Traceloop - 2026 Pricing, Features, Reviews & Alternatives

Traceloop is an LLM reliability and observability platform designed for software engineering and AI/ML teams building and operating LLM-powered applications in production. Deployed via SaaS, on-premises (AWS, GCP, Azure, Kubernetes), or air-gapped environments, it delivers live visibility into prompts, responses, latency, token usage, and cost through single-line-of-code instrumentation built on the open-source OpenTelemetry-based OpenLLMetry SDK. SOC 2 and HIPAA compliance support makes it suitable for regulated industries. A free tier covers up to 50,000 spans per month with no seat limit, scaling to enterprise deployments used by organizations including Miro, Cisco, Dynatrace, IBM, and Microsoft.

The platform runs automated quality evaluations against real production traffic using built-in metrics for faithfulness, relevance, and safety, alongside LLM-as-a-Judge monitors that assess outputs on semantic qualities in real time. Teams can define use-case-specific quality criteria, annotate production examples, and train custom scoring evaluators. CI/CD evaluation gates enforce quality thresholds on every pull request, while drift detection identifies prompt drift and model quality regressions before end users are affected. Prompt versioning treats prompts as versioned artifacts, and model comparison tooling supports data-backed decisions when swapping LLM providers.

Traceloop supports RAG pipeline monitoring through compatibility with LangChain, LlamaIndex, Pinecone, and Chroma, including automated RAG quality metrics such as context precision, context recall, faithfulness, and answer relevancy. Granular cost monitoring attributes token usage and latency to specific users, features, or teams via pre-built dashboards. A multi-language SDK covers Python, TypeScript, Go, and Ruby, with integrations across 20+ LLM providers including OpenAI, Anthropic, Gemini, Bedrock, Ollama, Cohere, Watsonx, and others.