AWS vs Azure

Here is how the core AWS services map directly to their Azure counterparts across key infrastructure categories:

Core Service Mapping

Category	AWS Service	Azure Equivalent	Description
Compute	EC2	Virtual Machines (VMs)	On-demand virtual instances/servers.
	AWS Lambda	Azure Functions	Event-driven serverless compute.
	Elastic Beanstalk	Azure App Service	Managed PaaS for web app deployment.
Containers	EKS	Azure Kubernetes Service (AKS)	Managed Kubernetes engine.
	ECS / Fargate	Azure Container Apps / ACI	Serverless container execution.
	ECR	Azure Container Registry (ACR)	Docker/OCI image repository.
Storage	S3	Azure Blob Storage	Scalable object storage.
	EBS	Azure Managed Disks	Block storage attached to compute instances.
	EFS / FSx	Azure Files	Fully managed network file shares (NFS/SMB).
	S3 Glacier	Azure Archive Storage	Cold/archival storage for backups.
Databases	RDS	Azure SQL Database / Flexible Server	Managed relational databases (PostgreSQL, MySQL, SQL Server).
	DynamoDB	Azure Cosmos DB	Fully managed NoSQL key-value/document database.
	ElastiCache	Azure Cache for Redis	In-memory caching.
Networking	VPC	Virtual Network (VNet)	Isolated private cloud networking.
	Route 53	Azure DNS	Managed DNS routing service.
	Direct Connect	Azure ExpressRoute	Dedicated private link to on-premise hardware.
	ALB / NLB	Application Gateway / Load Balancer	Layer 7 (HTTP/HTTPS) and Layer 4 load balancing.
Identity & Security	AWS IAM	Microsoft Entra ID (Azure AD) + RBAC	Identity management, auth, and role definitions.
	AWS KMS	Azure Key Vault	Key, secret, and certificate management.
	AWS Shield / WAF	Azure DDoS Protection / Web Application Firewall	DDoS defense and application security.
Analytics & AI	Redshift	Azure Synapse Analytics	Cloud data warehousing.
	Amazon SageMaker	Azure Machine Learning	Platform for building, training, and deploying ML models.
	AWS Glue	Azure Data Factory	Managed ETL and data integration pipelines.

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Key Structural Differences

• Identity Model: AWS centers permissions around IAM policies attached to users/roles via JSON documents. Azure heavily relies on Microsoft Entra ID with Role-Based Access Control (RBAC) applied across standard scope hierarchies (Management Groups → Subscriptions → Resource Groups → Resources).
• Resource Management: Azure mandates that every resource resides within a Resource Group for unified lifecycle management. AWS tags resources globally or groups them logically, but lacks the strict single-container grouping model native to Azure.

for most greenfield projects, standard microservices, and general container workloads, Fargate is the default best choice.

The reduced configuration, lack of node management (no OS patching, no AMI rotations, no cluster autoscaling setup), and per-task scaling make it the lower-overhead option for developer velocity.

However, saying it’s only when resources are "mostly pegged" misses a few critical non-cost boundaries where Fargate simply cannot be used or becomes significantly more expensive.

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Non-Cost Constraints (When Fargate Is Not an Option)

Sometimes you must use EC2 regardless of price or utilization:

1. GPU / Specialized Hardware Workloads: Machine learning inference/training (NVIDIA GPUs), high-performance media encoding, or AWS Inferentia/Trainium hardware require EC2 instance types. Fargate doesn't support GPU pass-through.
2. Custom Daemon / Host-Level Monitoring: If you rely on daemonsets/agents that must run at the host OS level (e.g., specific kernel-level eBPF tracing, custom security agents, or system-wide log forwarders), Fargate’s serverless boundary prevents host access. Sidecar containers are required on Fargate instead.
3. Privileged Mode & Kernel Tweaks: Tasks requiring Linux --privileged access, custom kernel module loading, raw network sockets, or sysctl modifications are blocked by Fargate’s isolation boundary.
4. Extreme Local Disk I/O: Fargate task storage tops out at 200 GB of ephemeral storage. If your app requires terabytes of high-speed local NVMe scratch disk (like i3en or z1d instances), you need EC2.

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Cost Decision Matrix

                    Is the workload continuous & predictable?
                                  /          \
                                 /            \
                                YES            NO (Bursty / Batch / Variable)
                               /                \
      Are you running high-volume baseline?      -->  Use FARGATE
                 /          \                         (Zero idle capacity waste)
                /            \
              YES             NO
              /                \
    Are you using 3-yr Savings  -->  Use FARGATE 
    Plans + tight bin-packing?       (Engineering time > ~20% savings)
            /
          YES
          /
     Use EC2
(Save up to 50%+)

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Summary Checklist

Dimension	Default Choice	Shift to EC2 when...
Operational Effort	Fargate (Zero node maintenance)	You already have robust node automation (e.g., Karpenter/Cluster Autoscaler) and dedicated DevOps resource.
Scale Mechanism	Fargate (Fast task provisioning)	Scaling is predictable and you can handle warm node pools.
Hardware	Fargate (Standard CPU/ARM + RAM)	You require GPUs, host storage, or kernel/privileged access.
Cost at Scale	Fargate (No waste from idle nodes)	Your monthly compute bill is large enough ($5k–$10k+/mo) that saving 30–50% pays for the engineering time to manage nodes.