The cloud provider decision feels weighty. It’s a long-term commitment with switching costs. Get it wrong, and you’re stuck with a suboptimal platform for years.
The reality is more forgiving. All three major providers can run most workloads well. The differences that matter are more specific than general “which is best” comparisons suggest.
The Honest Summary
AWS has the broadest service offering and largest ecosystem. If you need something obscure, AWS probably has it. The tradeoff is complexity—there are often multiple services that seem to do the same thing, and navigating the options takes experience.
Azure integrates deeply with Microsoft products. If you’re running Windows workloads, Active Directory, Office 365, or .NET applications, Azure offers integrations that are genuinely smoother. For non-Microsoft stacks, the advantage is less clear.
GCP has strong data and ML services, a cleaner interface, and better pricing for some workloads. It’s smaller, which means less ecosystem but also less complexity.
What Actually Matters
Skip the feature comparison tables. Most services you’ll use exist on all platforms. Instead, focus on these questions.
What’s your organization already using?
If you’re an Azure shop with Enterprise Agreement licensing, Azure makes financial and operational sense. If your team knows AWS, starting there reduces learning curve. Existing relationships and knowledge have real value.
What specific services do you need?
The generic services (compute, storage, databases) are equivalent across providers. Differentiation is in specialized services. AWS has the widest selection. Azure’s machine learning integration with enterprise data sources is strong. GCP’s BigQuery and AI platform are excellent.
Identify your specific requirements and check whether each provider supports them well—not just whether the service exists, but whether it’s mature and well-documented.
Where are your customers?
If you’re serving specific geographic regions, check provider presence. All three have global networks, but coverage and performance vary. Edge cases like specific compliance regions or emerging markets may favor one provider.
What’s your team’s experience?
Cloud platforms have learning curves. A team with AWS experience will be productive on AWS faster than learning a new platform. This matters more than theoretical advantages of alternatives.
AWS Specifics
Amazon Web Services holds the largest market share for good reason. The platform has been around since 2006, giving it a multi-year head start that translated into both service breadth and ecosystem depth. When you encounter a problem on AWS, someone else has almost certainly solved it before—and documented the solution.
Strengths:
- Broadest service portfolio by far, with over 200 fully-featured services spanning compute, storage, databases, machine learning, IoT, and specialized industry solutions
- Largest community and most documentation, making troubleshooting and hiring significantly easier
- Most third-party integrations and tooling, from monitoring solutions to deployment pipelines
- Mature enterprise features including compliance frameworks, support tiers, and enterprise agreements
Watch out for:
- Pricing complexity makes cost prediction hard—reserved instances, savings plans, spot pricing, and data transfer costs create a puzzle many organizations solve only through expensive trial and error
- Multiple overlapping services for similar purposes can create analysis paralysis (ECS vs EKS, Lambda vs Fargate, and so on)
- The sheer volume of options can be overwhelming without experienced guidance to navigate the decision matrix
Good for: Organizations that want the most options, need enterprise-grade everything, or require services that only AWS offers. If you’re uncertain and don’t have strong reasons pointing elsewhere, AWS is rarely the wrong choice—it’s the safe default that can handle whatever you throw at it.
Azure Specifics
Microsoft Azure’s strongest argument isn’t feature comparison—it’s integration. For organizations already invested in the Microsoft ecosystem, Azure offers a seamless extension of tools they already use daily. Active Directory becomes Azure AD with configuration changes rather than migration projects. The enterprise sales relationships Microsoft has cultivated over decades translate into Azure as well, with existing Enterprise Agreements often making Azure commercially attractive from day one.
Strengths:
- Microsoft ecosystem integration (Active Directory, Office 365, Dynamics 365) that creates a unified experience rather than bolted-on connections
- Strong hybrid cloud story with Azure Arc, which extends cloud management to on-premises infrastructure and even other clouds
- Good enterprise sales and support relationships, with existing Microsoft licensing agreements often reducing commercial friction
- Windows and .NET workloads are natural fit, with first-class support that competing platforms can’t match
Watch out for:
- Portal and documentation can be inconsistent, as if different teams built different parts without coordinating closely
- Some services feel less polished than AWS equivalents, particularly in areas where Microsoft entered the market later
- Naming and organization changes frequently enough that tutorials from two years ago may reference features that have been restructured or renamed
Good for: Microsoft shops, enterprises with existing Microsoft relationships, and hybrid cloud scenarios with on-premises Windows infrastructure. Fighting against an organization’s existing Microsoft ecosystem creates friction—embracing it with Azure often provides the smoothest path forward.
GCP Specifics
Google Cloud Platform takes a different strategic approach than its competitors. Rather than competing on breadth of services, GCP focuses on doing fewer things exceptionally well. The result is a platform that feels more coherent and developer-friendly, with interfaces that seem designed by people who actually use them. Google’s internal expertise in distributed systems and data processing shows throughout the platform.
Strengths:
- Cleaner, more consistent interface that reduces the cognitive load of navigating cloud infrastructure
- Strong data and analytics services (BigQuery, Dataflow) that often justify the platform choice on their own merits for data-intensive organizations
- Good pricing on committed use and sustained use discounts that apply automatically, making cost optimization more straightforward
- Kubernetes (GKE) is excellent, as you’d expect from the company that created Kubernetes—widely considered the best managed K8s offering available
Watch out for:
- Smaller service portfolio than AWS means you might occasionally need something that doesn’t exist or isn’t as mature
- Less enterprise sales presence can make procurement and relationship management feel less supported than with AWS or Azure
- Some services get deprecated unexpectedly, and Google’s history with consumer products creates enterprise anxiety (though cloud services have proven more stable)
- Smaller community means fewer Stack Overflow answers, fewer third-party integrations, and fewer engineers with deep experience to hire
Good for: Data-heavy workloads where BigQuery and Dataflow shine, teams that value developer experience and interface design, and organizations already using Google Workspace. GCP is underrated as a platform choice, particularly for teams willing to trade some breadth for depth and simplicity.
Multi-Cloud Considerations
“Multi-cloud” sounds strategic in executive presentations, but the reality involves substantial complexity that’s often underestimated. Running the same workload across providers requires abstraction layers that have their own costs, limitations, and learning curves. You end up building expertise in multiple platforms while not going deep enough in any of them, and the orchestration layer itself becomes a critical piece of infrastructure that needs maintenance.
That said, legitimate reasons for multi-cloud do exist:
- Specific services that only exist on one provider: If you need BigQuery’s capabilities but your primary workload runs on AWS, using both providers makes sense rather than forcing an inferior alternative
- Geographic requirements for data sovereignty: Some compliance frameworks or customer contracts may require data residency in regions where your primary provider doesn’t have presence
- Reducing vendor lock-in risk: This concern is often overstated—switching costs are real but not catastrophic—though for some organizations the risk mitigation justifies the complexity
- Acquisitions bringing in different providers: When you acquire a company running on a different cloud, forcing immediate migration often creates more disruption than maintaining both environments temporarily
If you don’t have a specific, concrete reason that fits one of these categories, single-cloud is simpler and more cost-effective. You can always expand later if needs genuinely change, and you’ll build deeper expertise faster by focusing on one platform.
The Switching Cost Question
Vendor lock-in concerns drive many cloud architecture decisions, but they’re often overblown in practice. Yes, switching providers requires effort—you’ll need to reconfigure networking, update deployment pipelines, retrain teams, and potentially rearchitect some components. But the core of most applications transfers reasonably well. Virtual machines are virtual machines. Object storage is object storage. Relational databases speak SQL regardless of which cloud hosts them.
The lock-in that genuinely matters comes from managed services specific to one provider: AWS Lambda’s ecosystem, Azure’s Active Directory integration, GCP’s BigQuery. These services create real dependencies. But here’s the key insight: those services also provide substantial value that typically justifies accepting the dependency. Using a provider-specific managed service often delivers better results than a portable alternative that requires more operational investment.
The risk of lock-in should be weighed against the certainty of additional complexity from avoiding it. Focus on building good applications that solve business problems rather than optimizing for hypothetical future migrations that may never happen. If you do need to migrate later, you’ll have more resources and better understanding of your actual requirements than you do today.
Making the Decision
For most organizations, a structured decision process cuts through the noise of endless feature comparisons:
Check existing commitments. Before evaluating features, look at what’s already in place. Licensing agreements, team expertise, organizational relationships, and existing infrastructure often point clearly to one provider. A team with three years of AWS experience starting fresh on Azure will lose productivity for months—that’s a real cost that feature comparisons don’t capture.
Identify required services. List the specific services you need, not generic categories like “compute” or “storage.” Do you need a specific machine learning framework? A particular compliance certification? A managed service that only one provider offers? Verify each provider supports your actual requirements adequately—not just that the service exists, but that it’s mature, well-documented, and fits your use case.
Evaluate cost realistically. Run the pricing calculators for your expected usage patterns, but understand their limitations. The cheapest provider varies dramatically by workload type. Compute-heavy applications, storage-heavy applications, and data-transfer-heavy applications will see very different pricing dynamics. Build in some margin for the costs you won’t anticipate.
Consider team experience. This factor is underweighted in most evaluations. Productivity on a familiar platform often beats theoretical advantages of alternatives. The best cloud provider is the one your team can use effectively, not the one that wins on paper.
Start with one and iterate. At some point, analysis becomes procrastination. Pick a provider based on the factors above and start building. You’ll learn what actually matters for your specific situation faster by using a platform than by continuing to evaluate. Course corrections are possible later if needed.
Our Observations
After working with all three major providers across various client projects, a few patterns have become clear.
AWS is the safe default. It has the most services, the most documentation, and the most people who know it. When we’re uncertain about the best choice for a client, AWS is rarely wrong. The breadth of services means you won’t paint yourself into a corner, and the depth of community knowledge means solutions to problems are usually a search away. The complexity is real, but it’s complexity in service of capability.
Azure makes sense when Microsoft integration genuinely matters to the organization. We’ve seen projects struggle unnecessarily because teams chose AWS when their entire identity infrastructure ran on Active Directory and their developers worked primarily in .NET. Fighting against an organization’s existing Microsoft investment creates friction that consumes time and budget that could go toward actual product development. When the Microsoft ecosystem is already in place, Azure extends it naturally.
GCP is underrated, particularly for teams that value developer experience and have data-intensive workloads. The platform is smaller, which means occasional gaps in services, but what exists is often more polished and easier to work with. For organizations doing serious data analytics or machine learning work, the BigQuery and Vertex AI capabilities frequently justify the platform choice on their own merits.
The honest truth is that any of these providers can run most workloads well. The differences that matter are specific to your situation—your team, your existing investments, your particular requirements. Pick one based on your actual constraints and focus on building product instead of endlessly evaluating platforms. The best cloud provider is the one that lets you ship software.