What is AWS?

Amazon Web Services (AWS) is a public cloud computing platform launched by Amazon in 2006. It provides on-demand access to infrastructure and platform services such as compute, storage, networking, databases, analytics, machine learning, and security.

At its core, AWS replaces the need for organizations to own and manage physical data centers. Instead of purchasing servers, storage, and networking equipment upfront, teams consume infrastructure as software, paying for what they use and scaling resources dynamically.

AWS pioneered this model at global scale. While cloud computing concepts existed before 2006, AWS was the first provider to make elastic, programmable infrastructure broadly available through APIs.

Over time, AWS evolved from “infrastructure on demand” into a comprehensive platform that supports full application lifecycles, from experimentation to regulated, production-grade systems.

What problems does AWS solve?

AWS is not just a hosting platform. It solves several structural problems that traditional infrastructure struggles with. First, it removes capital expenditure barriers. Companies no longer need to forecast hardware needs years in advance or overprovision to handle peak demand. Infrastructure becomes elastic and usage-based.

Second, AWS standardizes infrastructure operations. Provisioning servers, networks, and storage becomes repeatable, automated, and version-controlled. This is foundational for DevOps and modern delivery practices.

Third, AWS enables global reach by default. Applications can be deployed across regions and availability zones with built-in redundancy, without negotiating contracts with multiple data center providers. 

Finally, AWS shifts infrastructure risk. Hardware failures, physical security, and data center operations are abstracted away, allowing teams to focus on system design, reliability, and business logic. In practice, this means teams spend less time “keeping the lights on” and more time designing systems that can evolve safely under growth, compliance, and cost constraints.

When did AWS emerge and why did it matter?

AWS officially launched with services like Amazon S3 (storage) and EC2 (compute) in 2006. These services introduced two ideas that changed infrastructure permanently: infrastructure could be provisioned programmatically; capacity could scale up and down in minutes, not months.

This mattered because it aligned infrastructure with software development cycles. Instead of infrastructure being a constraint, it became part of the development workflow. Over time, AWS expanded from basic infrastructure to higher-level services such as managed databases, serverless computing, analytics, and AI tooling.

This expansion reflected a broader shift: infrastructure stopped being a separate operational concern and became a core part of application architecture and business delivery.

What types of companies does AWS make sense for?

AWS is used by startups, SMBs, and large enterprises, but for different reasons. For startups and early-stage companies, AWS reduces time to market. Teams can experiment, iterate, and scale without upfront infrastructure investment.

For SMBs, AWS provides enterprise-grade reliability and security that would be difficult to build in-house, while allowing gradual growth. For large enterprises, AWS enables modernization, global expansion, and the gradual replacement of legacy data centers, often through hybrid or multi-account architectures.

That said, AWS is not automatically the best choice for every organization. Teams without cloud governance, cost visibility, or operational maturity can struggle with complexity and unpredictable spending.

In other words, AWS amplifies organizational maturity: disciplined teams gain leverage while undisciplined ones accumulate risk faster.

AWS pricing: what does it really cost?

AWS pricing is usage-based and varies by service, region, and configuration. There is no single “AWS price”. 

At a high level:

• Amazon Elastic Compute Cloud (EC2): billed per second or hour, depending on instance type.

• Amazon Simple Storage Service (S3): billed per GB stored and data transferred. 

• Managed services: priced based on capacity, usage, or requests.

For order-of-magnitude reference:

• Small production workloads often start in the hundreds of dollars per month.

• Growing SaaS platforms commonly reach thousands to tens of thousands per month.

• Large-scale systems can spend hundreds of thousands or more, depending on architecture and traffic.

The challenge is not raw price, but cost visibility and control. AWS offers tools like Cost Explorer and Budgets, but financial discipline must be designed into architecture and workflows.

Without clear ownership, tagging, and architectural constraints, cloud costs tend to reflect organizational behavior more than technical necessity.

Real advantages of AWS

AWS offers several strengths that explain its continued market leadership. 

Service breadth and maturity: AWS has the widest portfolio of cloud services, many of which are battle-tested at massive scale.

Global infrastructure: dozens of regions and availability zones worldwide.

Ecosystem and talent availability: AWS skills are widely available in the market, reducing hiring and vendor risk.

Operational flexibility: fine-grained control over networking, security, and compute.

This combination makes AWS particularly attractive for organizations that value flexibility and long-term optionality over simplicity.

Real limitations and trade-offs of AWS

AWS also introduces real challenges that decision-makers should understand. 

Complexity: the service catalog is large, and poorly designed architectures can become hard to reason about.

Cost opacity: without governance, costs can grow faster than expected.

Vendor lock-in: some managed services are difficult to migrate away from.

Operational burden: flexibility comes with responsibility. AWS does not design architecture for you.

These limitations are not failures of AWS, but consequences of its power and flexibility.

The platform assumes a certain level of architectural and operational discipline. When that discipline is missing, complexity becomes visible very quickly.

AWS vs Azure vs GCP: a decision-level comparison

Below is a high-level comparison focused on strategic differences, not feature checklists.

AWS is often chosen for flexibility and scale, Azure for Microsoft-centric enterprises, and GCP for data-heavy or AI-first teams.

The “best” choice depends less on features and more on existing constraints, organizational skill sets, and long-term strategy.

Is AWS a tool or an operating model?

AWS is best understood not as a tool, but as an operating model for infrastructure and delivery. It requires teams to think in terms of:

• Architecture over servers.

• Automation over manual operations.

• Governance over ad-hoc decisions.

• Cost as a design constraint.

Organizations that treat AWS as “just a place to run VMs” rarely capture its benefits. Those that treat it as a system for building, operating, and evolving software at scale tend to succeed. In this sense, AWS does not simplify reality. It makes reality explicit.

FAQ: Frequently Asked Questions about AWS

What is AWS and how does it work?

AWS is a public cloud platform that delivers infrastructure and managed services via APIs. Companies provision compute, storage, networking, and services as software, scaling on demand and paying only for what they use.

What problems does AWS solve for companies?

AWS removes upfront infrastructure investment, standardizes operations through automation, enables global deployment, and shifts physical infrastructure risk away from internal teams, allowing focus on reliability, architecture, and business logic.

Is AWS expensive?

AWS is not inherently expensive, but poor governance can drive high costs. Pricing scales with usage and architecture. The main challenge is cost visibility and discipline, not the platform itself.

Is AWS good for startups and SMBs?

Yes, when speed, scalability, and reliability are priorities. AWS accelerates time to market and provides enterprise-grade infrastructure, but teams need basic governance to avoid complexity and cost surprises.

How is AWS different from Azure and Google Cloud?

AWS offers the broadest and most mature service portfolio. Azure fits Microsoft-centric environments, while Google Cloud excels in data and analytics. The right choice depends on organizational context.

When does AWS not make sense?

AWS may be a poor fit for stable, low-change workloads or teams without cloud governance and operational capacity. Simpler hosting models can be more cost-effective in these cases.

Is AWS a tool or an operating model?

AWS is an operating model. It requires automation, governance, cost awareness, and continuous change. Treating it only as infrastructure limits its value.

Closing perspective

AWS remains the reference platform for public cloud computing, not because it is simple, but because it reflects how modern systems actually operate: distributed, automated, and constantly evolving.

The real question for leaders is not “should we use AWS”?, but “are we ready to operate systems at the level of discipline AWS requires”? Cloud platforms amplify decisions. AWS is no exception.

Learn how production-ready teams structure AWS for scale, security, and resilience. Talk to us today.