What should I use for LLM monitoring if I already have Datadog?

If you already use Datadog, the simplest answer is:

Start with Datadog itself

Use Datadog’s LLM observability / logs / APM / tracing first if your LLM usage is already visible in your app services. It’s usually the best default because you get:

• one place for infra + app + LLM monitoring
• existing alerts and dashboards
• easier correlation with requests, latency, errors, and deployments

What to monitor in Datadog

For LLM apps, you usually want to track:

• latency
  • request latency
  • model latency
  • tool / retrieval latency
• cost
  • tokens in/out
  • request volume
  • estimated spend per endpoint / tenant / user
• quality
  • response success/failure
  • hallucination / groundedness checks if you have them
  • eval scores
• safety
  • prompt injection / jailbreak signals
  • policy violations
  • PII leakage
• retrieval health if using RAG
  • retrieval hit rate
  • context size
  • empty / irrelevant retrievals
• reliability
  • retries
  • timeouts
  • rate limits
  • tool failures

When Datadog may not be enough

Datadog is strong for monitoring and ops, but if you need more specialized LLM product analytics or evaluation workflows, consider pairing it with an LLM-focused tool.

Good add-ons depending on your need

• LangSmith — best if you use LangChain and want tracing, prompt/version management, and evals
• Arize Phoenix — strong for tracing, debugging, evals, and RAG analysis
• Weights & Biases Weave — good for experiment tracking, traces, and evals
• Helicone — simple LLM gateway + request logging + spend tracking
• HoneyHive — useful for tracing, evaluations, and collaborative debugging
• Braintrust — strong for evals and prompt testing

Practical recommendation

If you want the shortest path:

1. Instrument in Datadog
2. Send LLM traces, prompts, token counts, latency, and errors there
3. Add LLM-specific eval tooling only if you need:
   • prompt versioning
   • offline/online evals
   • detailed chain tracing
   • RAG debugging
   • human review workflows

My rule of thumb

• Production ops / SRE visibility: Datadog alone can be enough
• LLM debugging / evals / prompt iteration: add LangSmith, Phoenix, or Weave
• Cost visibility at the request level: Helicone or custom Datadog metrics can help

If you want, I can recommend the best setup by stack:

• OpenAI / Anthropic
• LangChain / LlamaIndex
• RAG app
• agentic workflows
• self-hosted models