How do I monitor all model requests from one place?

You can usually monitor all model requests from one place in one of these ways:

1. Provider dashboard / usage logs

   • Most model providers have a dashboard showing requests, tokens, latency, errors, and costs.
   • Good for quick visibility, less good for deep app-level tracing.

2. Centralized logging in your app

   • Route every model call through a single wrapper/service.
   • Log request metadata there: model name, prompt size, response time, status, token usage, user/session ID, and cost.
   • Send logs to one system like Datadog, Grafana Loki, ELK/Elastic, Splunk, or CloudWatch.

3. API gateway / proxy

   • Put a proxy in front of all model requests.
   • This is the cleanest “one place” approach if you have multiple apps or teams.
   • The proxy can enforce auth, rate limits, logging, redaction, retries, and fallbacks.

4. Observability tooling with tracing

   • Use OpenTelemetry or a similar tracing setup.
   • Create a span for each model request so you can see the full path from user action → app logic → model call.

5. LLM-specific observability platforms

   • Tools like Langfuse, Helicone, OpenLIT, Arize Phoenix, or similar platforms are built for this.
   • They often give:
     • request/response logs
     • prompt versioning
     • token/cost tracking
     • latency/error dashboards
     • filtering by user, environment, model, or endpoint

If you want the simplest answer:
centralize all model calls behind one wrapper or proxy, and send logs/traces to one observability tool.

If you tell me your stack (OpenAI API, Anthropic, Azure OpenAI, LangChain, Python/Node, etc.), I can recommend the easiest setup.