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Understanding Pricing in Azure Data Factory

This post is part 24 of 25 in the series Beginner's Guide to Azure Data Factory

Congratulations! You’ve made it through my entire Beginner’s Guide to Azure Data Factory 🤓 We’ve gone through the fundamentals in the first 23 posts, and now we just have one more thing to talk about: Pricing.

And today, I’m actually going to talk! You see, in November 2019, I presented a 20-minute session at Microsoft Ignite about understanding Azure Data Factory pricing. And since it was recorded and the recording is available for free for everyone… Well, let’s just say that after 23 posts, I think we could both appreciate a short break from reading and writing 😅

(And as a side note, I’m originally publishing this post on December 24th. Here in Norway, we celebrate Christmas all day today! This is the biggest family day of the year for me, full of food and traditions. So instead of spending a lot of time writing today, I’m going to link to my video and spend the rest of the day with my family. Yay! 🎅🏻🎄🎁)

Lessons Learned: Understanding Azure Data Factory Pricing

Azure Data Factory

Azure Data Factory pricing is easy, right? No upfront costs. Pay only for what you use. It’s a wonderful world for developers. Just a few clicks and your solution is ready for production!

But what do you present to management when they ask for cost estimates? “I guess we just have to wait for the next invoice” is rarely an acceptable answer. It is definitely not an acceptable answer if that invoice turns out to be unexpectedly high…

Session Recording

View the session recording on MyIgnite:

The first slide of Cathrine WIlhelmsen's session Understanding Pricing in Azure Data Factory

Slide Deck

View the slide deck on SlideShare:


In this post video, we looked at some lessons learned about understanding pricing in Azure Data Factory. To sum up the key takeaways:

  • Everything has a cost in Azure :)
  • Activities are prorated by the minute and rounded up
  • Azure Data Factory is probably not the right tool for small, frequent batches for many single files or tables
  • Manage cost by starting, stopping, pausing, or scaling resources when they are not used

There is one more post in this series. Just one! 🤯 Tomorrow, I will be publishing a post with a whole bunch of resources and references. We won’t go through anything new, but I will share where and how and from who you can continue learning about Azure Data Factory.


About the Author

Cathrine Wilhelmsen is a Microsoft Data Platform MVP, BimlHero Certified Expert, Microsoft Certified Solutions Expert, international speaker, author, blogger, and chronic volunteer who loves teaching and sharing knowledge. She works as a Senior Business Intelligence Consultant at Inmeta, focusing on Azure Data and the Microsoft Data Platform. She loves sci-fi, chocolate, coffee, craft beers, ciders, cat gifs and smilies :)