LangWatch

What is LangWatch?

LangWatch is a comprehensive LLM operations platform designed to help AI teams manage the entire lifecycle of large language model (LLM) applications. It integrates seamlessly with any tech stack to provide monitoring, evaluation, and optimization tools that ensure AI quality, safety, and performance. By automating quality checks, enabling human-in-the-loop evaluations, and offering detailed analytics, LangWatch helps businesses reduce AI risks such as hallucinations and data leaks while accelerating deployment from proof-of-concept to production. The platform supports continuous improvement through visual experiment tracking, customizable evaluations, and alerting systems, making it ideal for teams aiming to build reliable and compliant AI products.

Key Features

• Comprehensive LLM Monitoring

  Automatically logs inputs, outputs, latency, costs, and internal AI decision steps to provide full observability and facilitate debugging and auditing.

• Automated Quality Evaluations

  Runs real-time, customizable quality checks and safety assessments with over 30 built-in evaluators and supports human expert reviews.

• Optimization Studio

  Visual drag-and-drop interface to create, test, and refine LLM pipelines with automatic prompt generation and experiment version control.

• Alerts and Dataset Automation

  Real-time alerts on performance regressions and the ability to automatically generate datasets from annotated feedback for continuous model improvement.

• Custom Analytics and Business Metrics

  Enables building tailored dashboards and graphs to track AI performance indicators like response quality, cost, and user interactions.

• Enterprise-Ready and Flexible Deployment

  Open-source, model-agnostic platform with ISO compliance, role-based access control, and options for self-hosting or cloud deployment.

Use Cases

• AI Quality Assurance : Ensure consistent, safe, and accurate AI outputs by automating quality checks and involving domain experts in evaluation workflows.
• Risk Mitigation : Detect and prevent AI hallucinations, data leaks, and off-topic responses to safeguard sensitive information and brand reputation.
• Performance Monitoring : Track cost, latency, and error rates over time with customizable analytics to optimize AI system efficiency and user experience.
• Model Optimization : Use the Optimization Studio to iterate on prompt engineering and pipeline configurations, accelerating deployment from prototype to production.
• Human-in-the-Loop Evaluation : Integrate domain experts seamlessly to provide manual feedback and annotations, improving AI reliability and closing the feedback loop.

FAQs