AWS vs Azure and GCP in 2025: the technical duel decoded

In 2025, the cloud is no longer a binary choice between "on-premises" and "public cloud". It is a multi-platform ecosystem, with strong requirements for security, latency, sovereignty, cost, and speed of innovation. AWS, Azure, and Google Cloud Platform (GCP) largely dominate the market, but their technical positioning diverges on key points that influence architecture, time-to-market, and governance.

This article offers an in-depth technical reading of the AWS vs Azure vs GCP duel. The goal is not to choose a "winner", but to explain why each platform excels in certain areas and how these choices concretely impact modern systems, from microservices to data mesh and generative AI.

Technical landscape of platforms in 2025

AWS maintains an unmatched breadth of catalog, with a wider range of services than its competitors. Its technical strategy is based on modularity, service granularity, and a culture of "primitives" that enable bespoke architectures. This appeals to teams that want fine-grained control over performance, resilience, and costs, but it requires more advanced architecture expertise.

Azure, on the other hand, capitalizes on native integration with the Microsoft ecosystem. Azure's technical positioning in 2025 is clear: maximize compatibility for hybrid and enterprise-friendly environments, with simplified governance. This reduces friction in large organizations already anchored in Active Directory, Windows Server, .NET, or Microsoft 365.

GCP differentiates itself through strength in data, AI, and network engineering. The platform has historically bet on the performance of managed services like BigQuery, Cloud Spanner, or Vertex AI. In 2025, GCP positions itself as a preferred choice for data-intensive workloads and analytical pipelines, with a developer experience strongly oriented toward productivity.

Compute, containers, and orchestration

On the compute front, AWS remains the most flexible option thanks to EC2, a very wide variety of instances, and continuous hardware innovation (Graviton, Nitro). This richness allows fine optimization of performance and costs, especially for demanding or highly specific workloads. The downside is higher operational complexity.

Azure offers a more opinionated experience with VM Scale Sets, Azure Kubernetes Service (AKS), and an ecosystem well integrated with Microsoft tooling. The strong point is management consistency with Azure Arc, which unifies control of Kubernetes clusters on-premises and in the cloud. This meets the need for hybrid environments, very present in regulated sectors.

GCP, thanks to Google Kubernetes Engine (GKE), remains a reference in Kubernetes orchestration. The stability, operational simplicity, and advanced features of GKE explain its adoption by DevOps teams and internal platforms. In microservices architectures, the GCP ecosystem enables faster delivery with less overhead.

// Example call to a serverless service via an SDK (pseudo-structure)
// Goal: illustrate the simplicity of a "function + event" model
const handler = async (event) => {
  const payload = JSON.parse(event.body || "{}");
  const result = await processOrder(payload);
  return { statusCode: 200, body: JSON.stringify(result) };
};

Data, AI, and analytics: the major battlefield

In 2025, data is the primary strategic asset. AWS offers a broad palette (Redshift, Athena, Glue, Lake Formation), capable of covering almost all scenarios. The approach is modular: the data architecture is assembled as a set of services. This gives precise control, but requires expert design to avoid fragmentation.

GCP stands out through coherence across the data chain. BigQuery remains a reference in serverless analytics, and integrates naturally with Dataflow, Dataproc, and Vertex AI. This coherence reduces the time to implement a complete analytics pipeline. The trade-off is a cost model that is highly dependent on query volumes.

Azure is making strong progress, notably through Synapse Analytics and Fabric. The "single data platform" logic aims to simplify governance and supervision. The positioning is clear: offer an integrated data suite, consistent with Microsoft BI and reporting tools.

// Simple example of batch processing in pseudo-code
// The goal is to show a standardized data pipeline
async function runBatchJob(inputUri, outputUri) {
  const data = await loadData(inputUri);
  const cleaned = data.filter(row => row.isValid);
  await writeData(outputUri, cleaned);
}

Security, compliance, and governance

AWS offers highly granular security mechanisms: IAM, Organizations, KMS, GuardDuty, or Macie. The security model is powerful, but requires a clear governance architecture. It is easy to multiply accounts and policies, but hard to keep consistency without a solid framework.

Azure, by contrast, relies on native integration with enterprise identity and policies via Azure Active Directory, Microsoft Defender for Cloud, and Azure Policy. In regulated environments, this integration is a major advantage: it reduces compliance effort and speeds up audits.

GCP is recognized for its security-by-default innovations, notably with BeyondCorp and an advanced zero-trust approach. This philosophy can be very effective for organizations that want intrinsic security, but it often demands a deeper cultural transformation.

Hybrid and multi-cloud: the operational reality

Hybrid cloud is no longer an exception. In 2025, most large companies combine multiple clouds and internal infrastructures. AWS addresses this need with Outposts, EKS Anywhere, and a "cloud-everywhere" logic, but the ecosystem is often perceived as more complex to integrate into existing information systems.

Azure is historically the most hybrid-ready. Azure Arc, Azure Stack, and unified management capabilities make it an obvious choice for companies that want continuity between on-premises and cloud. This strength explains why Azure is often favored in progressive migrations.

GCP was long seen as less mature on hybrid, but the Anthos strategy has strengthened its credibility. Anthos provides a multi-cluster and multi-cloud management layer, useful for companies that want to standardize Kubernetes and avoid vendor lock-in.

// Example of a feature flag for a multi-cloud deployment
const useCloudProvider = (region) => {
  if (region === "eu") return "azure";
  if (region === "us") return "aws";
  return "gcp";
};

Costs, performance, and optimization

Cost is a technical dimension as much as a financial one. AWS offers very fine-grained purchasing options (Reserved Instances, Savings Plans), which allows strong optimization of stable workloads. But this flexibility implies a mature FinOps strategy, otherwise costs can explode quickly.

Azure offers notable advantages for companies already Microsoft customers, notably through the Azure Hybrid Benefit. This can reduce license costs and make Azure more competitive, especially in a Windows or SQL Server context. GCP, for its part, relies on automatic discounts (Committed Use Discounts) and often more transparent billing.

Performance depends on architectural choices. AWS excels in hardware variability, Azure in integration consistency, GCP in network efficiency. The technical positioning of 2025 shows that performance is not only raw compute, but also the ability to reduce operational friction.

// Example of application metrics collection (pseudo-code)
function recordMetrics(operation, durationMs) {
  console.log(JSON.stringify({
    op: operation,
    duration: durationMs,
    timestamp: Date.now()
  }));
}

How to choose in 2025: technical criteria

Choosing a platform is not limited to functional richness. You must evaluate team maturity, compatibility with existing systems, and the nature of workloads. For highly customized microservices architectures, AWS often remains a natural choice thanks to its granularity. For a company already anchored in Microsoft, Azure reduces transition costs. For advanced data and AI needs, GCP can offer higher productivity.

The "why" behind the choice is as important as the "how". A company aiming for speed to launch might favor GCP or Azure for their integration and coherence. An organization seeking fine control over performance and availability can rely on AWS. The key is to map priorities: latency, compliance, time-to-market, budgets, and internal skills.

Finally, the choice is not irreversible. More and more companies adopt a best-of-breed logic. This model requires strict governance, but it can maximize overall performance. From this perspective, architecture must be designed to limit excessive dependence on a proprietary service.

// Example of an abstraction strategy to limit lock-in
class StorageAdapter {
  constructor(provider) {
    this.provider = provider;
  }
  async putObject(key, data) {
    return this.provider.put(key, data);
  }
}