Traceloop

Traceloop offers an observability and reliability platform for applications built on large language models. The product captures traces of prompts, responses, latency and related telemetry with a single line of instrumentation code, and turns raw LLM logs into structured quality signals. It is positioned to address two problems the company describes: ad-hoc, spreadsheet-based quality scoring that leaves results in doubt, and silent model drift where output quality degrades before teams detect it.

On top of tracing, Traceloop runs evaluations on production data. Built-in metrics such as faithfulness, relevance and safety are applied automatically without configuration, and teams can define custom quality criteria, annotate real examples and train a custom evaluator that scores outputs according to their own standards. Evaluations can be executed in continuous-integration workflows (for example, on every pull request) or in real time as the application runs, with thresholds used to gate releases. The company frames this as a continuous feedback loop between evaluations and monitoring.

Traceloop is built on OpenTelemetry and ships OpenLLMetry, its open-source SDK, published on GitHub. Instrumentation is available for Python, TypeScript, Go and Ruby, either through OpenLLMetry or through Hub, the company's native OpenTelemetry-based gateway. The platform supports more than 20 model providers (including OpenAI, Anthropic, Gemini, Bedrock and Ollama), vector databases such as Pinecone and Chroma, and orchestration frameworks including LangChain, LlamaIndex and CrewAI.

Business model

Traceloop sells a developer platform for LLM observability and evaluation, offered as a hosted service with a free entry tier and published pricing, alongside self-managed deployments. An open-source SDK, OpenLLMetry, serves as the instrumentation layer and adoption channel for the commercial platform.

Latest developments

Traceloop's website announces that the company is joining ServiceNow.