phoenix vs langwatch
LLM application observability and evaluation compared
phoenix and langwatch help teams test and monitor LLM applications and AI agents. phoenix focuses on tracing, evaluation, and experimentation in a self-hosted platform, while langwatch also includes simulation testing, an AI gateway, and governance workflows, with cloud and self-hosted options.
phoenix: Observe and Evaluate AI Applications
Arize Phoenix is a self-hosted platform for tracing, evaluating, and troubleshooting LLM applications. It helps AI engineers inspect runtime behavior and test changes to prompts, models, and retrieval.
langwatch: Test and Monitor LLM Applications and AI Agents
LangWatch helps teams evaluate, test, and monitor LLM applications and AI agents in production. It combines observability, simulation testing, prompt management, an AI gateway, and governance, with cloud and self-hosted options.
| phoenix | langwatch | |
|---|---|---|
| Language | Python | TypeScript |
| License | NOASSERTION | Apache-2.0 |
| Stars | 11.7k | 4.9k |
| Forks | 1.2k | 405 |
| Last analyzed | Oct 3, 2026 | Oct 3, 2026 |
Key differences
- phoenix is written in Python, while langwatch is written in TypeScript.
- phoenix emphasizes OpenTelemetry-based tracing, datasets, and experiments; langwatch adds simulation testing, including for voice agents, and prompt management.
- langwatch offers an AI gateway with provider routing, virtual keys, and budgets, plus organizational AI governance workflows. phoenix focuses on application traces and evaluation.
- phoenix can run on a local machine, in containers, or in the cloud. langwatch offers cloud and self-hosted options, with separate guidance for production self-hosting.
- phoenix metadata reports NOASSERTION, while its README describes Elastic License 2.0. langwatch describes its core as Apache-2.0, with commercial licensing required for production use of enterprise modules.
- The supplied repository figures list phoenix at 11.7k stars and 1.2k forks, and langwatch at 4.9k stars and 405 forks.
Choose phoenix if you…
- need self-hosted tracing and evaluation across LLM calls, retrieval, and agent workflows.
- want to create versioned datasets and compare prompt, model, or retrieval changes through experiments.
- prefer a Python project and can review its license terms before use.
Choose langwatch if you…
- need simulation tests for agent behavior, including voice agents, alongside production monitoring.
- want a shared AI gateway with provider routing, virtual keys, and usage budgets.
- need to track coding-agent sessions or support organizational AI governance workflows.
Compare the numbers side by side, or add more projects
This comparison is generated with AI from the OSRepos analyses of both projects. Always check each project's repository and documentation before choosing.