Test, simulate, evaluate, and monitor LLM-powered agents end-to-end.
From $4/mo (free plan available)FreemiumAI & Automation
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Tracked since2026
The Bottom Line
- Price
- From $4/mo (free plan available). Compare plans
Key facts
- End-to-end testing and monitoring for LLM-powered agents.
- Integrated platform for evaluation, observability, and prompt optimization.
- Open-standard, framework-agnostic with an AI Gateway for cost control.
Pros
- Comprehensive solution for LLM agent lifecycle management (testing, evaluation, monitoring).
- Promotes open standards and avoids vendor lock-in with OpenTelemetry/OTLP-native design.
- AI Gateway provides robust cost control, governance, and security features.
- Supports both cloud and local deployment options for flexibility.
- Integrates with GitHub for version control of prompts and collaboration.
Cons
- Local setup can involve significant disk space for optional evaluators (e.g., Presidio at ~670MB).
- The complexity of managing multiple services (Postgres, Redis, ClickHouse) in local setup might be challenging for some users.
- The documentation does not explicitly mention support for all possible LLM providers beyond OpenAI/Anthropic compatibility for the gateway.
What is Claude Code usage tracking by LangWatch?
LangWatch is a platform designed for LLM evaluations and AI agent testing. It helps teams test, simulate, evaluate, and monitor LLM-powered agents throughout their lifecycle, both before release and in production. The platform is built for organizations that require robust regression testing, realistic simulations, and production observability without the need to develop custom tooling.
LangWatch enables end-to-end agent simulations, allowing users to run scenarios against their full stack, including tools, state, user simulators, and judges, to identify where and why agents fail. It integrates evaluation, observability, and prompt optimization into a single workflow, facilitating a cycle of tracing, dataset creation, evaluation, prompt/model optimization, and re-testing. The platform is OpenTelemetry/OTLP-native and agnostic to specific frameworks and LLM providers, promoting open standards and avoiding vendor lock-in. It also includes an AI Gateway for governance and cost control, offering features like virtual keys, hierarchical budgets, inline guardrails, and automatic fallback across providers.
Key Features
- End-to-end agent simulations
- Trace, dataset, evaluate, optimize, re-test loop
- OpenTelemetry/OTLP-native
- Framework and LLM-provider agnostic
- AI Gateway for governance and cost control
- OpenAI/Anthropic-compatible proxy
- Virtual keys and hierarchical budgets
- Inline guardrails
Pricing Plans
Free TrialPricing checked Sep 30, 2026
| Plan | Price | Details |
|---|---|---|
| Free | $0 USD per month |
+3 moreShow less
|
| Team | $4 USD per user/month |
+9 moreShow less
|
| Enterprise | Starting at $21 USD per user/month |
+7 moreShow less
|
Is Claude Code usage tracking by LangWatch worth the price?
Good value
The pricing for LangWatch's Claude Code usage tracking appears fair, especially with a generous Free tier and a competitive Team tier at $4 per user/month.
The Enterprise tier starting at $21 per user/month offers advanced features for larger organizations. This pricing structure is best for individual developers and small teams looking for cost-effective LLM monitoring, scaling up to enterprises needing robust compliance and management.
Hidden Costs & Gotchas
Package storage beyond free limits
Potential for higher Enterprise user minimums
Reviews
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Claude Code usage tracking by LangWatch FAQ
How does LangWatch ensure LLM provider agnosticism and prevent vendor lock-in?
LangWatch is designed to be OpenTelemetry/OTLP-native and framework-agnostic. This means it can integrate with various LLM providers and frameworks without requiring specific glue code, allowing users to switch or combine different LLMs as needed.
What specific functionalities does the AI Gateway offer for cost control and governance?
The AI Gateway acts as an OpenAI/Anthropic-compatible proxy, providing features like virtual keys, hierarchical budgets, inline guardrails, and automatic fallback across providers. It also supports Anthropic's cache_control passthrough, all designed to manage usage, costs, and security effectively.
Can LangWatch be deployed entirely on-premises, and what are the requirements for a local setup?
Yes, LangWatch supports local deployment. The fastest way is via Node.js, which installs necessary components like uv, Postgres, Redis, ClickHouse, the AI gateway binary, and the Langy assistant runtime into a local directory. Alternatively, it can be set up using Docker Compose with a provided example environment file.
What are the implications of enabling optional evaluators like Presidio or Lingua in a local LangWatch setup?
Enabling optional evaluators such as Presidio (for PII detection) or Lingua (for language detection) significantly increases the disk space required for the local installation. For instance, Presidio adds approximately 670MB for its language model, which is larger than the rest of the evaluator environment combined.
How does LangWatch facilitate collaboration among team members for improving AI agents?
LangWatch includes features for collaboration such as the ability to review runs, annotate failures, and queue tasks for domain experts to label edge cases. It also integrates with GitHub to keep prompts in version control, linking prompt versions directly to traces for systematic improvement.
Source: github.com