I'll address your questions for both SageMaker Studio JupyterLab and SageMaker Notebook Instances: A) SageMaker Studio JupyterLab: 1. SageMaker Studio is billed per second of usage, with a minimum of 1 minute. This means if you use Studio for just one minute, you'll be charged for that minute, not a full hour. The billing starts when you launch the SageMaker Studio application and ends when you log out or when an administrator ends your session. 2. In SageMaker Studio, each user gets their own dedicated workspace instance. If multiple data scientists are working in Studio, they would each have their own session and be billed separately. There isn't a concept of sharing the same Studio session among multiple users in the way you described. B) SageMaker Notebook Instance: 1. SageMaker Notebook Instances are also billed per second of usage, with a minimum of 1 minute. Like Studio, if you use a Notebook Instance for just one minute, you'll be charged for that minute, not a full hour. 2. For Notebook Instances, multiple users can technically share the same instance, but it's not designed for simultaneous use by multiple users. If two users are using the same Notebook Instance, you would still be charged based on the instance type and the total time it's running, not per user. So in your example with an ml.t3.16xlarge instance used for one hour, the charge would be $1 (assuming that's the correct rate), regardless of whether one or two users were using it. It's important to note that while sharing a Notebook Instance is possible, it's generally not recommended for collaborative work due to potential conflicts and security concerns. SageMaker Studio is better suited for multi-user environments, where each user gets their own workspace. In both cases, you only pay for the compute resources you use, and there are no additional charges for using the SageMaker Studio or Notebook Instance interfaces themselves. **Sources** Amazon SageMaker Studio pricing - Amazon SageMaker AI Machine Learning Service – Amazon SageMaker Pricing – AWS Community | Enhancing ML Efficiency with Amazon SageMaker

If you're bringing your own model, you don't need to use SageMaker. I've got mixtral-8x7b.Q5_K_M running on a g5.2xlarge (24GB of VRAM, 32 GB RAM), and it's $1.212 per hour in us-east-1. Just be sure to pick the Ubuntu deep learning AMI, as it has the required GPU drivers. Be aware that you're paying for the server whenever it's running - not just when you're interacting with the model. If you forget to shut it off and leave it for a month, that's $860...