When it comes to deploying AI and machine learning workloads on AWS, one of the first questions teams face is whether to use AWS SageMaker or self-hosted models. Both approaches have their strengths, but the right choice depends on your use case, team expertise, and priorities.

🚀 Why Choose AWS SageMaker?

  • ✅ Ease of Use – SageMaker provides a fully managed service that simplifies the ML lifecycle, from data preparation to training and deployment. It’s ideal for teams without deep DevOps expertise.
  • ✅ Seamless Integration – Built-in support for AWS services like S3, Lambda, and CloudWatch means minimal effort is needed to connect SageMaker to your existing infrastructure.
  • ✅ Scalability – With managed clusters for training and inference, SageMaker scales automatically to handle workloads of any size.
  • ✅ Specialised Features – Features like SageMaker Autopilot (for automated model generation) and SageMaker Clarify (for bias detection) give you capabilities beyond raw compute.

🔒 Why Choose Self-Hosted Models?

  • ✅ Cost Control – Self-hosting lets you decide instance types, scaling policies, and runtime environments. This can significantly reduce costs for long-running or predictable workloads.
  • ✅ Customisation – Fine-tune everything, from the container runtime to GPU utilisation. You’re not limited by SageMaker’s options.
  • ✅ Security – Maintain stringent control over data and models for workloads with strict compliance requirements.
  • ✅ Portability – Easily migrate workloads across cloud providers or hybrid environments, avoiding potential lock-in.

🔄 When to Combine Both Approaches

Many teams use SageMaker for experimentation and early prototyping, then migrate stable models to self-hosted environments for cost and control:

  • Experimentation phase: Use SageMaker’s Jupyter notebooks and pre-configured instances to iterate quickly.
  • Production phase: Deploy optimised models on self-hosted GPU clusters or ECS for cost-efficient, long-term scalability.

🛠 Practical Recommendations

  • For small teams or projects with tight timelines: Start with SageMaker to leverage its managed features.
  • For mature workloads or cost-sensitive applications: Consider self-hosting to optimise expenses and gain full control.
  • For hybrid needs: Use both approaches strategically—prototyping in SageMaker and scaling in self-hosted environments.