In today’s post I’d like to discuss how Azure Data Factory pricing works with the Version 2 model which was just released. The pricing is broken down into four ways that you’re paying for this service. I hope that by pointing these out, you can gain an understanding of not only how it works, but how you can keep an eye on your spending.

1. Azure activity runs vs self-hosted activity runs – there are different pricing models for these. For the Azure activity runs it’s about copying activity, so you’re moving data from an Azure Blob to an Azure SQL database or Hive activity running high script on an Azure HDInsight cluster.

With self-hosted, you want to copy activity moving from an on premises SQL Server to an Azure Blob Storage, a stored procedure to an Azure Blob Storage or a stored procedure activity running a stored procedure on an on premises SQL Server.

2. Volume of data moved – this is measured in DMUs (data movement units). This is one you should be aware of as this will default to auto, which is basically using all the DMUs it can use and this is paid for by the hour. Let’s say you specify and use 2 DMUs and it takes an hour to move that data. The other option is you could use 8 DMUs and it takes 15 minutes, this price is going to end up the same. You’re using 4X the DMUs but it’s happening in a quarter of the time.

This is good to look at and do some comparisons since how many DMUs you’re using is where the bulk of your spend if going to be.

3. SSIS integration run times – here you’re using A-series and D-series compute levels. When you go through these, it depends on what the compute needs are to invoke the process (how much CPU, how much RAM, how much attempt storage you need).

4. The inactive pipeline – you’re paying a small account for pipelines (about 40 cents currently). A pipeline is considered inactive if it’s not associated with a trigger and hasn’t been run for over a week. Yes, it’s a minimal charge, but they do add up and when you start to wonder where some of those charges come from it’s good to keep this in mind.

Also, each of the components inside the Azure Data Factory, whether it’s blob storage, SQL Server, HDInsight or any kind of storage or compute resources you’re using as part of your pipeline, will also incur charges. These are billed separately based specifically around what those resources are.

Something to keep in mind as you start of build workloads, like if you spin up an HDInsight cluster or a SQL data warehouse as part of a pipeline, make sure you shut down, pause it or destroy that cluster afterwards. So, there are opportunities to get your data moved but also keep the cost down but not keeping it running all the time.