We’re introducing yet another exciting update: Credit usage. It’s available on all plans, so give it a try and let us know your opinion!
What is the Credit usage feature?
The Credit usage table is a source of truth for credit consumption and all scenario executions. With this table, you get a clear, organization-wide view of your scenario executions and credit consumption across your Make scenarios and features for the last 30 days.
What are the key benefits
Centralized monitoring: Get complete visibility into your organization’s scenario executions and credit consumption
Performance optimization: Identify scenarios that are running most often, understand consumption trends, and spot usage spikes.
Granular insights
The exact scenario or event that consumed credits
The type of automation: scenario or agents
Credits spent per run
Data transferred
Timestamp of the usage
Head over to the Organization tab in Make and navigate to the “Credit usage” to view this new table!
The new Credit usage table gives a clear org, level and per, run view of credits, so it’s much easier to spot heavy scenarios and keep usage under control.
That’s a great starting point and will be extremely useful to track who is using what..
Now can we also sort by credits or data usage and have the ability to group things together and filter by date? So I can see who used most credits this week for example.
Also, I see my instagram scenario ran 5 times using 4 credits each time today. Instead I want to see one instance of the instagram scenario showing that it used 20 credits today. And then be able to filter and show weekly and/or monthly as well.
Cause at the moment its just a descending list going backwards from now showing individual runs.
Hi @Stoyan_Vatov, thanks a lot for the detailed feedback, the examples you shared make it very clear how you want to work with the data and that is incredibly helpful for us.
This release is the first step toward giving everyone a clearer, more transparent view of consumption. We’ll continue expanding the basics of the table so it becomes easier to understand overall usage, including simple grouping and totals that help surface the bigger picture rather than just individual runs.
Your points about filtering, summarizing credits over a period, and seeing scenario level totals are very much aligned with how we expect the view to evolve.
Thanks again for taking the time to share your thoughts. Input like this helps us make sure the next iterations support the way teams actually work with consumption data.
What is the Credit usage feature?
What are the key benefits
