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How come workflow uses 10 credits if the execution time is under 30 seconds? Cutting down the memory results in exceeding the default memory.
It seems like your workflow is using 10 credits despite the execution time being under 30 seconds because of the memory configuration. According to the Pipedream documentation, credits are charged based on both the execution time and the memory configuration of the workflow. If your workflow is configured with more than the default memory of 256MB, the credit usage increases proportionally. For example, if your workflow is set to use 1024MB of memory, it would use 4 credits per 30 seconds of execution time, as it’s using 4 times the default memory. In your case, if the workflow is using 10 credits for an execution time of under 30 seconds, it likely means that the memory configuration is significantly higher than the default. You might want to check the memory settings of your workflow and adjust them if necessary to optimize credit usage. For more details on how memory affects credit usage, you can refer to the Pipedream documentation.
If I’m setting the memory to, for example, 512 Mb it results in exceeding the default memory.
If you set the memory of your Pipedream workflow to 512 MB, it exceeds the default memory limit. By default, workflows run with a certain amount of memory, and increasing this limit will result in additional credit charges proportional to your memory configuration. You can adjust the memory settings in your workflow’s settings, and Pipedream will show you the number of credits you’ll be charged per execution. For more details, you can read about how workflow memory affects credits in the Pipedream pricing documentation.
is there a way to find out how much memory will a workflow use?
To determine how much memory a workflow will use, you can monitor the memory usage during the execution of your workflow. Pipedream does not provide a direct way to predict memory usage before execution, but you can adjust the memory settings based on your workflow’s requirements. Here are some steps you can take: 1. Monitor Execution Logs: When you run your workflow, check the execution logs for any memory-related messages or errors. This can give you an indication of whether your current memory allocation is sufficient. 2. Adjust Memory Settings: If you find that your workflow is running out of memory or if you want to optimize performance, you can increase the memory allocation. This can be done in your workflow’s settings. Remember that increasing memory will also increase the credits charged per execution. 3. Test with Different Configurations: Run your workflow with different memory configurations to see how it affects performance and credit usage. This can help you find the optimal balance between memory usage and cost. For more details on how memory affects credits, you can refer to the Pipedream documentation on memory and credits. If you have any further questions or need assistance, feel free to visit Pipedream Support.