LangSmith vs Portkey: Observability, Prompts & Evals Comparison
Compare LangSmith and Portkey side by side. Both are tools in the Observability, Prompts & Evals category.
Updated March 10, 2026
The short answer
Choose LangSmith if deep integration with LangChain framework provides unmatched observability for LangChain applications.
Choose Portkey if enterprise-scale monitoring (10B requests/month).
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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 | smith.langchain.com | portkey.ai |
| Key Features |
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| Use Cases |
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Pros & Cons: LangSmith vs Portkey
- +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
- +Enterprise-scale monitoring (10B requests/month)
- +Sub-40ms overhead
- +Comprehensive analytics and tracing
- +Strong security certifications
- −Professional tier expensive (USD 2-5k/mo)
- −Open-source has limited features
- −Complex for small teams
Pricing: LangSmith vs Portkey
- · 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
- · Open-source
- · Basic observability
- · Self-managed
- · Community support
- · Managed service
- · Advanced analytics
- · Standard support
- · Team features
- · Custom limits
- · SOC2, ISO27001
- · Priority support
- · Private deployment
When to Choose LangSmith vs Portkey
- Debugging LangChain and LangGraph applications
- Prompt iteration and A/B testing
- LLM output evaluation and scoring
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.
About Portkey
Portkey Observability is the monitoring and analytics component of the Portkey AI platform, providing comprehensive visibility into LLM applications. The platform tracks requests, costs, latency, errors, and user behavior across all LLM providers. Portkey Observability integrates seamlessly with the Portkey AI Gateway, offering unified monitoring for multi-provider AI applications. The platform provides real-time dashboards, alerting, and detailed trace analysis to help teams optimize AI performance and costs. Portkey processes over 10 billion requests monthly with sub-40ms overhead, providing enterprise-grade observability for production AI systems.
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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