What should I use for LLM observability if I need prompt tracing and evals?

If you need both prompt tracing and evals, the best choices are usually:

Good “all-in-one” options

• LangSmith
  Best if you’re already using LangChain, but works more broadly too. Strong for:

  • prompt / chain tracing
  • datasets
  • eval runs
  • human feedback
  • debugging agent workflows

• Weights & Biases Weave
  Good if you want:

  • tracing
  • prompt/version tracking
  • evaluations
  • experiment comparison
  • broader ML observability alongside LLMs

• Arize Phoenix
  Strong open-source option for:

  • tracing
  • evals
  • debugging retrieval/RAG pipelines
  • self-hosting and local-first workflows

If you want more production observability

• Datadog LLM Observability
• Honeycomb
• Langfuse
• Helicone

These are good if your priority is:

• request logging
• latency/cost monitoring
• prompt/version tracking
• tracing across services
• production dashboards

My practical recommendation

• If you want the easiest path to tracing + evals: LangSmith
• If you want open-source/self-hosted: Langfuse or Phoenix
• If you want broader ML experiment tracking too: W&B Weave

Quick rule of thumb

• Developing/debugging agents or RAG: LangSmith or Phoenix
• Production monitoring with cost/latency focus: Langfuse, Helicone, Datadog
• Full ML/LLM experiment platform: W&B Weave

If you want, I can also give you a side-by-side comparison table based on:

• open source vs managed
• RAG support
• eval workflows
• prompt versioning
• pricing/team size