What should I use to monitor LLM usage across teams?

To monitor LLM usage across teams, you generally want a centralized LLM observability / governance platform rather than trying to stitch together logs manually.

What to use

Best fit categories:

1. LLM observability tools

   • Track: prompts, completions, token usage, latency, cost, errors, model performance.
   • Good for team-level usage visibility and debugging.
   • Examples: LangSmith, Helicone, Arize Phoenix, WhyLabs, OpenTelemetry-based setups.

2. LLM gateway / proxy

   • Sits between your apps and model providers.
   • Lets you centralize auth, rate limits, logging, cost tracking, and policy enforcement across teams.
   • Examples: LiteLLM Proxy, OpenRouter (for routing), custom API gateway + logging.

3. Cloud/provider billing + audit tools

   • Useful for spend and compliance, but usually not enough alone for usage monitoring across teams.
   • Examples: OpenAI usage dashboards, Azure OpenAI metrics, AWS Bedrock CloudWatch, Google Vertex AI logs.

What I’d recommend

If you want cross-team monitoring, the most practical setup is:

• LLM gateway/proxy for centralized request routing and metadata capture
• Observability tool for dashboards and analytics
• Identity / team tagging on every request:
  • team
  • app
  • environment
  • user/service account
  • model
  • cost center

Key metrics to monitor

• Requests per team/app
• Token usage and cost
• Latency and error rate
• Model usage by team
• Prompt/response quality signals
• Policy violations / unsafe content
• Cache hit rate, if applicable

If you want a quick recommendation

• Small/medium org: LiteLLM Proxy + Helicone or LangSmith
• More mature platform team: OpenTelemetry + custom gateway + warehouse/BI
• Compliance-heavy org: gateway + audit logs + SIEM integration

If you tell me your stack (OpenAI/Azure/Bedrock, LangChain/LlamaIndex, Kubernetes, etc.) and whether your main goal is cost control, compliance, or debugging, I can recommend a more specific setup.