Helicone and LangSmith both cater to AI observability, but Helicone stands out with a freemium pricing model and integration capabilities, boasting 5,406 GitHub stars, while LangSmith, a cloud-only service with paid access, is supported by a larger team of around 98 employees and is backed by $260M Series B funding. Helicone is noted for its integrations with platforms like Prometheus and Grafana, whereas LangSmith offers comprehensive agent debugging tools and performance monitoring dashboards.
Best for
Helicone is the better choice when focusing on monitoring LLM performance and startup environments, especially if you're leveraging open-source integrations and need a cost-effective entry point.
Best for
LangSmith is the better choice when dealing with complex multi-agent AI systems in larger organizations that require robust agent debugging tools and cloud infrastructure reliability.
Key Differences
Verdict
Choose Helicone if you seek flexibility with pricing and community-driven development, ideal for small teams or educational projects that benefit from its open-source nature. LangSmith is suited for enterprises who require reliable support and advanced features like multi-agent system observability, backed by significant financial resources. Your decision should consider not just current needs but the strategic direction you foresee, particularly regarding scale and complexity.
Helicone
AI Gateway & LLM Observability
Helicone appears to be well-regarded, achieving positive ratings of 4/5 and 5/5 on G2, indicating user satisfaction with its functionality. Users highlight its integration within the domain of LLM (Large Language Model) tools, although it seems to have its own tracing format, which may add complexity in environments where standardization, like OpenTelemetry, is present. While pricing specifics are not detailed, the overall sentiment regarding value appears to be positive, given the high ratings. Helicone has a solid reputation, with notable mentions across multiple platforms, suggesting a strong presence and interest in its capabilities.
LangSmith
View in LangSmith
LangSmith is recognized for its capabilities in providing observability for AI agents, a necessary feature due to the risk associated with running these agents in production environments. A key complaint highlighted is that LangSmith is a cloud-only service with paid access, which may not be ideal for all users, especially those preferring open-source alternatives. The general sentiment around its pricing is somewhat negative, as users express a preference for non-commercial options. Overall, LangSmith appears to have a solid reputation for its functional strengths but faces criticism regarding its availability and cost structure.
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What do you like best about Helicone?Track usage, costs, and latency metrics with one line of codes. Review collected by and hosted on G2.com.What do you dislike about Helicone?How long it takes to scan the computer while doing the upload. Review collected by and hosted on G2.com.
What do you like best about Helicone?It's actually a great Open-source and cheap Platform for tracking different LLM usage, and can also create alerts on LLM responses. It supports multiple LLMs, including open-source ones. You'll get 100,000 free token uses. It's easy to implement and also offers great customer support. I use it more to integrate it into my projects. Review collected by and hosted on G2.com.What do you dislike about Helicone?The issue is that there are numerous alternatives, and implementing a custom LLM proxy on a framework like Axflow is challenging. The Experiment features are yet to be introduced, so we'll have to wait and see how go it is. Review collected by and hosted on G2.com.
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OpenTelemetry just standardized LLM tracing. Here's what it actually looks like in code.
Every LLM tool invents its own tracing format. Langfuse has one. Helicone has one. Arize has one. If...
LangSmith
Ask HN: How are you monitoring AI agents in production?
With the recent incidents (DataTalks database wipe by Claude Code, Replit agent deleting data during code freeze), it's clear that running AI agents in production without observability is risky.<p>Common failure modes I've seen: no visibility into what the agent did step-by-step, surprise
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LangSmith is better suited for monitoring AI agent performance with its specialized tools for real-time metrics and debugging.
Helicone offers a freemium option and multiple tiered pricing starting as low as $5, making it more flexible compared to LangSmith's paid-only access.
Helicone appears to have better community support indicated by 5,406 GitHub stars, emphasizing its open-source and collaborative nature.
While primarily focusing on different aspects of AI observability, using both could complement broader AI performance and debugging needs, but integration may require custom solutions.
Helicone's freemium model and open-source approach may offer an easier starting point for smaller teams or those with budget constraints.
