Arize AI vs LangSmith: Observability, Prompts & Evals Comparison
Compare Arize AI and LangSmith side by side. Both are tools in the Observability, Prompts & Evals category.
Updated March 9, 2026
The short answer
Choose Arize AI if built on OpenTelemetry standards ensuring interoperability and avoiding vendor lock-in.
Choose LangSmith if deep integration with LangChain framework provides unmatched observability for LangChain applications.
How they actually differ
Arize AI and LangSmith both call themselves LLM observability platforms but they grew up in different worlds, and that shows in the trace shape, the eval workflow, and what you pay for.
Arize AI started in classical ML observability (drift detection, feature attribution, embedding monitoring) and added LLM coverage on top. Its open-source companion Phoenix runs on OpenTelemetry, which means traces flow into any OTel-compatible backend and out again. The platform is a fit for ML platform teams who already think in terms of features, embeddings, and model performance, and who want one tool for both classical ML and LLM workloads.
LangSmith is a LangChain-native product. If your stack is LangChain or LangGraph end to end, the integration is one line of config and the trace tree maps cleanly to your chains and agents. Outside the LangChain ecosystem the story gets weaker. The instrumentation exists for plain OpenAI and a few other SDKs, but the depth of detail visibly drops.
Where the trade-off bites: Arize gives you portability (OTel) at the cost of LangChain ergonomics. LangSmith gives you frictionless LangChain DX at the cost of vendor lock-in (proprietary trace format, harder to export, no real self-host option below enterprise pricing). If you write half your stack outside LangChain, that lock-in matters.
Pricing. Arize starts on a contact-sales motion for the full platform with Phoenix as the free open-source path. LangSmith has a published per-seat and per-trace pricing model that scales with usage. Run the numbers on your actual trace volume rather than the headline rate.
Where Respan fits. If you want LangChain-native ergonomics plus OpenTelemetry portability plus an LLM gateway in one platform, that is the gap Respan was built to close. We auto-instrument LangChain and LangGraph callback style, ship the OTel ingestion path for non-LangChain code, and surface evals, prompt management, and routing on the same data model. See our LangSmith comparison for the head-to-head.
Want to compare Arize AI and LangSmith on your own traffic?
Respan lets you trace LLM and agent calls across any model or framework, A/B test prompts on production traffic, and route requests across 250+ models through one gateway. Free tier covers 10K traces per month. Setup in 5 minutes, no credit card.
Quick Comparison
| Category | Observability, Prompts & Evals | Observability, Prompts & Evals |
| Pricing | Freemium | Freemium |
| Best For | ML teams who need comprehensive observability spanning traditional ML models and LLM applications | LangChain developers who need integrated tracing, evaluation, and prompt management |
| Website | arize.com | smith.langchain.com |
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Pros & Cons: Arize AI vs LangSmith
- +Built on OpenTelemetry standards ensuring interoperability and avoiding vendor lock-in
- +Impressive scale with 1 trillion spans processed and 5 million monthly OSS downloads
- +Open-source Phoenix OSS option available for self-hosting
- +Enterprise-proven with major clients like DoorDash, Instacart, Reddit, and Uber
- +Comprehensive platform covering development, debugging, evaluation, and production monitoring
- −Enterprise pricing starting at ~$1,000/month may be prohibitive for smaller teams
- −Complexity of full feature set may require longer onboarding and learning curve
- −Less transparent pricing compared to competitors with clear tier structures
- +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: Arize AI 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 Arize AI vs LangSmith
- Production ML and LLM monitoring
- Embedding quality monitoring
- Model performance tracking
- Debugging LangChain and LangGraph applications
- Prompt iteration and A/B testing
- LLM output evaluation and scoring
About Arize AI
Arize AI is a unified LLM observability and agent evaluation platform designed for AI application development and production management. The platform enables teams to build, observe, and improve AI systems through integrated development and production capabilities. Built on OpenTelemetry standards and open-source principles, Arize features 'adb,' a proprietary datastore optimized for generative AI workloads with real-time ingestion and sub-second query capabilities. The platform includes an agent framework for building and debugging AI agents, comprehensive tracing for full visibility into LLM application flows, automated evaluators with custom evaluation models, and Alyx, an AI engineering agent that assists with debugging and development. Arize offers experiment testing and optimization capabilities, production monitoring and alerting, a prompt playground for optimization, and data annotation tools. With impressive scale processing 1 trillion spans, 50 million evaluations per month, and 5 million monthly downloads of Phoenix OSS, Arize serves notable clients including DoorDash, Instacart, Reddit, Roblox, Uber, and Booking.com.
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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