Helicone vs LangSmith: Observability, Prompts & Evals Comparison
Compare Helicone and LangSmith side by side. Both are tools in the Observability, Prompts & Evals category.
Updated March 9, 2026
The short answer
Choose Helicone if open-source with 5.2K GitHub stars and strong community support.
Choose LangSmith if deep integration with LangChain framework provides unmatched observability for LangChain applications.
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Quick Comparison
| Category | Observability, Prompts & Evals | Observability, Prompts & Evals |
| Pricing | — | Freemium |
| Best For | — | LangChain developers who need integrated tracing, evaluation, and prompt management |
| Website | helicone.ai | smith.langchain.com |
| Key Features | — |
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| Use Cases | — |
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Pros & Cons: Helicone vs LangSmith
- +Open-source with 5.2K GitHub stars and strong community support
- +Minimal latency overhead (under 1ms for self-hosted)
- +Flexible deployment options including cloud-hosted, self-hosted, or SDK-only
- +Y Combinator-backed with SOC 2 Type II and HIPAA compliance
- +No code changes required for AI Gateway features like caching and rate limiting
- −Adds computational cost and one network hop for cloud deployment
- −Limited pricing transparency on website
- −May require infrastructure expertise for self-hosted setup
- +Deep integration with LangChain framework provides unmatched observability for LangChain applications
- +Comprehensive evaluation capabilities with custom metrics and automated regression testing
- +Excellent tracing UI with detailed step-by-step execution visualization
- +Strong dataset management and annotation workflows for human feedback
- −Tight coupling to LangChain ecosystem limits flexibility for non-LangChain applications
- −UI scalability issues reported when working with very large datasets
- −Production testing limitations - relies primarily on production logs for test datasets
- −SaaS-only deployment with no self-hosting option may not suit all enterprise requirements
Pricing: Helicone vs LangSmith
- · 5,000 traces per month
- · 14-day retention
- · One seat
- · Basic evaluations
- · 100,000 traces (overage ~$0.50 per 1,000)
- · 400-day retention
- · Full evaluations
- · Custom dashboards
- · Custom trace volumes
- · Custom retention policies
- · SSO
- · Dedicated support
When to Choose Helicone vs LangSmith
- Debugging LangChain and LangGraph applications
- Prompt iteration and A/B testing
- LLM output evaluation and scoring
About Helicone
Helicone is an open-source AI Gateway and LLM Observability platform that enables developers to route, debug, and analyze their AI applications with end-to-end visibility from user sessions to individual token decisions. As a Y Combinator-backed company, Helicone combines observability with infrastructure management, offering token-level cost analysis, prompt version tracking, and session tracing alongside intelligent routing features like caching, rate limiting, and load balancing. The platform provides real-time dashboards, user metrics tracking, alert systems, and multi-step LLM interaction visualization for root cause analysis. Helicone is SOC 2 Type II certified, HIPAA compliant, and offers flexible deployment options including cloud-hosted gateway leveraging Cloudflare's global network, self-hosted via Kubernetes Helm charts, or SDK-only observability without proxying.
About LangSmith
LangSmith is LangChain's observability and evaluation platform for building production-grade LLM applications. Founded in July 2023 by Harrison Chase and Ankush Gola as part of the LangChain ecosystem, LangSmith provides comprehensive tracing of every LLM call, chain execution, and agent step with detailed visibility into inputs, outputs, latency, token usage, and cost. The platform includes annotation queues for human feedback, dataset management for systematic evaluation, and regression testing capabilities for prompt changes. With over 1 million developers using LangChain products globally, LangSmith has become the go-to debugging and monitoring tool for teams building with the LangChain framework, serving major enterprises including Klarna, LinkedIn, Replit, GitLab, Elastic, and Cisco.
What is Observability, Prompts & Evals?
Tools for monitoring LLM applications in production, managing and versioning prompts, and evaluating model outputs. Includes tracing, logging, cost tracking, prompt engineering platforms, automated evaluation frameworks, and human annotation workflows.
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