From my research on the pricing structure for Azure ML, it appears to have three components, per API call, computational time and per seat. As of writing of this post, the “seat” implies Azure ML workspace tied to an Azure Subscription. So for each Azure ML workspace, the subscription will be billed $9.69/month. Please note $9.69 is what I found to be in our billing statement, other sources mentioned $9.99.
The time taken to compute is charged at $2 / hour, for both BES and RRS.
Every API call is charged at the listed prices ($0.0005 per call), both BES (BATCH EXECUTION) and RRS (REQUEST/RESPONSE).
So for example if 1000 records take 1 hour to compute, the total charge will be $2.0005. $2 for for the time taken + $0.0005 for 1 API call.
Why does this matter? RRS is generally low computation time but high API usage, while BES is usually low API usage but high computational time.
I’ve noticed that processing 10 – 10000 records took about the same time using batch execution. As I mentioned in my earlier post, it took about 28 minutes using batch execution program to process 2.1 million records, with 160 columns. I suspect that most of the time is spent uploading data to blob storage and downloading to my machine. Our billing statement shows Machine Learning Production API Compute Hours at 0.1358 consumed units, where unit of measure is 10 hours. Machine Learning Production API Transactions metric indicates a value of 0.0056 whereas units of measure here 10000 s. These numbers do change in real time in Microsoft Azure Enterprise Portal.
So far I’ve processed over 8 million records with batch execution, and our billing statement shows $0 for both BES and RRS. The only charge is $9.69 for the seat.
Also, I’ve been told that there is no distinction anymore between Stage and Production. And 10 GB limit that is there for free accounts is lifted with enterprise accounts.