What should I use to monitor token usage and API cost for an LLM app?

Use a dedicated LLM observability / cost-tracking layer rather than trying to infer usage from raw logs alone.

Good options

• Native provider dashboards
  • OpenAI / Azure OpenAI / Anthropic / etc.
  • Best for billing verification and provider-level usage.
• Application-level observability tools
  • LangSmith, Helicone, PromptLayer, WhyLabs, Datadog custom metrics
  • Best for tracking per-request tokens, latency, retries, model/version, prompt changes, and cost by feature/user.
• Cloud cost tooling
  • If you deploy in AWS/GCP/Azure, use their billing + logs for infrastructure cost, but this won’t capture LLM token cost cleanly by itself.

What to track

At minimum:

• model name/version
• input tokens
• output tokens
• total tokens
• estimated cost per request
• latency
• request ID / user ID / feature name
• retries / tool calls / streaming usage

Practical recommendation

If you’re building an LLM app, a common stack is:

• Helicone or LangSmith for request tracing + token/cost tracking
• provider dashboard for billing reconciliation
• Datadog / Grafana / CloudWatch for alerts and business metrics

If you want the simplest setup

• For OpenAI-only apps: start with OpenAI usage dashboards + app logs
• For multi-provider or production apps: use Helicone or LangSmith

Rule of thumb

• Billing accuracy → provider dashboard
• Product analytics and per-user cost → observability tool
• Infra monitoring → cloud monitoring

If you tell me your stack (OpenAI vs Anthropic, LangChain/LlamaIndex, cloud provider), I can recommend the best exact tool and setup.