AWS vs Azure: A Decision Framework for Production Workloads

Your team just got approval for a new microservices platform. The CTO asks whether to go with AWS or Azure. You’ve used both—maybe AWS for a previous startup, Azure through an enterprise gig with Microsoft EA discounts. Both worked fine. Both had their quirks. But now you need to make a defensible recommendation that accounts for your specific workload patterns, team expertise, and long-term costs. “It depends” won’t fly in the architecture review.

This is where most cloud comparisons fail you. They’ll tell you AWS has more services (true), Azure integrates better with Microsoft tooling (also true), and both have Kubernetes offerings (obviously). What they won’t tell you is how to weigh these factors against your actual constraints: a team that knows Terraform but not ARM templates, a data pipeline that needs sub-100ms latency to on-prem systems, a compliance requirement that narrows your region choices.

I’ve sat through dozens of these decisions—some I got right, some I’d make differently with hindsight. The pattern that emerged isn’t about which cloud is objectively superior. It’s about building a framework that maps your workload requirements to platform strengths, then pressure-testing that mapping against organizational realities. The technical capabilities matter, but so does the procurement process your finance team already has in place.

Let’s start with the premise that changes how you approach this decision: for 95% of production workloads, both AWS and Azure are genuinely capable platforms. The question isn’t which is better—it’s which fits.

The Real Question Isn’t Which Is Better—It’s Which Fits

Every few months, a new article declares a winner in the AWS versus Azure debate. These comparisons typically stack feature lists, count data center regions, and tally managed services. By those metrics, both platforms score remarkably similar—because both platforms are remarkably similar for the vast majority of production workloads.

AWS commands roughly 31% of the cloud market. Azure holds about 25%. Together, they power millions of applications across every industry vertical imaginable. The uncomfortable truth that feature-comparison articles ignore: either platform will handle 95% of what you need to build.

So why does the decision still feel so consequential?

Beyond the Feature Checklist

The AWS-or-Azure question matters less than three questions that rarely appear in comparison guides:

What does your organization already own? Enterprise agreements, existing Active Directory infrastructure, and Microsoft 365 subscriptions create genuine cost advantages for Azure. Conversely, teams already running production on EC2 benefit from consolidated billing, reserved instance commitments, and institutional knowledge that takes years to accumulate.

What does your team already know? A senior engineer proficient in CloudFormation delivers value on day one. That same engineer takes months to reach equivalent productivity with ARM templates—and vice versa. Hiring pipelines, training investments, and operational runbooks all carry switching costs that never appear on a pricing calculator.

Which specific services are non-negotiable? Azure’s integration with the Microsoft ecosystem remains unmatched for enterprises standardized on .NET and SQL Server. AWS leads in serverless maturity, container orchestration options, and certain specialized services like SageMaker for ML workflows. These specific requirements—not general capability—drive defensible decisions.

💡 Pro Tip: Before evaluating any cloud platform, inventory your team’s certifications, your existing vendor agreements, and the three services your architecture cannot function without. This exercise reveals constraints that matter more than any benchmark.

A Framework for Practical Evaluation

Throughout this article, we’ll evaluate AWS and Azure across four dimensions that determine real-world success:

• Compute flexibility: How each platform handles VMs, containers, and serverless at scale
• Data service alignment: Where storage, database, and analytics offerings genuinely differ
• Network and hybrid capabilities: Critical considerations for enterprise connectivity
• Organizational fit: Cost modeling, team skills, and long-term operational implications

This framework won’t declare a universal winner. It will give you the structure to identify which platform wins for your specific situation—and the evidence to defend that recommendation.

Let’s start where most architectures begin: compute services and the foundational infrastructure decisions that cascade through everything else.

Compute Services: EC2 vs Azure VMs and Beyond

The compute layer forms the foundation of any cloud deployment, and both AWS and Azure offer mature, battle-tested services. Understanding the nuances between them helps you make informed provisioning decisions rather than arbitrary ones.

Instance Type Naming: Decoding the Alphabet Soup

AWS and Azure use different naming conventions that initially seem impenetrable but follow logical patterns.

AWS uses a format like m5.xlarge where m indicates the family (general purpose), 5 is the generation, and xlarge specifies the size. Azure follows Standard_D4s_v3 where D is the family, 4 indicates vCPUs, s means premium storage capable, and v3 is the generation.

Finding equivalents requires mapping by specs rather than names:

💡 Pro Tip: AWS Graviton (arm64) instances offer 20-40% better price-performance. Azure’s equivalent is the Ampere Altra-based Dpsv5 series. Factor architecture into your cost modeling.

Serverless: Lambda vs Azure Functions

Both platforms handle serverless compute well, but cold start behavior differs significantly in production.

Lambda cold starts range from 100ms to several seconds depending on runtime and VPC configuration. Azure Functions on the Consumption plan show similar patterns, but Azure’s Premium plan eliminates cold starts entirely through pre-warmed instances.

For latency-sensitive APIs, Lambda Provisioned Concurrency and Azure Functions Premium both solve the cold start problem—at additional cost. Lambda charges per provisioned instance-hour; Azure Premium charges per vCPU-second with a minimum instance count.

Container Orchestration: EKS vs AKS

EKS and AKS both run vanilla Kubernetes, but the managed experience differs. AKS provides the control plane free of charge while EKS costs $0.10 per hour per cluster ($73/month). This matters when running multiple clusters for dev/staging/production environments.

AKS integrates tightly with Azure Active Directory for RBAC, making it the natural choice for enterprises already invested in Microsoft identity. EKS uses IAM roles for service accounts (IRSA), which requires more configuration but offers fine-grained permissions.

Here’s equivalent Terraform for deploying a production-ready Kubernetes cluster on each platform:

module "eks" {
  source          = "terraform-aws-modules/eks/aws"
  version         = "19.15.3"

  cluster_name    = "production-cluster"
  cluster_version = "1.28"

  vpc_id          = module.vpc.vpc_id
  subnet_ids      = module.vpc.private_subnets

  eks_managed_node_groups = {
    workers = {
      min_size       = 2
      max_size       = 10
      desired_size   = 3
      instance_types = ["m5.xlarge"]
      capacity_type  = "ON_DEMAND"
    }
  }
}

resource "azurerm_kubernetes_cluster" "main" {
  name                = "production-cluster"
  location            = azurerm_resource_group.main.location
  resource_group_name = azurerm_resource_group.main.name
  dns_prefix          = "prod"
  kubernetes_version  = "1.28"

  default_node_pool {
    name                = "workers"
    min_count           = 2
    max_count           = 10
    node_count          = 3
    vm_size             = "Standard_D4s_v3"
    enable_auto_scaling = true
  }

  identity {
    type = "SystemAssigned"
  }
}

Notice the structural similarities—both define node pools with autoscaling, specify Kubernetes versions, and handle identity. The Azure configuration includes identity blocks inline while AWS handles IAM through separate resources or the module’s internal configuration.

Operational Differences

EKS add-ons (CoreDNS, kube-proxy, VPC CNI) require explicit management and versioning. AKS handles these components automatically with less visibility but less operational burden.

For logging and monitoring, AKS ships logs to Azure Monitor with minimal configuration. EKS requires setting up Fluent Bit or CloudWatch Container Insights as a separate deployment step.

Both platforms achieve compute parity for most workloads. The decision often comes down to your team’s existing expertise and the broader ecosystem you’re building within. Speaking of ecosystems, data services represent where these platforms show their most significant architectural differences.

Data Services: Where the Platforms Truly Diverge

Data services represent the sharpest differentiation between AWS and Azure. While compute and networking have largely converged in capability, database and analytics offerings reflect fundamentally different platform philosophies—and choosing the wrong platform for your data workload creates friction you’ll fight against for years.

Managed Relational Databases: Heritage Matters

Azure SQL Database carries three decades of SQL Server DNA. If your organization runs SQL Server workloads, Azure provides capabilities that AWS simply cannot match: native compatibility with existing T-SQL code, seamless integration with SQL Server Management Studio, and features like Hyperscale that scale to 100TB while maintaining full SQL Server semantics.

Amazon RDS supports SQL Server, but it’s a managed wrapper around the engine—not a native evolution of it. You’ll encounter subtle incompatibilities with advanced features, and the operational experience feels bolted-on rather than integrated.

The calculus flips for PostgreSQL and MySQL workloads. AWS Aurora redesigned these engines from the ground up for cloud architecture, delivering 3-5x throughput improvements over standard implementations. Azure’s flexible server offerings are competent but conventional—they’re managed databases, not reimagined ones.

💡 Pro Tip: If you’re migrating from on-premises SQL Server with heavy use of CLR integration, Service Broker, or cross-database queries, Azure SQL Managed Instance provides a migration path that RDS cannot. These features aren’t supported on AWS at all.

NoSQL and Purpose-Built Databases: Different Strengths

DynamoDB and Cosmos DB both deliver single-digit millisecond latency at scale, but they solve different problems well.

Cosmos DB offers five consistency models—from strong to eventual—giving you fine-grained control over the consistency-latency tradeoff. Its multi-model API support (SQL, MongoDB, Cassandra, Gremlin, Table) lets teams use familiar interfaces. For globally distributed applications requiring tunable consistency, Cosmos DB provides flexibility DynamoDB lacks.

DynamoDB wins on operational simplicity and predictable pricing. Its single-table design patterns, while requiring upfront modeling work, yield remarkably efficient query patterns. The combination of DynamoDB with DynamoDB Streams for change data capture creates event-driven architectures with minimal operational overhead.

Where AWS pulls definitively ahead: purpose-built databases for specific workloads. Neptune handles graph queries with native support for both Gremlin and SPARQL. Timestream provides purpose-built time-series storage with automatic data tiering and interpolation functions. DocumentDB offers MongoDB compatibility with better operational characteristics than self-managed clusters.

Azure covers these use cases through Cosmos DB’s multi-model approach, but a database that does everything adequately often loses to one that does your specific thing exceptionally.

Analytics and Data Lakes: Architectural Philosophies

The S3 + Athena + Glue stack embodies the AWS philosophy: discrete, composable services that you wire together. S3 provides storage, Glue catalogs metadata and handles ETL, Athena runs serverless queries. You build exactly what you need, paying only for what you use.

Azure Synapse Analytics takes the integrated platform approach. A unified workspace combines data integration, enterprise data warehousing, and big data analytics. For organizations wanting a single pane of glass across their analytics estate, Synapse reduces integration complexity.

Neither approach is objectively superior. Teams comfortable with distributed systems architecture often prefer AWS’s composability. Organizations prioritizing time-to-insight with smaller data engineering teams frequently find Synapse’s integration accelerates delivery.

The data services decision ripples outward into networking costs, team skills, and operational complexity. But before optimizing for data, you need to understand how these platforms handle the network fabric connecting your services—and how they bridge back to your existing infrastructure.

Networking and Hybrid Cloud: The Enterprise Consideration

Your networking architecture determines how workloads communicate—with each other, with on-premises systems, and with the internet. Both AWS and Azure provide mature networking primitives, but their design philosophies reflect different assumptions about where your existing infrastructure lives.

VPC vs VNet: Similar Concepts, Different Defaults

AWS Virtual Private Cloud (VPC) and Azure Virtual Network (VNet) solve the same fundamental problem: isolated network environments for your cloud resources. The core concepts—subnets, route tables, security groups, and network ACLs—exist in both platforms with comparable functionality.

The differences emerge in defaults and integration patterns. AWS VPCs require explicit configuration for most connectivity scenarios, giving you granular control but demanding more upfront planning. Azure VNets ship with certain conveniences baked in, particularly around name resolution and integration with Azure Active Directory.

For greenfield projects, this distinction matters less than you’d expect. For migrations, it matters significantly—Azure’s networking model maps more naturally to traditional enterprise network segmentation, while AWS’s model rewards teams comfortable with infrastructure-as-code and explicit dependency management.

Hybrid Connectivity: Direct Connect vs ExpressRoute

Dedicated network connections between your data centers and cloud providers bypass the public internet, delivering consistent latency and higher throughput for hybrid workloads.

AWS Direct Connect provides dedicated 1 Gbps, 10 Gbps, or 100 Gbps connections through a global network of colocation facilities. You establish BGP peering and manage routing policies with familiar network engineering practices.

Azure ExpressRoute offers similar dedicated connectivity with an additional option: ExpressRoute Global Reach enables direct routing between your on-premises locations through Microsoft’s backbone network. For organizations with multiple data centers, this creates a compelling hub-and-spoke topology without additional hardware.

💡 Pro Tip: Both services require 30-90 days for physical circuit provisioning. Start the procurement process early in your migration planning, and use site-to-site VPN as a temporary bridge.

The Microsoft Stack Advantage

Organizations running Active Directory, Microsoft 365, and Windows Server workloads find Azure’s integration compelling. Azure AD Connect synchronizes identities seamlessly. Conditional access policies span on-premises and cloud resources. SharePoint, Teams, and Dynamics 365 share the same backbone.

This integration reduces operational complexity for Microsoft-centric environments. Single sign-on works without custom federation. Group policies extend naturally. Your existing Microsoft Enterprise Agreement licensing often includes Azure credits and hybrid use benefits.

AWS for Multi-Cloud and Internet-First

AWS’s networking shines in multi-cloud architectures and internet-facing applications. Transit Gateway simplifies complex topologies across multiple VPCs and accounts. AWS’s peering agreements with major internet exchanges deliver consistently low latency for global user bases.

If your architecture spans multiple cloud providers or prioritizes internet-first delivery, AWS’s networking ecosystem provides more mature tooling for these patterns.

Your hybrid connectivity requirements and existing enterprise infrastructure create natural platform affinities—but they’re only one input to the decision. The next consideration hits budgets directly: building accurate cost models that account for the pricing differences between platforms.

Cost Modeling: Building Comparable Estimates

Comparing cloud costs between AWS and Azure isn’t as simple as matching instance types and checking hourly rates. The pricing structures, discount mechanisms, and hidden fees differ enough that a naive spreadsheet comparison can be off by 40% or more. Building accurate cost projections requires understanding how each platform structures pricing and where the unexpected charges hide.

Why Naive Pricing Comparisons Fail

Both platforms publish hourly rates for compute instances, but these numbers tell only part of the story. AWS prices EC2 instances separately from EBS storage, while Azure bundles temporary storage with VM pricing. AWS charges for Elastic IP addresses when they’re not attached to running instances; Azure charges for public IPs regardless of state. Data transfer pricing tiers differ, and the definition of “free” inbound traffic varies by service.

Instance type naming conventions further complicate direct comparisons. AWS’s m5.xlarge (4 vCPUs, 16GB RAM) maps roughly to Azure’s Standard_D4s_v3, but the underlying hardware generations, network bandwidth allocations, and burst capabilities don’t align perfectly. Memory-to-CPU ratios vary across equivalent tiers, and storage IOPS baselines differ between EBS gp3 volumes and Azure Premium SSD defaults.

Reservation and Savings Plan Strategies

The most significant pricing difference emerges in commitment discounts. AWS offers Reserved Instances (1 or 3 year) and Savings Plans (compute or EC2-specific), while Azure provides Reserved VM Instances and Azure Savings Plans. The flexibility and discount depths aren’t equivalent—a 3-year all-upfront EC2 Reserved Instance can save up to 72%, while Azure Reserved VM Instances max out around 65% for comparable commitments.

Flexibility differs substantially between platforms. AWS Savings Plans can apply across instance families and regions when using Compute Savings Plans, providing headroom for architectural changes. Azure Savings Plans offer similar cross-region flexibility but require more careful capacity planning. Organizations with unpredictable growth patterns may find AWS’s more granular commitment options easier to optimize incrementally.

Automating Price Comparison

Rather than manually navigating pricing calculators, pull pricing data programmatically for consistent comparisons. The following script fetches on-demand pricing for comparable instance types:

import boto3
import requests
from typing import Dict, Any

def get_aws_pricing(instance_type: str, region: str = "us-east-1") -> float:
    """Fetch AWS EC2 on-demand pricing via the Pricing API."""
    client = boto3.client("pricing", region_name="us-east-1")

    response = client.get_products(
        ServiceCode="AmazonEC2",
        Filters=[
            {"Type": "TERM_MATCH", "Field": "instanceType", "Value": instance_type},
            {"Type": "TERM_MATCH", "Field": "location", "Value": "US East (N. Virginia)"},
            {"Type": "TERM_MATCH", "Field": "operatingSystem", "Value": "Linux"},
            {"Type": "TERM_MATCH", "Field": "tenancy", "Value": "Shared"},
            {"Type": "TERM_MATCH", "Field": "preInstalledSw", "Value": "NA"},
        ],
        MaxResults=1
    )

    import json
    price_data = json.loads(response["PriceList"][0])
    on_demand = price_data["terms"]["OnDemand"]
    price_dimensions = list(list(on_demand.values())[0]["priceDimensions"].values())[0]
    return float(price_dimensions["pricePerUnit"]["USD"])


def get_azure_pricing(vm_size: str, region: str = "eastus") -> float:
    """Fetch Azure VM pricing via the Retail Prices API."""
    url = "https://prices.azure.com/api/retail/prices"
    query = (
        f"armRegionName eq '{region}' and "
        f"armSkuName eq '{vm_size}' and "
        f"priceType eq 'Consumption' and "
        f"contains(productName, 'Virtual Machines') and "
        f"contains(meterName, 'Spot') eq false"
    )

    response = requests.get(url, params={"$filter": query})
    items = response.json().get("Items", [])

    linux_prices = [i for i in items if "Windows" not in i.get("productName", "")]
    return linux_prices[0]["unitPrice"] if linux_prices else 0.0


## Compare similar instance types
comparisons = [
    ("m5.xlarge", "Standard_D4s_v3"),
    ("c5.2xlarge", "Standard_F8s_v2"),
    ("r5.large", "Standard_E2s_v3"),
]

for aws_type, azure_type in comparisons:
    aws_price = get_aws_pricing(aws_type)
    azure_price = get_azure_pricing(azure_type)
    diff_pct = ((azure_price - aws_price) / aws_price) * 100
    print(f"{aws_type} vs {azure_type}: AWS ${aws_price:.4f}/hr, Azure ${azure_price:.4f}/hr ({diff_pct:+.1f}%)")

Hidden Cost Multipliers

Data egress charges catch teams off guard consistently. AWS charges $0.09/GB for the first 10TB of outbound internet traffic; Azure charges $0.087/GB for the same tier. Cross-region data transfer adds another layer—AWS charges $0.02/GB between most US regions, while Azure charges $0.02/GB for inter-region traffic within the same geography. For data-intensive applications transferring terabytes monthly, these seemingly small per-GB differences compound into significant annual costs.

Support costs scale differently as well. AWS Business Support costs 10% of monthly charges (minimum $100), while Azure Standard Support runs $100/month flat. For organizations spending over $10,000 monthly, Azure’s fixed support pricing provides meaningful savings. Enterprise support tiers diverge further—AWS Enterprise Support starts at $15,000/month or 3% of usage, while Azure Premier Support uses custom pricing based on organizational scope.

Logging and monitoring costs often surprise teams during the first production billing cycle. CloudWatch Logs charges $0.50/GB for ingestion; Azure Monitor Logs charges vary by commitment tier but typically run $2.30/GB for pay-as-you-go. High-volume logging scenarios can shift the total cost picture significantly in AWS’s favor despite higher compute rates.

💡 Pro Tip: Include 15-20% overhead in your estimates for data transfer, logging, and monitoring services. These “invisible” costs accumulate quickly in production workloads.

Cost modeling provides the financial foundation for your decision, but the economic equation extends beyond infrastructure pricing to the team building on it.

Team Skills and Hiring: The Often-Ignored Factor

Technical capabilities matter, but your team’s ability to ship reliably matters more. The cloud platform your engineers know deeply will outperform the “objectively better” platform they’re learning on the job. This human factor deserves the same rigorous analysis as compute pricing or database features.

The Certification and Talent Landscape

AWS dominates the certification ecosystem with over 12 active certifications and a decade-long head start in training programs. The AWS Solutions Architect credential remains the most recognized cloud certification globally, making AWS-skilled engineers easier to find—and more expensive to hire.

Azure certifications have gained significant traction, particularly in enterprises already invested in Microsoft technologies. Engineers with .NET backgrounds often transition to Azure naturally, and the Microsoft Learn platform provides comprehensive free training. If your organization runs Active Directory, SharePoint, or Dynamics 365, you’ll find Azure skills overlap substantially with existing Microsoft expertise.

💡 Pro Tip: Don’t just count certifications on resumes. Engineers who’ve operated production workloads through an outage teach you more about a candidate than any credential.

CLI and SDK Ergonomics

Daily developer experience differs substantially between platforms. The AWS CLI follows a service-namespace pattern, while Azure CLI uses a resource-group-centric model with more verbose but arguably more readable commands.

## AWS: List EC2 instances in a region
aws ec2 describe-instances \
  --region us-east-1 \
  --filters "Name=instance-state-name,Values=running" \
  --query "Reservations[].Instances[].InstanceId" \
  --output table

## Azure: List VMs in a resource group
az vm list \
  --resource-group prod-workloads-rg \
  --show-details \
  --query "[?powerState=='VM running'].{Name:name, Size:hardwareProfile.vmSize}" \
  --output table

AWS commands tend toward brevity with heavy reliance on --query for JMESPath filtering. Azure commands are longer but often more self-documenting. Your team’s preference here affects daily productivity more than benchmarks suggest.

Infrastructure as Code: The Real Differentiator

CloudFormation and ARM templates both use JSON/YAML and both generate frustration. The practical question is how your team handles infrastructure today.

## Initialize Terraform with AWS provider
terraform init
terraform plan -var="environment=production" -out=tfplan

## Same workflow works for Azure—only provider configuration changes
## This portability makes Terraform the pragmatic choice for multi-cloud teams

## AWS-specific: Deploy with CloudFormation
aws cloudformation deploy \
  --template-file infrastructure/main.yaml \
  --stack-name prod-api-stack \
  --parameter-overrides Environment=production \
  --capabilities CAPABILITY_IAM

## Azure-specific: Deploy with Bicep
az deployment group create \
  --resource-group prod-workloads-rg \
  --template-file infrastructure/main.bicep \
  --parameters environment=production

Terraform abstracts both platforms effectively, but native tools offer deeper integration. CloudFormation handles IAM permissions and cross-stack references elegantly. Bicep provides superior type checking and a cleaner syntax than ARM templates, with excellent VS Code tooling.

Teams with strong Terraform experience maintain cloud mobility. Teams choosing native tools gain tighter platform integration at the cost of portability. Neither choice is wrong—but the choice should be deliberate.

Hiring Market Realities

AWS skills command premium rates in most markets, but supply is higher. Azure expertise is rarer but increasingly sought after as enterprises modernize. If you’re building a team from scratch, AWS offers a larger candidate pool. If you’re leveraging existing Microsoft-stack developers, Azure reduces ramp-up time significantly.

The platform your senior engineers advocate for is the platform they’ll maintain at 2 AM during an incident. That advocacy, grounded in real experience, carries weight beyond any feature comparison.

With team dynamics addressed, the final step is synthesizing these factors into an actionable scoring framework for your specific situation.

Making the Call: A Scoring Framework

After evaluating compute, data services, networking, costs, and team capabilities, you need a systematic way to synthesize your findings into a defensible recommendation. A weighted scoring framework transforms subjective preferences into documented, repeatable decisions.

The Weighted Evaluation Matrix

Create a scoring matrix with dimensions weighted according to your organization’s priorities. Each dimension receives a score from 1-5 for both platforms:

Adjust weights based on your constraints. A startup prioritizing speed-to-market increases compute and cost weights. An enterprise with existing Microsoft investments increases ecosystem alignment.

When AWS Wins

AWS emerges as the stronger choice when your requirements align with its strengths:

• Startup velocity matters most. AWS’s breadth of managed services—from Lambda to DynamoDB to SQS—lets small teams ship production systems without dedicated infrastructure engineers.
• You need specialized services. AWS leads in purpose-built databases (Neptune, Timestream, QLDB), ML infrastructure (SageMaker), and IoT platforms.
• Multi-cloud is the strategy. AWS’s dominance means more third-party tooling assumes AWS-first compatibility. Kubernetes on EKS integrates cleanly with cross-cloud orchestration patterns.

When Azure Wins

Azure becomes the clear choice under different conditions:

• Microsoft ecosystem runs deep. Organizations built on Active Directory, Microsoft 365, and Dynamics see immediate value from Azure’s native integrations. Azure AD conditional access policies extend seamlessly to cloud resources.
• Enterprise compliance is non-negotiable. Azure’s compliance certifications for government, healthcare, and financial services often arrive first for Microsoft-specific requirements.
• Hybrid is the reality, not the aspiration. Azure Arc and Azure Stack provide the most mature path for organizations that genuinely need workloads spanning on-premises datacenters and cloud.

When the Answer Is Both

Multi-cloud works when it solves real problems rather than creating theoretical flexibility:

• Disaster recovery across providers. Critical workloads replicated between AWS and Azure eliminate single-vendor failure modes.
• Best-of-breed services. Running Azure for corporate applications and AWS for customer-facing products leverages each platform’s strengths.
• Acquisition integration. Merging companies on different platforms rarely justifies immediate consolidation costs.

💡 Pro Tip: Multi-cloud succeeds when each platform serves distinct workloads with clear boundaries. Avoid splitting single applications across providers—the complexity tax outweighs theoretical benefits.

Document your scoring matrix and share it with stakeholders. The framework itself often matters more than the final score, giving leadership confidence that the recommendation rests on systematic evaluation rather than preference.

With your platform decision made, the real work begins: building the infrastructure, migrating workloads, and proving your framework predicted reality.

Key Takeaways

• Build a weighted scorecard across compute, data, networking, cost, and team fit—then document your reasoning for stakeholders
• Run Terraform or equivalent IaC deployments on both platforms for your specific workload before committing
• Factor in your organization’s existing Microsoft or AWS investments, as integration costs often outweigh raw service comparisons
• Model costs with realistic usage patterns including egress, support tiers, and reservation strategies rather than on-demand pricing