Are you overspending on machine learning with AWS?

Machine learning has revolutionised industries by automating tasks, making predictions, and extracting insights from data. Amazon Web Services (AWS), as a leader in cloud services, offers robust tools for machine learning, with Amazon SageMaker at the forefront. In this blog post, we’ll explore the benefits of moving your machine learning models from Amazon EC2 to Amazon SageMaker, and we’ll also introduce you to the world of Amazon Bedrock.

Face sparkle

Photo by h heyerlein

Part 1: Why move from Amazon EC2 to Amazon SageMaker?

Simplified deployment

Managing EC2 instances for machine learning can be complex and time consuming. SageMaker simplifies this process by providing a managed environment specifically designed for ML model deployment. With SageMaker, you can focus on building models, not managing infrastructure.

Scalability and cost optimisation

Machine learning workloads often require scalable resources, especially for training large models or handling increased workloads. SageMaker can automatically scale resources up or down, ensuring you have the computing power you need while optimising costs.

Experimentation, versioning, and model monitoring

SageMaker provides a framework for tracking experiments, versioning models, and monitoring performance, improving efficiency while maintaining model accuracy.

Part 2: Introducing Amazon Bedrock

Model selection and customisation

Amazon Bedrock offers a curated selection of foundation models (FMs) via an API, simplifying model selection. You can also customise FMs with your data, tailoring them to your unique needs.

Quick start and integration

With Amazon Bedrock’s serverless experience, you can quickly begin using FMs and seamlessly integrate them into your applications using familiar AWS tools, including SageMaker. This serverless approach means you don’t need to worry about infrastructure management, reducing operational overhead.

Generative AI applications

Amazon Bedrock agents enable the creation of generative AI applications, expanding the capabilities of your applications. You can quickly add text generation, smart search, image generation, text summarisation, personalisation, and chatbots to your solutions.

Scalability and SaaS applications

Amazon Bedrock’s serverless architecture seamlessly integrates into Software-as-a-Service (SaaS) solutions, regardless of the tenancy model. Whether you deploy your software on a per-customer basis or within a large multi-tenant platform, integrating Bedrock is a straightforward process, allowing you to guarantee to your customers that their data will never end up in another tenant’s model.


Migrating from EC2 to SageMaker and integrating with Bedrock streamlines machine learning workflows, reduces costs through scalability and efficient use of resources, and offers a curated selection of foundation models for enhanced efficiency and innovation.

This blog is written exclusively by The Scale Factory team. We do not accept external contributions.

Free Healthcheck

Get an expert review of your AWS platform, focused on your business priorities.

Book Now

Discover how we can help you.

Consulting packages

Advice, engineering, and training, solving common SaaS problems at a fixed price.

Learn more >

Growth solutions

Complete AWS solutions, tailored to the unique needs of your SaaS business.

Learn more >

Support services

An ongoing relationship, providing access to our AWS expertise at any time.

Learn more >