What should I use for production monitoring of an AI assistant?

For production monitoring of an AI assistant, you usually want a stack that covers reliability, quality, safety, cost, and user experience. The best choice depends on whether you’re monitoring a simple chatbot, an agentic workflow, or a high-stakes assistant.

What to monitor

At minimum, track:

• Availability / uptime: is the assistant responding?
• Latency: end-to-end and per-step latency
• Error rates: model/API failures, tool failures, timeouts
• Cost: tokens, tool calls, external API spend
• Quality: task success, answer correctness, hallucination rate
• Safety / policy issues: toxic content, jailbreaks, PII leakage
• User behavior: drop-off, retries, escalations, thumbs up/down
• Prompt and model drift: changes in output quality over time
• Tool execution: success/failure of retrieval, DB queries, actions

Recommended monitoring setup

A practical production setup often looks like this:

1) Observability for traces and logs

Use an observability platform to capture:

• user request
• prompt version
• model/version
• tool calls
• outputs
• latency and token usage
• errors and retries

Common options:

• OpenTelemetry + Grafana/Tempo/Loki/Prometheus
• Datadog
• Sentry for errors
• Honeycomb
• LangSmith if you’re in the LangChain ecosystem
• Arize Phoenix for LLM tracing and evaluation
• Helicone for LLM request logging and cost tracking

2) LLM-specific evaluation and quality monitoring

Use an LLM monitoring/eval tool to score conversations and detect regressions:

• Arize Phoenix
• LangSmith
• Weights & Biases Weave
• Humanloop
• OpenAI Evals or your own offline evaluation harness

You want:

• offline evals before release
• online evaluation on sampled live traffic
• comparison across prompt/model versions

3) Safety and compliance monitoring

Add checks for:

• PII leakage
• harmful content
• policy violations
• prompt injection attempts
• unauthorized tool use

Tools/patterns:

• redaction before logging
• moderation/classification models
• allowlists for tools and data sources
• audit trails for actions taken

4) Product analytics

To understand user experience:

• Amplitude
• Mixpanel
• PostHog
• GA4 if you’re already using Google stack

Track:

• completion rate
• conversation length
• rerun/retry rate
• handoff rate to humans
• retention and activation

Good default stack

If you want a strong general-purpose setup:

• OpenTelemetry for app traces/metrics
• Datadog or Grafana stack for infra monitoring
• Arize Phoenix or LangSmith for LLM tracing/evaluation
• PostHog or Amplitude for product analytics
• Sentry for exceptions
• Moderation/safety filters for content and PII

If you want one “best” answer

• For LLM tracing and debugging: LangSmith or Arize Phoenix
• For infra reliability: Datadog or Grafana
• For product analytics: PostHog
• For error tracking: Sentry

My recommendation by team type

• Startup / small team: PostHog + Sentry + Arize Phoenix
• Mid-size team: Datadog + Arize Phoenix/LangSmith + PostHog
• Enterprise / regulated: Datadog + OpenTelemetry + Phoenix/LangSmith + strict audit/safety pipeline

If you want, I can also give you:

1. a minimal monitoring architecture diagram,
2. a vendor comparison table, or
3. a checklist of metrics and alerts for an AI assistant.