Enterprise guide to AWS GenAI infrastructure choices
Demystifying AWS Service Choices
T. K. Daisy Leung
Amazon Employee
Published Mar 2, 2025
Last Modified Mar 3, 2025
Generative AI is transforming industries — from content creation to personalized customer experiences — and AWS is at the forefront with a range of services tailored to every need. As a former GenAI scientist with hands‑on experience, I’ve had the privilege of deploying state‑of‑the‑art open‑source models from Hugging Face using AWS services. In this post, I’ll walk you through the key considerations for training and inference, comparing AWS Bedrock, Amazon SageMaker (including its specialized options), containerized solutions (ECS/EKS), and HPC clusters (AWS ParallelCluster). Whether you’re a tech leader, ML engineer, or an executive, this guide is designed to help you make practical, informed decisions for your AWS GenAI journey.
When evaluating AWS GenAI services, it helps to think about two sets of priorities:
• Ease of Deployment: Services like AWS Bedrock and SageMaker JumpStart provide one‑click deployments and automated container handling.
• Development Flexibility: Options such as ECS/EKS and SageMaker HyperPod (designed for large‑scale, distributed training) offer granular control.
• Integration Capabilities: Native integrations with Hugging Face on SageMaker streamline workflows, including model versioning and CI/CD support.
• Monitoring & Debugging: Built‑in tools in SageMaker (e.g., experiment tracking and VPC endpoints) ease troubleshooting.
• Scaling Controls & Custom Container Support: For teams with advanced DevOps skills, ECS/EKS allow maximum customization and manual scaling.
• Resource Management & Development Time: Fully managed services reduce time-to-market, while custom solutions require more hands‑on management.
• Development Flexibility: Options such as ECS/EKS and SageMaker HyperPod (designed for large‑scale, distributed training) offer granular control.
• Integration Capabilities: Native integrations with Hugging Face on SageMaker streamline workflows, including model versioning and CI/CD support.
• Monitoring & Debugging: Built‑in tools in SageMaker (e.g., experiment tracking and VPC endpoints) ease troubleshooting.
• Scaling Controls & Custom Container Support: For teams with advanced DevOps skills, ECS/EKS allow maximum customization and manual scaling.
• Resource Management & Development Time: Fully managed services reduce time-to-market, while custom solutions require more hands‑on management.
• Time to Market: Fully managed services like Bedrock and SageMaker JumpStart accelerate deployment, reducing development overhead.
• Total Cost of Ownership (TCO): Managed services trade off some flexibility for predictable, pay‑per‑use pricing, while container‑based or HPC solutions (like ParallelCluster) can offer lower cost at scale—provided you have the right expertise.
• Operational Overhead & Team Expertise: The choice between a no‑ops solution (Bedrock API) and a more hands‑on approach (ECS/EKS) depends on your team’s technical depth.
• Vendor Lock‑In Risk & Security/Compliance: AWS’s managed services provide robust security, compliance, and SLA guarantees. Meanwhile, container‑based deployments offer greater control and flexibility, albeit with increased operational complexity.
• Initial Investment & Ongoing Maintenance: Evaluate whether your strategic priorities favor rapid deployment or custom, cost‑optimized solutions that may require a higher upfront investment but lower long‑term costs.
• Total Cost of Ownership (TCO): Managed services trade off some flexibility for predictable, pay‑per‑use pricing, while container‑based or HPC solutions (like ParallelCluster) can offer lower cost at scale—provided you have the right expertise.
• Operational Overhead & Team Expertise: The choice between a no‑ops solution (Bedrock API) and a more hands‑on approach (ECS/EKS) depends on your team’s technical depth.
• Vendor Lock‑In Risk & Security/Compliance: AWS’s managed services provide robust security, compliance, and SLA guarantees. Meanwhile, container‑based deployments offer greater control and flexibility, albeit with increased operational complexity.
• Initial Investment & Ongoing Maintenance: Evaluate whether your strategic priorities favor rapid deployment or custom, cost‑optimized solutions that may require a higher upfront investment but lower long‑term costs.
Before choosing a service, ask yourself:
Training Requirements
• Fine‑Tuning vs. Training from Scratch? Decide if you need to fine‑tune a model or train a model entirely. Fine‑tuning might be efficiently handled on SageMaker.
• Scale of Training: For large models with distributed training needs, look to specialized services like SageMaker HyperPod (which supports dynamic clusters on EKS and integrates with AWS Trainium/NVIDIA GPUs).
• Cost Optimization & Infrastructure Overhead: If reducing training cost is paramount—and your team is comfortable with container orchestration—ECS/EKS or even ParallelCluster (for batch training and HPC workloads) may be appropriate.
• Fine‑Tuning vs. Training from Scratch? Decide if you need to fine‑tune a model or train a model entirely. Fine‑tuning might be efficiently handled on SageMaker.
• Scale of Training: For large models with distributed training needs, look to specialized services like SageMaker HyperPod (which supports dynamic clusters on EKS and integrates with AWS Trainium/NVIDIA GPUs).
• Cost Optimization & Infrastructure Overhead: If reducing training cost is paramount—and your team is comfortable with container orchestration—ECS/EKS or even ParallelCluster (for batch training and HPC workloads) may be appropriate.
Inference Requirements
• Latency and Throughput: For low‑latency, high‑throughput applications, the fully managed Bedrock API or SageMaker endpoints (leveraging real‑time inference or serverless options) are good options to start with.
• Cost and Operational Overhead: A simple, pay‑per‑use model like Bedrock reduces operational overhead, while ECS/EKS provides maximum control for cost‑optimized, large‑scale inference deployments.
• Scaling Needs: Automatic scaling in SageMaker endpoints offers a balanced solution for many production workloads without requiring deep operational expertise.
• Latency and Throughput: For low‑latency, high‑throughput applications, the fully managed Bedrock API or SageMaker endpoints (leveraging real‑time inference or serverless options) are good options to start with.
• Cost and Operational Overhead: A simple, pay‑per‑use model like Bedrock reduces operational overhead, while ECS/EKS provides maximum control for cost‑optimized, large‑scale inference deployments.
• Scaling Needs: Automatic scaling in SageMaker endpoints offers a balanced solution for many production workloads without requiring deep operational expertise.
AWS Bedrock API
• What It Is: A fully managed, inference‑only service with minimal infrastructure management.
• Benefits: Zero‑ops and quick integration via simple API calls; Built‑in monitoring and auto‑scaling.
• Tradeoffs: Limited customization and control over the underlying model.
• Ideal for: Executives and developers who want to get to market quickly with minimal operational overhead.
• What It Is: A fully managed, inference‑only service with minimal infrastructure management.
• Benefits: Zero‑ops and quick integration via simple API calls; Built‑in monitoring and auto‑scaling.
• Tradeoffs: Limited customization and control over the underlying model.
• Ideal for: Executives and developers who want to get to market quickly with minimal operational overhead.
Amazon SageMaker
• What It Is: A fully managed machine‑learning platform that supports both training and inference.
• Benefits: End‑to‑end support—from fine‑tuning using Hugging Face DLCs to deployment via SageMaker endpoints; Integrated experiment tracking, VPC configuration, and CI/CD pipelines; Flexible deployment options (real‑time, serverless, and asynchronous inference).
• Tradeoffs: Some infrastructure expertise is required, and there may be tradeoffs in latency compared to custom solutions.
• Ideal for: Teams seeking a balanced approach for both training and inference with strong integration into the AWS ecosystem.
• What It Is: A fully managed machine‑learning platform that supports both training and inference.
• Benefits: End‑to‑end support—from fine‑tuning using Hugging Face DLCs to deployment via SageMaker endpoints; Integrated experiment tracking, VPC configuration, and CI/CD pipelines; Flexible deployment options (real‑time, serverless, and asynchronous inference).
• Tradeoffs: Some infrastructure expertise is required, and there may be tradeoffs in latency compared to custom solutions.
• Ideal for: Teams seeking a balanced approach for both training and inference with strong integration into the AWS ecosystem.
SageMaker HyperPod
• What It Is: Optimized for large‑scale, distributed training of foundation models. HyperPod provisions resilient clusters on EKS with dynamic capacity management.
• Benefits: Superior performance for distributed training workloads (e.g., for fine‑tuning large language models); Automatic replacement of faulty accelerators enhances reliability.
• Tradeoffs: Higher minimum cost commitment and increased complexity; Requires some level of expertise in distributed training.
• Ideal for: Training large models where performance and cost‑efficiency (e.g., leveraging AWS Trainium and Inferentia) are critical.
• What It Is: Optimized for large‑scale, distributed training of foundation models. HyperPod provisions resilient clusters on EKS with dynamic capacity management.
• Benefits: Superior performance for distributed training workloads (e.g., for fine‑tuning large language models); Automatic replacement of faulty accelerators enhances reliability.
• Tradeoffs: Higher minimum cost commitment and increased complexity; Requires some level of expertise in distributed training.
• Ideal for: Training large models where performance and cost‑efficiency (e.g., leveraging AWS Trainium and Inferentia) are critical.
ECS/EKS (Container‑Based Deployment)
• What It Is: Maximum flexibility through containerized deployments that allow full control over resource allocation and custom pipelines.
• Benefits: Cost‑effective for high‑volume inference with custom scaling; Full customization and integration with existing containerized environments.
• Tradeoffs: DevOps expertise required.
• Ideal for: Organizations with robust DevOps capabilities and a need for custom, cost‑optimized solutions both for training (if re‑architected) and inference.
• What It Is: Maximum flexibility through containerized deployments that allow full control over resource allocation and custom pipelines.
• Benefits: Cost‑effective for high‑volume inference with custom scaling; Full customization and integration with existing containerized environments.
• Tradeoffs: DevOps expertise required.
• Ideal for: Organizations with robust DevOps capabilities and a need for custom, cost‑optimized solutions both for training (if re‑architected) and inference.
AWS ParallelCluster
• What It Is: An HPC‑oriented service ideal for batch training workloads.
• Benefits: Cost‑effective for high‑performance, batch‑oriented training; Excellent for use cases that do not require real‑time inference; Familiarity for researchers from academic background.
• Tradeoffs: High operational overhead and complexity; Less suited for inference workloads due to latency constraints.
• Ideal for: Specialized training tasks where batch processing and HPC are key, rather than interactive inference.
• What It Is: An HPC‑oriented service ideal for batch training workloads.
• Benefits: Cost‑effective for high‑performance, batch‑oriented training; Excellent for use cases that do not require real‑time inference; Familiarity for researchers from academic background.
• Tradeoffs: High operational overhead and complexity; Less suited for inference workloads due to latency constraints.
• Ideal for: Specialized training tasks where batch processing and HPC are key, rather than interactive inference.
For Training
• Large, Distributed Training: Use SageMaker HyperPod — its integration with AWS Trainium/NVIDIA accelerators ensures cost‑efficient, high‑performance training for massive models.
• Fine‑Tuning or Moderate‑Sized Models: SageMaker provides a balanced, fully managed solution that handles both training and inference with built‑in experiment tracking.
• Cost‑Optimized Training with DevOps Expertise: Consider a container‑based solution via ECS/EKS, which, while operationally complex, offers granular control and lower costs at scale.
• Batch Training Workloads: ParallelCluster is a viable option with strong technical expertise when interactive training is not required.
• Large, Distributed Training: Use SageMaker HyperPod — its integration with AWS Trainium/NVIDIA accelerators ensures cost‑efficient, high‑performance training for massive models.
• Fine‑Tuning or Moderate‑Sized Models: SageMaker provides a balanced, fully managed solution that handles both training and inference with built‑in experiment tracking.
• Cost‑Optimized Training with DevOps Expertise: Consider a container‑based solution via ECS/EKS, which, while operationally complex, offers granular control and lower costs at scale.
• Batch Training Workloads: ParallelCluster is a viable option with strong technical expertise when interactive training is not required.
For Inference
• Minimal Overhead and Rapid Deployment: The Bedrock API is best for quickly deploying inference‑only workloads with minimal configuration.
• Managed but Customizable Inference: SageMaker Inference Endpoints (including serverless options) strike a balance between ease of use and control.
• Maximum Customization and Cost Optimization: For organizations with advanced container orchestration capabilities, ECS/EKS can deliver finely tuned performance and cost‑efficiency.
• Minimal Overhead and Rapid Deployment: The Bedrock API is best for quickly deploying inference‑only workloads with minimal configuration.
• Managed but Customizable Inference: SageMaker Inference Endpoints (including serverless options) strike a balance between ease of use and control.
• Maximum Customization and Cost Optimization: For organizations with advanced container orchestration capabilities, ECS/EKS can deliver finely tuned performance and cost‑efficiency.
• GPU & Accelerator Availability: Check AWS Regional availability and instance types (including AWS Trainium and Inferentia) to ensure your chosen service meets your performance requirements.
• Framework Support: Hugging Face models are seamlessly integrated with SageMaker (via Deep Learning Containers, JumpStart, and the Inference Toolkit), ensuring compatibility with PyTorch, TensorFlow, and JAX.
• Security & Compliance: Managed services like SageMaker and Bedrock provide VPC support and integration with AWS PrivateLink for secure, enterprise‑grade deployments.
• Scalability & Operational Overhead: Weigh the benefits of fully managed services against the customization of container‑based deployments. For many enterprises, the slight premium of a managed service is offset by lower operational risk and faster time to market.
• Framework Support: Hugging Face models are seamlessly integrated with SageMaker (via Deep Learning Containers, JumpStart, and the Inference Toolkit), ensuring compatibility with PyTorch, TensorFlow, and JAX.
• Security & Compliance: Managed services like SageMaker and Bedrock provide VPC support and integration with AWS PrivateLink for secure, enterprise‑grade deployments.
• Scalability & Operational Overhead: Weigh the benefits of fully managed services against the customization of container‑based deployments. For many enterprises, the slight premium of a managed service is offset by lower operational risk and faster time to market.
Choosing the right AWS GenAI infrastructure depends on your specific requirements — whether it’s the speed of deployment, cost optimization, or the flexibility to fine‑tune models at scale. With services like AWS Bedrock for effortless inference, Amazon SageMaker for full‑cycle ML workflows, and container/HPC solutions for maximum control, AWS offers a spectrum of options to meet the demands of both developers and executive decision makers. In my experience, balancing technical agility with business efficiency has been critical. By understanding and leveraging these AWS services in line with your unique requirements, you can accelerate innovation, reduce costs, and drive impactful business outcomes.
Ready to dive deeper? Explore the latest AWS documentation and case studies on deploying Hugging Face models with SageMaker and other AWS services to see how leading organizations are leveraging these tools to transform their operations.
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This blog post is intended to help both technical teams and executive leaders navigate AWS’s diverse GenAI offerings. I hope it provides clarity and sparks innovative ideas for your next project. Feel free to share your thoughts and experiences — let’s drive the conversation on building scalable, enterprise‑grade GenAI solutions on AWS.
Any opinions in this post are those of the individual author and may not reflect the opinions of AWS.