richardchen874-sys commented on issue #858:
URL: https://github.com/apache/flink-agents/issues/858#issuecomment-5018300297

   I like the way this issue frames tracking token usage metrics for embedding 
models, not only chat models. In real workloads, the hard part is usually not 
sending one request; it is keeping cost, latency, and response shape 
predictable once users switch providers.
   
   I would record provider, model, dimensions, input batch size, token usage, 
and latency for embeddings just like chat responses. That makes embedding-heavy 
workflows visible before they become a hidden cost center.
   
   I am testing an OpenAI-compatible multi-model API layer around official 
Chinese models, so this kind of routing, usage, and compatibility boundary is 
very relevant. Who do you expect to consume these metrics first: developers 
debugging agents, operators managing spend, or end users?


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