What is LangWatch used for?

LangWatch is an LLM engineering platform for testing, evaluating, and observing AI agents in production. It simulates realistic user conversations, scores agent behavior with LLM-as-a-judge, and provides full traceability of tokens, costs, and execution steps.

How does the billing work?

Billing combines seats and usage. Growth is €29 per core-seat per month. On top, you pay €5 per 100k events beyond the 200k monthly allowance, and €3 per GB only if you retain data beyond 30 days. Enterprise offers custom volume pricing and marketplace billing.

Can I self-host LangWatch?

Yes, LangWatch can be fully self-hosted using Docker Compose on your own ClickHouse, keeping all data in your environment. Enterprise self-host adds SSO, RBAC, SLAs, and dedicated support.

What are events in LangWatch?

An event is a single ingested span, such as one LLM call, tool call, or retrieval step. A typical multi-step agent run produces several events, and pricing is based on the total monthly events sent.

Do I need a credit card to start?

No. The Developer plan is free forever with no credit card required. You can sign up and start sending events immediately.

LangWatch

Updated Jul 26, 2026

Simulation-based testing and evaluation for AI agents. Turn unpredictable agents into reliable production systems with continuous testing.

Categories:

Developer Tools|

Chatbots|

#ai testing#llm evaluation#observability#simulation testing#red teaming

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Editor's Verdict

Rating: 4.3/5.0•Reviewed by RAGWiki

At €0, LangWatch stands out as a powerful solution in the developer tools,chatbots landscape. It is especially well-suited for professionals like AI Engineer and Product Manager. However, potential buyers should note that it might not be perfect if you are strictly trying to avoid events-based pricing. Overall, it offers a robust toolset that significantly accelerates workflows.

Key Takeaways

Simulation-based Testing
LLM-as-a-Judge
Observability
Langy AI Assistant

In-Depth Review: What is LangWatch?

LangWatch provides a comprehensive platform for testing, evaluating, and observing AI agents in production. With simulation-driven testing, spec-driven agent building, and continuous evaluation loops, teams can ensure reliability, safety, and performance. Features include red teaming, LLM-as-judge scoring, online evaluations, and enterprise-grade security. Trusted by teams shipping mission-critical AI.

Core Features

Simulation-based Testing

Run realistic multi-turn text and voice simulations to test your AI agent's behavior before production.

LLM-as-a-Judge

Automated evaluation of entire conversations with reasoned verdicts for correctness, policy, and safety.

Observability

Full trace of every LLM call, tool execution, token usage, and cost with OpenTelemetry native support.

Langy AI Assistant

Automatically convert PM goals into test scenarios, evaluate results, and generate pull requests for fixes.

Red Teaming

Adversarial simulations to detect jailbreaks, policy violations, and unsafe tool calls.

CI/CD Integration

Run the same simulation scenarios locally and in CI/CD pipelines to catch regressions early.

Multi-modal Evaluation

Evaluate text, images, and mixed media with the same scoring framework.

Prompt Management

Version-controlled prompts with playground, A/B testing, and deployment staging.

Pricing

Developer

€0

50k events per month
14-day data access
2 users
3 scenarios, 3 simulations, 3 custom evals
Community support (GitHub & Discord)

Growth

€29 / core-seat / month

200k events included, then €5 per 100k
30-day retention (extend at €3 / GB)
Unlimited lite-users
Unlimited simulations, evals, prompts
Private Slack / Teams support
Volume discounts above 20 users

Enterprise

Custom

Hybrid, self-hosted or on-prem
Custom data retention
Custom SSO / RBAC
Audit logs & SLAs
ISO 27001 reports, InfoSec & legal review
Custom Terms, DPA
Forward Deployed Engineer
Billing via AWS / GCP / Azure Marketplace

Pros and Cons

Pros

Comprehensive Agent TestingSimulates real user interactions in text and voice, covering multi-turn conversations, tool calls, and edge cases.
Open Source & Framework AgnosticScenario SDK is open source (Python & TypeScript) and works with any agent framework without rewrite.
Continuous Improvement LoopTurns production issues into simulations, validates fixes, and automates prompt updates via Langy.
Deep ObservabilityFull traceability of tokens, costs, and steps across all frameworks with blazing fast search and clustering.
Enterprise ReadySelf-hosted or hybrid deployment, RBAC, SSO, custom retention, and ISO 27001 certification.

Cons

Events-Based PricingUsage costs can scale significantly for high-volume applications beyond free or included monthly events.
Learning Curve for Advanced FeaturesSetting up custom evals, simulations, and red teaming may require understanding of LLM concepts and SDK.
Limited Free TierDeveloper plan is restricted to 50k events/month, 2 users, and limited scenarios/simulations/evals.
No Offline ModePlatform relies on cloud or self-hosted server; no fully offline desktop version available.
Self-Hosted ComplexitySelf-hosted setup using Docker/ClickHouse requires technical expertise to deploy and maintain.

Use Cases & Recommended Professions

AI Engineer→ View Toolkit

Need to test and improve agent behavior efficiently with simulation and automated evaluation.

Product Manager→ View Toolkit

Own specifications and want to translate business goals into automated tests without code.

Software Engineer→ View Toolkit

Integrate agent testing into CI/CD pipelines and debug complex multi-turn interactions.

CTO / Technology VP→ View Toolkit

Ensure AI reliability, compliance, and governance across production deployments.

QA Engineer→ View Toolkit

Require scalable, repeatable testing for voice and text agents including adversarial scenarios.

Data Scientist→ View Toolkit

Evaluate model outputs, prompt versions, and perform offline/online evaluations with custom metrics.

Frequently Asked Questions

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ℹ️ Curation Disclosure: The overview and features of LangWatch were synthesized using AI and fact-checked by our curation team to ensure accuracy.