LangWatch vs Phoenix: Observability, Prompts & Evals Comparison
Compare LangWatch and Phoenix side by side. Both are tools in the Observability, Prompts & Evals category.
Updated March 27, 2026
The short answer
Choose LangWatch if unique agent simulation testing via Scenario framework — enables multi-turn, stateful agent testing unmatched by LangSmith or Langfuse.
Choose Phoenix if open-source with active development by Arize.
Quick Comparison
| Category | Observability, Prompts & Evals | Observability, Prompts & Evals |
| Pricing | Open Source + Cloud | Open Source |
| Best For | AI teams building and testing LLM-powered agents | Engineering teams building agent and RAG systems who want OpenTelemetry-native observability with both self-hosted and managed options |
| Website | langwatch.ai | phoenix.arize.com |
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Pros & Cons: LangWatch vs Phoenix
- +Unique agent simulation testing via Scenario framework — enables multi-turn, stateful agent testing unmatched by LangSmith or Langfuse
- +Fully open-source with MIT license and no feature gating on self-hosted deployments
- +All-in-one platform combining tracing, evaluation, prompt management, and PII redaction
- +Framework-agnostic with OpenTelemetry-native integration across all major agent frameworks
- +Enterprise-ready with ISO 27001, SOC2, GDPR compliance, and on-prem deployment options
- −Very small team (~5 employees) with only EUR 1M pre-seed — long-term viability less proven than well-funded competitors
- −Smaller community and fewer tutorials compared to LangSmith or Langfuse ecosystems
- −Self-hosting requires Kubernetes/Helm for production with known issues on reverse proxy deployments
- −Only supports LLM-as-a-judge evaluations — no heuristic or rule-based evaluation options
- +Open-source with active development by Arize
- +OpenTelemetry-native (no proprietary trace format lock-in)
- +Strong evaluator library out of the box
- +Both self-hosted and managed cloud options available
- +Upgrade path to full Arize enterprise platform
- −Smaller community than Langfuse for open-source-first teams
- −Cloud version's enterprise pricing is contact-sales only
- −UI and feature set tilt toward ML engineers more than application developers
Pricing: LangWatch vs Phoenix
- · All platform features
- · 50K logs/month
- · 14-day data retention
- · 2 users
- · 200K events included
- · EUR 0.0005/additional event
- · 30-day retention
- · Unlimited lite-users
- · On-prem/hybrid/self-hosted
- · Custom retention
- · SSO/RBAC
- · Audit logs
When to Choose LangWatch vs Phoenix
- Agent testing before deployment
- Hallucination detection
- Regression testing for LLM apps
- OpenTelemetry-native LLM observability
- Hallucination detection for RAG
- Experiment tracking and golden-set evaluation
About LangWatch
LangWatch is an open-source LLMOps platform focused on testing, evaluating, and monitoring AI agents. Founded in 2023 in Amsterdam by Rogerio Chaves (CTO, ex-Booking.com, ex-Lightspeed) and Manouk Draisma (CEO), the company raised EUR 1M in pre-seed funding led by Passion Capital with participation from Volta Ventures and Antler.
LangWatch's standout differentiator is its Scenario framework — an open-source agent testing library (804 GitHub stars) that enables multi-turn, simulation-based testing of AI agents. Unlike static input/output evaluations, Scenario provides a User Simulator Agent that generates realistic conversations against your agent, with a Judge Agent evaluating pass/fail at every turn. Available in Python, TypeScript, and Go, it works with any agent framework (LangGraph, CrewAI, Pydantic AI, OpenAI, Vercel AI SDK, Google ADK).
The platform combines OpenTelemetry-native tracing, custom evaluators with real-time scoring, prompt and model management with version control, and dataset management that converts production traces into reusable test cases. LangWatch processes 900K+ daily evaluations, has 780K+ monthly package installs, and holds ISO 27001 and SOC2 certifications. It supports self-hosted deployment via Docker and Kubernetes with no feature gating.
About Phoenix
Phoenix is the open-source observability and evaluation platform built by Arize AI for LLM and agent applications. It is OpenTelemetry-native, which means traces written through Phoenix can flow into any OTel-compatible backend in addition to Phoenix's own UI. The platform includes built-in evaluators for hallucination detection, retrieval relevance, and QA correctness, plus dataset management and prompt playground features. Phoenix can be deployed via Docker for self-hosting or used in Arize's managed cloud. The open-source core makes it attractive to teams that want to inspect and customize the observability layer, while the integration with the full Arize platform provides an upgrade path for organizations that need enterprise features like RBAC, SSO, and SLA-backed support.
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.
Browse all Observability, Prompts & Evalstools →