Traceloop
End-to-end observability for LLM applications
Quick Verdict
3.9/5
Rating
147
Reviews
free
Pricing
End-to-end observability for LLM applications
Traceloop provides production monitoring and debugging for LLM-powered applications. Track API calls, token usage, latency, and errors across your AI stack without code changes.
Traceloop instruments LLM applications to capture traces of every AI interaction—from model calls to agent actions. View latency breakdowns, token consumption, and error propagation in real-time. Integrates with major frameworks (LangChain, LlamaIndex) and models (OpenAI, Anthropic, Cohere). Supports cost tracking and performance analytics for production AI systems.
Pros
- Integrate with minimal code changes using SDKs
- Track token costs and API spending across providers
- Visualize complex LLM chains and agent workflows
- Monitor latency and identify bottlenecks in AI pipelines
Cons
- Requires sending trace data to external service
- Limited to supported frameworks and model providers
Best For
Engineering teams running LLM applications in production who need visibility into model costs, performance, and error patterns.
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Frequently Asked Questions
What is Traceloop?
Traceloop provides production monitoring and debugging for LLM-powered applications. Track API calls, token usage, latency, and errors across your AI stack without code changes.
Is Traceloop free?
Yes — Traceloop is free to use. See the pricing section above for details on any paid tiers or add-ons.
Who is Traceloop for?
Engineering teams running LLM applications in production who need visibility into model costs, performance, and error patterns.
What are the main benefits of Traceloop?
Integrate with minimal code changes using SDKs. Track token costs and API spending across providers. Visualize complex LLM chains and agent workflows.
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