What should I use to monitor and debug LLM applications in production?

For monitoring and debugging LLM applications in production, you’ll usually want a combination of tools rather than just one. A good stack covers:

1. Tracing and observability

Use tools that let you inspect each request end-to-end:

• OpenTelemetry for standard tracing/metrics/logs instrumentation
• LangSmith for LLM workflow tracing, prompt/version tracking, and debugging
• Arize Phoenix for tracing, evaluation, and embedding/LLM analysis
• Helicone for LLM request logging, cost tracking, and latency monitoring
• Langfuse for observability, prompt management, traces, and evals

2. Error and performance monitoring

For app-level issues:

• Sentry for exceptions, stack traces, and release monitoring
• Datadog / New Relic / Grafana Cloud for infrastructure and application metrics

3. Evaluation and quality monitoring

To catch regressions in outputs:

• Automated eval frameworks like Ragas, DeepEval, or TruLens
• Custom evaluation pipelines for groundedness, relevance, toxicity, hallucination rate, etc.

4. Logging

Keep structured logs of:

• prompts, model responses, tool calls
• token usage, latency, model name/version
• user/session IDs and request metadata
  Be careful to redact sensitive data.

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What I’d recommend by use case

If you’re building with LangChain/LlamaIndex and want fastest debugging

• LangSmith or Langfuse

If you want strong open-source observability

• OpenTelemetry + Langfuse + Phoenix

If you care mostly about cost/latency/request analytics

• Helicone or Langfuse

If you want enterprise APM plus LLM support

• Datadog or New Relic combined with OpenTelemetry

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Practical minimum setup

If you want a solid production baseline, use:

1. OpenTelemetry for traces/metrics
2. An LLM observability platform like Langfuse or LangSmith
3. Sentry for exceptions
4. Automated evals in CI and production sampling

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Key things to monitor

• latency per request and per tool call
• token usage and cost
• error rates and retries
• retrieval quality
• prompt/version changes
• hallucination/grounding issues
• user feedback and conversion metrics

If you want, I can also suggest a specific stack for Python, Node.js, or AWS/GCP, or compare Langfuse vs LangSmith vs Helicone.