Amazon Bedrock
Bedrock is the AI layer that lets an application call ready-made foundation models through a common API. It brings generative inference into product features without requiring you to build model-training infrastructure yourself.
▶Architecture Diagram
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You may want to add chat or summarization, but once you absorb model serving, scaling, and provider-specific API differences yourself, product development slows down sharply. If model operations become bigger than the feature experiment, the entry cost is too high.
Early generative AI adoption meant self-hosting open-source models and managing GPUs and security entirely. This is why services like Bedrock, which let you consume model APIs without infrastructure burden, quickly became important.
Bedrock exposes foundation models from Amazon and multiple AI providers through a common API, and applications typically call them through Lambda or API Gateway. It lets you start quickly in a serverless way, supports private customization with your own data, and relies on IAM and CloudWatch for access control and usage monitoring.
Bedrock and SageMaker are both AI services, but they solve different problems. Bedrock focuses on calling prebuilt models, while SageMaker focuses on training and deploying your own models. If you want to add generative features quickly with prompts and application logic, look at Bedrock; if you need to own the training pipeline and model operations, look at SageMaker.
Well-suited for chatbots, summarization, classification, assisted writing, and RAG prototypes where model invocation is the core feature. Not a good fit when you need to train models on your own data or perform precise fine-tuning.