Free Tokens Are Not an SLO: An Ops Cost Drill for AI Batch Queues
Free Tokens Are Not an SLO: An Ops Cost Drill for AI Batch Queues This week, two numbers trended: a harness at 100%, a model at 30%. For platform teams, a better pair is queue age and deadline slack. This article is a cost drill for the simplest AI batch path: free tokens, free server, non-negotiable deadline. Disclosure: This article was prepared as part of MonkeyCode's product outreach.…
The article explores the cost implications of using free tokens for AI batch queues, emphasizing that free tokens do not equate to a Service Level Objective (SLO). The primary focus is on understanding the operations cost of such a setup by measuring token costs and ignoring operations costs. A minimal worker.py script demonstrates a single-threaded worker processing 1,000 requests with a free model endpoint.
The worker completes 987 out of 1,000 requests, with an average processing time of 1.234 seconds per request, totaling 1,742 seconds (29 minutes) and incurring a cost of $24.19. The article also discusses the potential impact of fault injection, such as network loss, highlighting the importance of queue age and deadline slack as indicators for actionable control decisions.
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