What really happens when you call an LLM API?
- Your request hits the front door
Your request doesn’t go straight to the model. It first passes through an API gateway where things like authentication, rate limits, and request validation are checked.
If you’ve ever seen a 429 error, this is where your request got stopped.
- It gets routed to the right place
Next, a load balancer decides where your request should go. It looks at things like location and system load, then sends it to the best available GPU cluster. This is why the same request can sometimes feel faster or slower.
- Your words become numbers
Before the model understands anything, your text is broken into tokens. Think of tokens as small chunks of text, this is also where your usage (and cost) starts getting calculated.
- The system picks the right model path
There’s a routing layer that decides how your request should be handled. Bigger requests go to more powerful setups, lighter ones to faster, smaller systems. It’s all about balancing speed, cost, and performance.
- The model does the real work
This is where most of the time is spent. First, the model processes your entire input. Then, it generates the response one token at a time. That’s why streaming works you’re seeing the output as it’s being created.
- The response gets cleaned up
Once the model finishes, the output is converted back into readable text.
Safety checks run, formatting is applied, and everything is packaged neatly for you.
- You get the answer (and the bill is calculated)
Finally, the response is sent back to you. Costs are based on how many tokens you used both input and output. Longer prompts and responses mean higher cost.
Thanks to Brij Kishore Pandey for this wonderful Picture
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