Fair catch, 2M input tokens is genuinely cheap. The breakeven is driven by output tokens and sustained concurrency rather than input volume. I should have split those out.
Yes this is where I stopped also, and how much is that 2 Million Tokens is there an average number of requests or do I need to test it through APIs first then decide as I don’t think that 2 Million Tokens a day are that much.
Really liked the framing around 'keep the GPU busy', that’s the real constraint. One angle that often gets missed in the buy-vs-API debate is existing compute: most orgs already have servers, workstations, even laptops sitting idle large parts of the day. If you can route workloads there, the economics and utilization equation look very different versus provisioning new capacity. Same game, just against hardware you’ve already paid for.
I'm curious how you think about that in the context of scaling and reliability? (I’ve been working on this space at Kibbu.io)
I appreciate the premise of the article, and all the concepts feel right. I wonder if I misunderstood the count, though? Two million tokens a day?
The standard cost for 2 million input tokens using GPT-5.6 Sol is $10.00
Fair catch, 2M input tokens is genuinely cheap. The breakeven is driven by output tokens and sustained concurrency rather than input volume. I should have split those out.
Yes this is where I stopped also, and how much is that 2 Million Tokens is there an average number of requests or do I need to test it through APIs first then decide as I don’t think that 2 Million Tokens a day are that much.
Depends on request shape, 2M a day is roughly 1000 requests at 2K tokens each. Measure your own input-output split first, that ratio decides the math.
Thank you. Great article, much needed!
Appreciate it. The breakeven question is the one nobody publishes honestly.
Really liked the framing around 'keep the GPU busy', that’s the real constraint. One angle that often gets missed in the buy-vs-API debate is existing compute: most orgs already have servers, workstations, even laptops sitting idle large parts of the day. If you can route workloads there, the economics and utilization equation look very different versus provisioning new capacity. Same game, just against hardware you’ve already paid for.
I'm curious how you think about that in the context of scaling and reliability? (I’ve been working on this space at Kibbu.io)