Provides a monitoring and debugging platform for large language model (LLM) applications, enabling real-time detection of output inconsistencies, hallucinations, and performance issues. The tool supports 22 LLM providers, offering features like backtesting, prompt optimization, and automated change rollouts to ensure reliable and high-quality model performance.
Funding
$500K raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Monitoring and debugging large language model (LLM) applications is challenging due to the difficulty in detecting output inconsistencies, hallucinations, and performance degradation in real-time. Existing methods often rely on manual checks and ad-hoc metrics, leading to delayed issue detection and unreliable model performance.
Solution
Traceloop offers a comprehensive LLM observability platform that enables real-time monitoring, debugging, and continuous improvement of LLM applications. By seamlessly integrating with over 20 LLM providers and various vector databases and frameworks, Traceloop provides immediate visibility into prompts, responses, latency, and other critical metrics with just a single line of code. The platform automates quality checks using built-in metrics for faithfulness, relevance, and safety, while also allowing users to define custom evaluation metrics tailored to their specific use cases. Traceloop facilitates proactive issue detection, performance optimization, and confident deployments, ensuring consistent and reliable LLM application performance.
Target Audience
Traceloop is designed for AI engineers, machine learning operations (MLOps) teams, and organizations building and deploying LLM-powered applications who require robust monitoring, debugging, and evaluation capabilities to ensure model reliability and performance.
Features
- Real-time monitoring of LLM application performance, including prompts, responses, and latency
- Automated quality checks using built-in metrics such as faithfulness, relevance, and safety
- Custom evaluator training for defining quality metrics specific to individual use cases
- Integration with 20+ LLM providers, including OpenAI, Anthropic, and Google Gemini
- Compatibility with vector databases like Pinecone, Chroma, Qdrant, and Weaviate
- Support for frameworks such as LangChain, LlamaIndex, Haystack, and CrewAI
- OpenTelemetry-based architecture with OpenLLMetry SDK for transparency and interoperability
- Enterprise-ready with SOC 2 and HIPAA compliance, supporting cloud, on-premise, and air-gapped deployments