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...

I've listed some pros and cons from experience...

..., as opposed to marketing materials. If I were to guess, I'd say you have a much higher chance to experience all the drawbacks of SageMaker, than any one of the benefits.

Drawbacks

• Cloud vendor lock in: free improvements in the open source projects in the future and better prices in competitor vendors are difficult to get. Why don't AWS invest developers in JupyterLab, they have done limited work in open source. Find some great points here, where people have experienced companies using as few AWS services as possible with good effect.
• SageMaker instances are currently 40% more expensive than their EC2 equivalent.
• Slow startup, it will break your workflow if every time you start the machine, it takes ~5 minutes. SageMaker Studio apparently speeds this up, but not without other issues. This is completely unacceptable when you are trying to code or run applications.
• SageMaker Studio is the first thing they show you when you enter SageMaker console. It should really be the last thing you consider.
  • SageMaker Studio is more limited than SageMaker notebook instances. For example, you cannot mount an EFS drive.I spoke to a AWS solutions architect, and he confirmed this was impossible (after looking for the answer all over the internet). It is also very new, so there is almost no support on it, even by AWS developers.
• Worsens the disorganised Notebooks problem. Notebooks in a file system can be much easier to organise than using JupyterLab. With SageMaker Studio, a new volume gets created and your notebooks lives in there. What happens when you have more than 1...
• Awful/ limited terminal experience, coupled with tedious configuration (via Lifecycle configuration scripts, which require the Notebook to be turned off just to edit these scripts). Additionally, you cannot set any lifecycle configurations for Studio Notebooks.
• SageMaker endpoints are limited compared to running your own server in an EC2 instance.
• It may seem like it allows you to skip certain challenges, but in fact it provides you with more obscure challenges that no one has solved. Good luck solving them. The rigidity of SageMaker and lack of documentation means lots of workarounds and pain. This is very expensive.

Benefits

These revolve around the SageMaker SDK (the Sagemaker console and SageMaker SDK) (please comment or edit if you found any more benefits)

• Built in algorithms (which you can easily just import in your machine learning framework of choice): I would say this is worse than using open source alternatives.
• Training many models easily during hyperparameter search YouTube video by AWS (a fast way to spend money)
• Easily create machine learning related AWS mechanical turk tasks. However, mturk is very limited within SageMaker, so youre better off going to mturk yourself.

My suggestion

If you're thinking about ML on the cloud, don't use SageMaker. Spin up a VM with a prebuilt image that has PyTorch/ TensorFlow and JupyterLab and get the work done.

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