What is AWS used for? The main ways businesses use it
AWS is used for almost anything computing-related that used to require your own servers — hosting websites and applications, storing and backing up data, running databases, big-data analytics, machine learning, and much more, all rented on demand and paid for by usage. Rather than being one product, AWS is a catalogue of hundreds of services that organisations combine to build whatever they need, from a single personal blog to the backend of a global bank. This article walks through the main categories of real-world use, who actually relies on AWS day to day, why organisations choose it over running their own hardware, and an honest note about just how much of the catalogue any one user actually touches.
Hosting websites and applications
The most common use of AWS is running the servers behind a website or application. Instead of buying physical machines, a team rents virtual servers through a service like Amazon EC2 (see our plain-English guide to what EC2 is) and can resize or multiply them in minutes as traffic changes. This covers everything from a small company site to an application serving millions of people, without anyone owning a single physical machine.
Alongside compute, most applications need somewhere to serve static content quickly and reliably worldwide, and AWS offers content delivery and load-balancing services for exactly that. The pattern is the same across scales: rent only the capacity you are using right now, and add more the moment demand grows.
Storing and backing up data
AWS is heavily used simply as a place to keep data — documents, images, videos, application files, backups of on-premises systems — using object storage services such as Amazon S3 (our guide to what S3 is explains the concept in depth). Because storage is billed by what you actually keep, it scales from a handful of files to enormous archives without any upfront commitment to disk space.
This is also one of AWS’s quieter but most important uses: disaster recovery. Businesses replicate critical data and even entire systems into AWS so that if their own data centre fails, they can recover quickly rather than losing everything — a use case that rarely makes headlines but underpins a great deal of enterprise cloud adoption.
Databases, analytics and machine learning
Beyond raw storage, AWS runs the databases behind applications — managed relational databases for traditional structured data and managed NoSQL databases for large-scale, flexible workloads — so teams do not have to install, patch or scale database software themselves.
On top of stored data, organisations use AWS for big-data analytics (see our explainer on what big data is) — querying enormous datasets to find patterns — and for machine learning, from training custom models to calling ready-made AI services for tasks like image recognition or language processing. These are workloads that would once have required specialist hardware and teams, now available as a rented service.
Networking and development environments
AWS also provides the networking layer that connects everything together — private virtual networks, secure connections back to an organisation’s own offices, and traffic management between services — and identity tools that control who and what can access any of it.
A less visible but very common use is development and testing: spinning up temporary environments to build and test software, then shutting them down the moment they are no longer needed, so a team never pays for idle test infrastructure.
Who actually uses AWS
The honest answer is almost everyone, at very different scales. Solo developers and startups use AWS because it removes the upfront cost of hardware entirely — you can build a real product for the cost of what it consumes, not what you predicted you would need. Established enterprises use it to modernise ageing infrastructure and to expand into new regions without building new data centres. Government agencies and highly regulated industries use it too, under strict compliance and security controls the platform is built to support.
What draws all of them is the same combination: no capital spent on hardware, capacity that expands and contracts with real demand, billing tied to actual usage rather than ownership, and access to data centres around the world without building any of them.
A catalogue, not a product — and where to start
It is worth being honest that “AWS” is not one thing to learn. It is a vast catalogue of services, and most individuals and even most companies only ever use a handful of them well — a compute service, a storage service, a database, and a few supporting pieces, combined to solve their particular problem. Nobody uses all of AWS; that would not even make sense.
This is exactly why AWS certification is structured the way it is: the foundational Cloud Practitioner exam covers the core services and concepts that apply everywhere, and the associate-level exams — Solutions Architect included — go deeper into designing real systems from these building blocks. Learning AWS well means learning the handful of services relevant to what you are building, not memorising the entire catalogue.
Original practice questions, timed mock exams and revision notes. No card, nothing to pay.