What are your pros to using Sagemaker? Seems to me that it’s a little dead whereas bedrock is the future due to it’s ease of use and flexibility specially for getting to use something that’s already “built”

Hi all,

I’m a self-taught ML enthusiast, and I’m really enjoying my journey so far. I’m hoping to get some advice from those with more experience.

So far, I’ve successfully fine-tuned a Llama model using both SageMaker JumpStart and Amazon Bedrock. (Interestingly, in Bedrock, I had to switch to a different AWS region to access the same model I used in SageMaker.) My ultimate goal is to build a web-based app for users to interact with my fine-tuned model. However, for now, I’m still in the testing phase to ensure the model generalises well to my dataset.

I’d love some guidance on whether I should stick with SageMaker or switch fully to Bedrock. My main concern is cost management, as I’d prefer to use a serverless endpoint to avoid keeping the model “always-on.” Here’s where I’m stuck:

SageMaker: I’ve been deploying real-time endpoints on low-cost instances and deleting them after testing, but this workflow feels inefficient. I tried configuring a serverless endpoint, but I discovered it doesn’t support models requiring certain features (e.g., AWS Marketplace packages, private Docker registries, or network isolation).

Bedrock: It requires provisioned throughput ($23.50/hour per model unit) to serve fine-tuned models. While it’s fully managed, this seems expensive for my testing phase, and I’ve also noticed that Bedrock doesn’t provide detailed insights into the fine-tuning process.

For a beginner like me, what would you recommend?

Should I stick with SageMaker real-time endpoints on a low-cost instance and delete them when not in use?

Would it make sense to fine-tune the model in SageMaker and then deploy it in Bedrock?

Is there another cost-effective solution I haven’t considered?

Thank you for your time and insights!