[AWS](https://aws.amazon.com/)

meta_title: What is AWS (Amazon Web Services)? The Full Explainer
meta_description: Amazon Web Services (AWS) is the leading cloud platform, offering compute, storage, databases, and AI tools for developers.
slug: aws
post_excerpt: AWS is Amazon’s cloud computing platform, offering on-demand compute, storage, databases, and AI services for businesses of any size.

tool_url: https://aws.amazon.com/
tool_logo: https://a0.awsstatic.com/libra-css/images/logos/aws_logo_smile_1200x630.png
tool_pricing: Freemium
tool_category: Cloud / IaC
tool_industry: Engineering
tool_similar: Google Cloud, Microsoft Azure, DigitalOcean, Oracle Cloud, IBM Cloud
tool_glossary: EC2, S3, Lambda, IaaS, Availability Zone, IAM

tool_features: |
Over 240 cloud services spanning compute, storage, networking, and databases
Amazon Bedrock for building generative AI applications on foundation models
Amazon SageMaker for training, fine-tuning, and deploying custom ML models
Global infrastructure across 39 geographic regions and 123 availability zones
Pay-as-you-go pricing with no upfront commitment
Fine-grained identity and access management for securing every resource

faq_1_q: What is AWS used for?
faq_1_a: AWS is used to run applications and infrastructure without owning physical servers. Teams use it to host websites, store data, run databases, train and deploy machine learning models, and build generative AI applications. It charges only for what you use, so a startup and a large enterprise can run on the same platform at very different scales.
faq_2_q: Is AWS free?
faq_2_a: AWS offers a free tier with limited usage of core services like EC2 and S3 for new accounts, useful for learning and small projects. Beyond that, AWS is metered and pay-as-you-go: you pay per hour, per gigabyte, or per request depending on the service, with no subscription required to get started.
faq_3_q: What is the difference between Amazon Bedrock and Amazon SageMaker?
faq_3_a: Bedrock gives developers API access to prebuilt foundation models for building generative AI apps quickly, without managing infrastructure. SageMaker is built for teams that need to train, fine-tune, and deploy their own custom machine learning models at scale. Many teams use Bedrock for inference and SageMaker for custom model development, since the two are complementary.

AWS, short for Amazon Web Services, is Amazon’s cloud computing platform. It rents out compute power, storage, databases, and hundreds of other infrastructure services over the internet, on a pay-as-you-go basis, so companies never have to buy or maintain their own physical servers. Launched in 2006, AWS now spans over 240 services across 39 geographic regions worldwide. For AI developers, AWS matters because it provides the compute and managed AI services, like Amazon Bedrock and SageMaker, that most production machine learning systems actually run on.

How AWS works

AWS delivers infrastructure as a service (IaaS), meaning you rent raw computing resources instead of buying hardware, and platform and software services built on top of that infrastructure.

• Compute: services like EC2 (Elastic Compute Cloud) give you virtual servers you can spin up or shut down in minutes, scaling to match demand.
• Storage: S3 (Simple Storage Service) stores any amount of data as objects, accessed over the internet and billed by usage.
• Serverless: AWS Lambda runs your code in response to events without you managing any server at all, billing only for the compute time actually used.
• Regions and availability zones: AWS infrastructure is split into geographic regions, each containing multiple isolated availability zones, so applications can stay online even if one data center fails.
• Identity and access management (IAM): every resource in AWS is protected by fine-grained permissions, controlling exactly who and what can access each service.

Everything in AWS is accessible through a web console, a command-line interface, or an API, which is what makes it possible to automate infrastructure entirely in code.

What you can build with AWS

• Generative AI applications: use Amazon Bedrock to call foundation models through a single API, adding retrieval-augmented generation with Knowledge Bases and safety controls with Guardrails, without hosting any model yourself.
• Custom ML pipelines: use Amazon SageMaker to train, fine-tune, and deploy your own machine learning models at scale, with managed infrastructure for experimentation and versioning.
• AI agents that take action: deploy agents through Bedrock AgentCore that can reason about a task, call external APIs, query databases, and complete multi-step workflows.
• Scalable web applications: run a full-stack app on EC2 or serverless Lambda functions, backed by managed databases, that automatically scale from a handful of users to millions.
• Document processing systems: extract structured data from unstructured documents like invoices, contracts, or medical records using Bedrock Data Automation.
• Global content delivery: serve static assets, videos, and APIs from edge locations close to your users, cutting latency for applications used worldwide.