Multi-Cloud Cost Optimization Strategies for AWS and Azure

Introduction

In Part 1, we focused on why multi-cloud cost management is hard and how to build a unified visibility and monitoring framework. With that foundation in place, the logical next question is:

“Now that I can see my spend across AWS and Azure, how do I actually reduce it in a structured way?”

Part 2 answers that question.

In this part, we move from visibility to action. We will walk through step-by-step optimization workflows for both AWS and Azure, and then explain how to compare costs between them, so you can place each workload on the most cost-effective platform.

The structure is intentional:

• First, optimize within each cloud
• Then, compare between clouds
• Finally, use that information for smart workload placement

AWS Cost Optimization – Step-by-Step

Step 1: Establish a Baseline for AWS Spend

Before changing anything, you need to understand where the money is going.

Key actions:

• Use AWS Cost Explorer to break down costs by:
  • Service (Amazon EC2, Amazon S3, Amazon RDS, etc.)
  • Linked accounts
  • Tags such as Environment and CostCenter
• Identify:
  • Top 3 most expensive services
  • Top 10 costliest accounts or projects
  • Any unusual spikes in the last 30–90 days

Outcome: You know which areas to target first (usually Amazon EC2, Amazon RDS, Amazon S3, and data transfer).

Step 2: Optimize AWS Compute (EC2) – Commitments and Rightsizing

For most organizations, Amazon EC2 is the single largest cost driver. Optimization here has the biggest impact.

Analyze Instance Utilization

• Use Amazon CloudWatch metrics and AWS Compute Optimizer to check:
  • Average CPU utilization
  • Memory utilization (where available)
  • Network I/O
• Look for instances that:
  • Run at very low CPU (e.g., under 20% over a long period)
  • Are idle during nights or weekends

Are oversized (e.g., m5.4xlarge but using m5.large capacity)

Rightsize and Reclassify

Based on the analysis:

• Downgrade large instances to smaller sizes or cheaper families.
• Switch to burstable instances (t3, t4g) for low, spiky workloads.
• Shut down or terminate truly unused resources (old Dev/POC instances).

Use Savings Plans and Reserved Instances

Once workloads are stable and right-sized, introduce commitment-based savings.

• For dynamic or mixed workloads:
  • Use Compute Savings Plans to cover a portion of your steady usage.
• For very predictable usage:
  • Use Reserved Instances (Standard RIs) for specific instance families and regions.

A simple approach:

1. Calculate your stable baseline (e.g., the average of always-on usage over the last 3–6 months).
2. Cover 50–70% of that baseline with Savings Plans.

Use Spot Instances for Fault-Tolerant Workloads

For workloads that can be interrupted:

• Use Spot Instances via:
  • Auto Scaling groups with mixed instance policies
  • Amazon ECS/Amazon EKS worker nodes that tolerate restarts
  • Analytics, batch processing, CI/CD runners

This can dramatically reduce cost, especially in data processing workloads.

Step 3: Optimize AWS Storage and Backups

Storage is often “set and forget”, which makes it a silent cost leak.

Amazon S3 Optimization

• Enable Amazon S3 Storage Class Analysis to understand access patterns.
• Based on this:
  • Use Amazon S3 Intelligent-Tiering for data with unknown or variable access patterns.
  • Move old/unaccessed data to cheaper tiers (Infrequent Access, Glacier, Glacier Deep Archive).
• Create Lifecycle Policies for:
  • Logs older than X days → IA
  • Data older than Y months → Glacier

EBS Optimization

• Identify and delete unattached EBS volumes.
• Consolidate or shrink oversized volumes where utilization is low.
• Consider switching from older volume types to gp3, as it can offer equivalent performance at a potentially lower cost.

Snapshot Management

• Implement automated snapshot retention policies:
  • Keep daily snapshots for 7–14 days
  • Weekly snapshots for a certain number of weeks

Step 4: Control AWS Database Costs (Amazon RDS, Amazon DynamoDB)

Amazon RDS

• Right-size Amazon RDS instances based on CPU, memory, I/O, and connections.
• Enable storage autoscaling where appropriate, but monitor growth closely.
• For stable production workloads, use Amazon RDS Reserved Instances.

Amazon DynamoDB

• Monitor read/write capacity and evaluate if:
  • You should move from on-demand to provisioned capacity with autoscaling (or vice versa).
  • You can use DAX caching to reduce direct table reads.

Azure Cost Management Techniques – Step-by-Step

We now mirror a similar approach on Azure, utilizing the native tooling and licensing benefits.

Step 1: Establish a Baseline with Azure Cost Management

• Use Cost Analysis to segment costs by:
  • Subscription
  • Resource group
  • Tags (Environment, CostCenter, Project)
• Identify:
  • Top services (Virtual Machines, Storage, SQL Database, etc.)
  • Largest consuming resource groups
  • Any cost anomalies over recent months

This indicates where optimization efforts are most effective.

Step 2: Use Azure Reserved Instances for Stable Workloads

For virtual machines that run continuously:

• Analyze VM usage over the past 3–12 months.
• Commit to 1-year or 3-year Azure Reserved VM Instances where suitable.

Key points:

• Choose the right scope:
  • Single subscription
  • Shared scope (across subscriptions)
  • Management group level for large enterprises
• Enable instance size flexibility whenever possible, allowing reservations to apply across similar SKUs.

Step 3: Leverage Azure Hybrid Benefit

This is a major cost differentiator.

If your organization already owns Windows Server and SQL Server licenses with Software Assurance:

• Apply Azure Hybrid Benefit on:
  • Windows VMs
  • Azure SQL Database
  • Azure SQL Managed Instances

This reduces the compute costs significantly because you reuse your existing licenses instead of paying for them again on Azure.

Step 4: Optimize Azure Storage and Database Costs

Azure Storage (Blob, Files, Disks)

• Choose the correct storage tiers:
  • Hot tier for frequently accessed data
  • Cool or Archive tier for long-term infrequent access data
• Clean up:
  • Unused managed disks
  • Old snapshots and backup blobs
  • Stale file shares

Azure SQL and Databases

• For SQL workloads:
  • Review performance levels (DTUs or vCores)
  • Reduce tiers where utilization is consistently low
  • Consider serverless or elastic pools for variable workloads
• For non-SQL databases:
  • Monitor provisioned throughput and scale down where possible.

Conclusion

In this part, we shifted from “seeing the bill” to actively shaping it. We walked step by step through:

• How to reduce costs on AWS using commitments, rightsizing, storage lifecycle policies, and Spot usage
• How to take advantage of Azure’s strengths, such as Reservations and Hybrid Benefit

With these optimization levers in place, the next stage is to ensure that cost efficiency is not a one-time project but a continuous process.

Drop a query if you have any questions regarding AWS or  Azure and we will get back to you quickly.

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