Your monthly OpenAI or Anthropic invoice tells you how much you spent. It doesn't tell you which feature spent it, which model, or why last Tuesday cost three times as much as Monday. So at some point you (or your team) will build a metering layer: wrap the client, read usage off the response, multiply by a price table, ship it to a database.
I did exactly that over the past few months while building an LLM observability service, and my numbers were wrong in five different ways before they were right. Every one of these failures was silent. No exception, no alert, just numbers that were quietly too low or too high. This is the list I wish someone had handed me.
Pitfall 1: Streaming responses quietly report zero tokens
OpenAI's Chat Completions API returns no usage data at all for streaming requests unless you pass stream_options: { include_usage: true }. No error, no warning. The stream just never contains token counts.
If your metering reads usage off the chunks, every streaming call gets recorded as 0 tokens, $0. And since chat UIs are almost always streaming, that's most of your traffic.
