Start with the division. A 1,000 RPM peak at 1,200 tokens per prompt is 1,200,000 input tokens per minute [5]. Luna's listed constant is 30,000 input TPM per PTU, so the input side of that workload is 40 PTUs [4][2]. Run the identical workload against Terra and it is 400 PTUs; against Sol, 1,000 [2]. Only the divisor moved, and the divisor is a property of the model version pinned in the deployment [2].
The dev.to walkthrough's own answer for that example is 80 PTUs before caching [6]. Input accounts for half of it [3]. The other 40 has to come from the output-side constants in Foundry's sizing table, and that table did not survive into the text supplied to me [3]. The caching delta is a decent check on the reading: cached input tokens consume no PTU capacity, and the drop from 80 to 60 is 20 PTUs, exactly half the input-side 40 [6][4]. Half the input tokens served from cache produces that number.
Then the rounding grammar. Estimates below the 15-PTU minimum go up to 15, and everything else lands on a 5-PTU increment, which is why 62.4 becomes 65, a 4.2 percent overshoot on the computed figure [8][5].
The PayGo case is checkable too. Six four-hour blocks summing to 7,000 RPM give 1,680,000 requests a day at 240 minutes per block, 2.016B input tokens a day, 60.48B over 30 days [9][6]. That reconciles with the published split of 24.192B regular input, 30.24B cached and 6.048B written, and output lands at 10.08B [11][6]. Against $19,051.20 for the month, the blend is $0.27 per million tokens across all 70.56B [12][7]. That looks cheap until you price the peak: 2,500 RPM against a 1,166 RPM daily mean, a factor of 2.14 [9][8]. Provisioned capacity is sized to the peak while PayGo is billed on the mean, and that gap is the spillover argument, one the rates could settle if they had reached me: the figures were pulled on 27 August 2026 from the Azure OpenAI pricing page and the Retail Prices API [13], and neither source reached the text I have.
One unit mismatch is worth catching before anyone signs. The hourly PTU monthly equivalent assumes 720 hours [14], while the PayGo annual figure assumes 365 days [12]. Those are not the same year. The annual total is 12.1667 of those 30-day months, and twelve 720-hour months is 8,640 hours against the 8,760 in a 365-day year, so multiplying a monthly PTU equivalent by twelve drops 120 hours, about 1.4 percent [9].
For any of this to transfer, your workload has to look like the example: an average prompt near 1,200 tokens whose first 1,024 tokens are identical across requests, which is what a cache hit requires [7]; roughly half your input tokens actually reading from cache [10]; and your region and deployment type matching the ones priced. The last condition is the one you cannot fix afterwards, because quota is granted per subscription, region and deployment type, and East US quota stays East US quota; it will not convert into West Europe quota [3]. The quota pool applies the same way no matter which model you point it at [2].
