Hi @yann_stoneman, you're right. Up to 4 apps can run on the same instances, so different kernels could still be run on the same instance. For example, a data scientist could be working on a tabular use case, and an image processing use case - so they might have a CPU and GPU instance running. Or they might use a larger instance for data processing or data wrangler feature. Depending on your data scientists' projects and use cases, I'd account for at most 2 instances per data scientist running concurrently. If your users already use SageMaker Notebook Instances, you can use the commonly used resource type as the Studio instance resource type for estimates - that way you can get a closer estimate to the actual costs. If you're allowing for shared spaces (real time collaboration), include additional instances in your estimate - the users will now be able to use a private space through their user profile (unique to one user) and a shared space (this instance can be accessed across profiles). I'd also recommend using a plugin to shut down idle instances as a best practice when your teams are onboarded to Studio, so these instances are shut down if there are no notebooks actively running (ref: https://aws.amazon.com/blogs/machine-learning/save-costs-by-automatically-shutting-down-idle-resources-within-amazon-sagemaker-studio/)