Cost controls on the Build Your Own AI Platform track. Attribute cost to a tenant and a model version. Show it daily. Hard-stop runaway jobs. A platform that cannot answer 'what spent this' is not finished.
This lesson assumes you already worked through Logging prompts and predictions.
The idea in practice
Tag every GPU hour and every token. Review the top spenders.
A concrete check
goal = {
'track': 'Build Your Own AI Platform',
'lesson': 'Cost controls',
}
checks = [
'input available at decision time',
'score matches the real decision',
'failure case written down',
]
print(goal['lesson'])
for item in checks:
print('-', item)
Run the sketch locally if you have Python. The printout is a reminder of the checks, not a trained model. Replace the strings with the real inputs from your own example before you treat it as a design.
What usually goes wrong
A single cloud bill with no tags. When this happens, stop adding parameters or tools. Fix the check, the data, or the permission, then run the same example again.
What to write down
- The input you are allowed to use at decision time.
- The output and the score or pass rule.
- One failure you will test on purpose.
- What you will not claim the system can do.
Practice
Name the tag you put on a training job and on an inference call.
Self-check
- Say Cost controls in one sentence that mentions an input and an output.
- Name the failure mode in this lesson and the check that would catch it.
Done when: you can explain this lesson without the page open, and you have a written failure case.