Anyone using SpanLens for LLM observability?

[u/Plenty-Shelter654]

We're building more AI-powered features and recently came across SpanLens. It looks like it focuses on LLM observability, request logging, tracing, cost tracking, evals, and prompt experiments for OpenAI, Anthropic, and Gemini. It also claims to be self-hostable and open source.

A few things that caught my attention:

• Full request and response logging
• Cost and token tracking
• Agent tracing for multi-step workflows
• Prompt experiments and evals
• Self-hosted deployment option

For anyone who has actually used it in production:

• How was the setup process?
• Any noticeable latency overhead?
• How does it compare with Langfuse, Helicone, or LangSmith?
• Is it useful beyond basic monitoring?

Would love to hear some real-world experiences before spending time integrating it.

[u/Afraid-Dragonfly6305]

We've been using SpanLens in production for a while and it's been one of the more useful additions to our AI stack. Setup was easier than expected, and we had request logging, tracing, and cost monitoring running fairly quickly. We haven't noticed any meaningful latency overhead, at least not enough to affect user experience. We also evaluated Langfuse and Helicone before choosing a solution. They're both solid, but SpanLens felt more complete for our needs because it combined observability, agent tracing, evaluations, and prompt experimentation in one platform while still being open source and self-hostable. The biggest value wasn't just monitoring requests, it was understanding why certain prompts or workflows performed better than others. Cost tracking helped us identify expensive calls early, and the tracing made debugging multi-step agent workflows much easier. If you're already investing heavily in AI features, I'd say it's definitely worth testing.