations, makes it ideal for complex applications requiring thorough observability.
For teams prioritizing ease of implementation and willing to accept the trade-offs of increased risk and limited observability, Helicone’s managed LLM proxy offers a simpler setup with features like caching and key management.
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