MLCOST01-BP02 Use managed services to reduce total cost of ownership (TCO) - Machine Learning Lens
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MLCOST01-BP02 Use managed services to reduce total cost of ownership (TCO)

Using managed machine learning services enables organizations to operate more efficiently with reduced resources and costs compared to self-managed options. This approach reduces undifferentiated heavy lifting, reduces operational burden, and allows teams to focus on delivering business value.

Desired outcome: By adopting managed services and pay-per-usage models, you significantly reduce your total cost of ownership while gaining access to a comprehensive suite of AI/ML tools. You can use pre-built capabilities instead of developing custom solutions, automatically scale resources based on demand, and benefit from AWS's continuous innovations without additional investment. Your teams can focus on creating business value rather than managing infrastructure.

Common anti-patterns:

  • Building and maintaining custom ML infrastructure on EC2 or Kubernetes.

  • Overprovisioning resources for peak ML workloads.

  • Failing to use commitment discounts for persistent workloads.

  • Developing proprietary AI services when managed services would suffice.

  • Not analyzing workload patterns to optimize instance selection.

Benefits of establishing this best practice:

  • Significantly lower total cost of ownership compared to self-managed options.

  • Reduced operational overhead and simplified management.

  • Increased team productivity with focus on core business problems.

  • Access to continuously updated and improved AI/ML capabilities.

  • Flexibility to scale resources based on actual demand.

  • Ability to use commitment-based pricing for additional savings.

Level of risk exposed if this best practice is not established: High

Implementation guidance

Managed services remove the operational burden of maintaining infrastructure, allowing you to concentrate on developing ML models and applications that drive business value. Using AWS's managed ML services provides a comprehensive environment for building, training, and deploying models with significantly lower costs than self-managed options.

When evaluating your ML strategy, consider the total cost of ownership including infrastructure, operational personnel, maintenance, scaling, and upgrades. Amazon SageMaker AI provides a fully managed service that avoids many of these costs while offering advanced ML capabilities. Similarly, AWS's pre-trained AI services can address common use cases without requiring ML expertise, further reducing implementation time and costs.

To maximize cost efficiency, analyze your workload patterns and determine which components would benefit from commitment discounts. By using Savings Plans, you can significantly reduce your AWS usage costs while maintaining flexibility across instance families, sizes, regions, and components.

Implementation steps

  1. Use Amazon SageMaker AI as your fully managed ML solution. Amazon SageMaker AI enables building, training, and deploying models at scale with significantly lower costs. The total cost of ownership (TCO) of SageMaker AI over a three-year period is much lower than other self-managed cloud-based ML options, such as Amazon Elastic Compute Cloud (Amazon EC2) and Amazon Elastic Kubernetes Service (Amazon EKS). SageMaker AI includes technologies such as Autopilot, Feature Store, Clarify, Debugger, Studio, Training, Model deployment, Monitoring, and Pipelines.

  2. Use Amazon managed AI services for common use cases. AWS pre-trained AI services provide ready-made intelligence for your applications and workflows. These services address common use cases such as personalized recommendations, contact center modernization, safety and security improvement, and customer engagement enhancement. They don't require machine learning expertise, are fully managed, and offer pay-as-you-go pricing with no upfront commitment.

  3. Perform pricing model analysis for cost optimization. Analyze each component of your ML workload to determine if it will run for extended periods, making it eligible for commitment discounts such as AWS Savings Plans. You can use Savings Plans to reduce AWS usage costs by committing to a consistent amount of usage. Amazon SageMaker AI Savings Plans offer flexible attributes such as instance family, instance size, AWS Region, and component for your SageMaker AI instance usage.

  4. Implement right-sizing strategies for ML resources. Evaluate your actual ML workload resource requirements and adjust instance types and sizes accordingly. This blocks overprovisioning and assists to control costs while maintaining performance. Use SageMaker AI's automatic scaling capabilities to match resources with demand.

  5. Use serverless options when appropriate. For intermittent workloads or those with variable demand, consider serverless options like Amazon SageMaker AI Serverless Inference to avoid paying for idle resources.

  6. Use Amazon Bedrock for foundation model access. Amazon Bedrock provides a unified API for accessing various foundation models, making it simple to experiment with and integrate generative AI capabilities without investing in model training infrastructure. This fully managed service assists to reduce costs while allowing flexibility to choose the right model for your use case.

  7. Use Foundation Model Hub for centralized model access. Use the Foundation Model Hub to access a centralized catalog of popular foundation models with simplified deployment and performance benchmarking tools, reducing the time and cost of model selection and deployment.

  8. Use AI-powered code generation tools. Use Amazon Q Developer and AI-powered IDEs like Kiro to accelerate ML development through AI-assisted coding, automated code generation, and intelligent troubleshooting, significantly reducing developer time and associated costs.

Resources

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