3 ways to deploy your large language models on AWS

In this episode of the AWS Developers Podcast, we dive into the different ways to deploy large language models (LLMs) on AWS. From self-managed deployments on EC2 to fully managed services like SageMaker and Bedrock, we break down the pros and cons of each approach. Whether you're optimizing for compliance, cost, or time-to-market, we explore the trade-offs between flexibility and simplicity. You'll hear practical insights into instance selection, infrastructure management, model sizing, and prototyping strategies. We also examine how services like SageMaker Jumpstart and serverless architectures like Bedrock can streamline your machine learning workflows. If you're building or scaling AI applications in the cloud, this episode will help you navigate your options and design a deployment strategy that fits your needs.With Germaine Ong (linkedin.com/in/germaineong), Startup Solution Architect ; With Jarett Yeo (linkedin.com/in/germaineong), Startup Solution Architect• Blog: Deploying Deepseek R1 Distill on Amazon EC2 (community.aws/content/2sEuHQlpyIFSwCkzmx585Jck…)Blog: Deploying DeepSeek R1 Distill on Amazon Sagemaker Jumpstart (community.aws/content/2sG84dNUCFzA9z4HdfqTI0tc…)Ollama (ollama.com)Open Web UI (github.com/open-webui/open-webui)Doc: deploy your own model on Amazon Sagemaker (docs.aws.amazon.com/sagemaker/latest/dg/deploy…)Doc: deploy your own model on Amazon Bedrock (docs.aws.amazon.com/bedrock/latest/userguide/b…)