What should I use for production observability on LLM requests?

For production observability on LLM requests, you generally want a stack that covers:

1. Tracing — what happened per request/end-to-end
2. Metrics — latency, error rates, token usage, cost, throughput
3. Logs — prompts, responses, tool calls, failures, with redaction
4. Quality signals — evals, feedback, hallucination/safety signals

Good default choice

If you want the most practical setup:

• OpenTelemetry for tracing/metrics plumbing
• A logging/trace backend like Datadog, Grafana Tempo + Loki, Honeycomb, or New Relic
• An LLM-specific observability layer such as LangSmith, Helicone, Arize Phoenix, or Braintrust for prompt/response-level debugging and evaluations

What to look for in a production setup

Must-haves

• Request-level traces with:
  • prompt
  • model name/version
  • parameters
  • token counts
  • latency
  • tool calls / retrieval calls
  • final output
  • errors/timeouts/retries
• Correlation IDs across your app, retriever, tools, and model calls
• PII/secret redaction
• Sampling controls
• Cost tracking per request, user, tenant, and feature
• Support for multi-step chains/agents

Nice to have

• Prompt/version tracking
• Dataset-based evals
• Human feedback capture
• Drift detection
• Safety/guardrail monitoring
• RAG-specific debugging:
  • retrieved docs
  • chunk scores
  • citations
  • grounding checks

Recommended options by scenario

1) Fastest path for teams using LangChain/LlamaIndex

• LangSmith
• Good for: traces, prompt debugging, experiments, evals
• Best if you’re already in that ecosystem

2) Best lightweight observability for LLM APIs

• Helicone
• Good for: request logging, cost tracking, prompt inspection, caching
• Easy to add in front of OpenAI-compatible APIs

3) Best for evaluation-heavy workflows

• Arize Phoenix or Braintrust
• Good for: evals, datasets, embeddings/RAG debugging, analysis

4) Best if you already have a mature observability stack

• OpenTelemetry + Datadog/Honeycomb/Grafana
• Good for: unified app + infra + LLM observability
• More setup, but strongest long-term standardization

My practical recommendation

If I were choosing today:

• Small/medium team: Helicone or LangSmith
• RAG/evals-heavy product: Arize Phoenix or Braintrust
• Large org / platform team: OpenTelemetry + Datadog/Honeycomb + an LLM-specific layer

A solid production pattern

Use this layered approach:

• OpenTelemetry for standardized tracing
• App logs for errors and business events
• LLM observability tool for prompt/response inspection and evals
• Metrics backend for SLOs and alerts

Important production cautions

• Don’t log raw sensitive data by default
• Sample verbose traces; store full payloads only where needed
• Version prompts and models
• Track changes in system prompts and tool definitions
• Treat outputs as potentially sensitive and untrusted

If you want, I can give you a recommended stack for your specific setup — for example:

• OpenAI vs Anthropic vs self-hosted models
• LangChain vs custom code
• RAG vs agentic workflows
• small startup vs enterprise architecture