AWS Graviton: Cut Server Costs by 40% with ARM-Based Instances

What Is AWS Graviton and Why Should You Care?

AWS Graviton is Amazon’s custom-built ARM-based processor family designed specifically for cloud workloads. Launched in 2018 with the first generation and now in its fourth iteration (Graviton4, released in 2024), these processors represent a fundamental shift in how we think about cloud computing economics.

The core promise is straightforward: up to 40% better price-performance compared to equivalent x86-based instances. That’s not a marginal improvement—it’s a transformational cost reduction that can save organizations tens of thousands of dollars annually on their AWS bills.

But how does this work? And more importantly, is the migration worth the effort?

Let’s break it all down.

Understanding the Graviton Processor Family

Graviton Evolution: From Gen1 to Gen4

AWS has iterated rapidly on its ARM processor designs:

• Graviton1 (2018): First generation, based on ARM Cortex-A72 cores. Available in A1 instances. Modest performance, limited adoption.
• Graviton2 (2020): Major leap forward. 64 custom Neoverse N1 cores, 7x better performance than Gen1. Available in M6g, C6g, R6g, T4g families.
• Graviton3 (2022): 25% better compute performance than Graviton2, 2x floating-point performance, 3x better ML performance. Available in M7g, C7g, R7g families.
• Graviton4 (2024): 30% better performance than Graviton3, up to 96 vCPUs, 50% more cores. Available in M8g, C8g, R8g families.

Each generation has brought substantial improvements while maintaining backward compatibility with the ARM64 instruction set.

Why ARM Architecture Delivers Better Efficiency

The cost advantage isn’t magic—it’s architecture. ARM processors use a RISC (Reduced Instruction Set Computing) design that offers:

• Lower power consumption per core: ARM cores do less per cycle but consume far less energy
• Higher core density: More cores fit in the same thermal envelope
• Simpler pipeline design: Fewer transistors wasted on legacy compatibility
• Custom silicon advantages: AWS designs specifically for cloud workloads, eliminating unnecessary features

The result is that AWS can offer these instances at a lower price point while still delivering competitive—or superior—raw performance.

Real-World Cost Savings: The Numbers

Let’s look at concrete pricing comparisons. The following table compares popular instance types between x86 (Intel/AMD) and Graviton equivalents in the US East (N. Virginia) region:

Instance Type	Architecture	vCPUs	RAM (GiB)	On-Demand Price/hr	Monthly Cost (730hr)
m6i.xlarge	Intel x86	4	16	$0.192	$140.16
m6g.xlarge	Graviton2	4	16	$0.154	$112.42
m7i.xlarge	Intel x86	4	16	$0.2016	$147.17
m7g.xlarge	Graviton3	4	16	$0.1632	$119.14
c6i.2xlarge	Intel x86	8	16	$0.340	$248.20
c6g.2xlarge	Graviton2	8	16	$0.272	$198.56
r6i.xlarge	Intel x86	4	32	$0.252	$183.96
r6g.xlarge	Graviton2	4	32	$0.2016	$147.17

The raw price difference alone is typically 20% lower for Graviton instances. But the real savings compound when you factor in the performance advantage—many workloads run faster on Graviton, meaning you can often downsize your instance type.

A Practical Example: Web Application Fleet

Consider a mid-sized e-commerce platform running:

• 10x m6i.xlarge instances for application servers
• 3x r6i.xlarge instances for caching layers
• 2x c6i.2xlarge for background processing

Monthly x86 cost:

• Application: 10 × $140.16 = $1,401.60
• Caching: 3 × $183.96 = $551.88
• Processing: 2 × $248.20 = $496.40
• Total: $2,449.88/month

Monthly Graviton cost (equivalent g-family instances):

• Application: 10 × $112.42 = $1,124.20
• Caching: 3 × $147.17 = $441.51
• Processing: 2 × $198.56 = $397.12
• Total: $1,962.83/month

Annual savings: $5,844.60 — and that’s just on compute, before any performance-driven downsizing.

At Lueur Externe, our AWS Solutions Architect-certified team regularly helps clients achieve these savings during infrastructure audits. For larger deployments with hundreds of instances, we’ve seen annual savings exceed $100,000.

Where Graviton Shines: Best Use Cases

Graviton instances aren’t just for EC2. The ARM advantage extends across the AWS ecosystem:

Web Servers and Application Tiers

Nginx, Apache, Node.js, Python (Django/Flask), Java (Spring Boot), Go, and PHP applications all run natively on ARM64. Most modern web frameworks have had full ARM support for years.

Containerized Workloads (ECS/EKS)

If you’re running containers, migration is often trivial. Multi-architecture Docker images let you build once and deploy on both x86 and ARM:

# Build multi-arch image with Docker Buildx
docker buildx create --name multiarch --use

docker buildx build \
  --platform linux/amd64,linux/arm64 \
  -t myapp:latest \
  --push .

# EKS Node Group with Graviton instances
apiVersion: eksctl.io/v1alpha5
kind: ClusterConfig

metadata:
  name: production-cluster
  region: eu-west-1

managedNodeGroups:
  - name: graviton-workers
    instanceType: m7g.xlarge
    desiredCapacity: 5
    minSize: 2
    maxSize: 10
    labels:
      arch: arm64
    taints:
      - key: arch
        value: arm64
        effect: NoSchedule

Managed Databases (RDS)

Amazon RDS supports Graviton instances for MySQL, PostgreSQL, MariaDB, and more. Switching is as simple as modifying the instance class:

aws rds modify-db-instance \
  --db-instance-identifier my-production-db \
  --db-instance-class db.r6g.xlarge \
  --apply-immediately

AWS reports that Graviton-based RDS instances deliver up to 35% better performance for database workloads compared to their x86 equivalents.

ElastiCache and In-Memory Workloads

Redis and Memcached on Graviton instances offer better throughput per dollar—critical for session stores and caching layers in high-traffic applications.

CI/CD Pipelines

GitHub Actions, GitLab CI, and AWS CodeBuild all support ARM runners. Build times for interpreted languages remain similar, while costs drop significantly.

When to Be Cautious: Potential Compatibility Issues

Not every workload is a perfect fit for Graviton. Here are scenarios requiring careful evaluation:

• Legacy binaries compiled for x86: Any pre-compiled software without ARM64 support won’t run
• Windows workloads: Graviton instances only support Linux-based operating systems
• Specific hardware dependencies: Workloads relying on Intel-specific instruction sets (AVX-512, etc.)
• Third-party software without ARM builds: Always verify vendor support
• GPU-accelerated workloads: Graviton doesn’t replace GPU instances for ML training

Checking Application Compatibility

AWS provides the Porting Advisor for Graviton tool to assess your codebase:

# Install the porting advisor
pip install porting-advisor-for-graviton

# Scan your project
porting-advisor-for-graviton ~/my-project/

This tool identifies potential issues like:

• Architecture-specific inline assembly
• x86-specific compiler flags
• Dependencies without ARM64 support
• Platform-specific library calls

Step-by-Step Migration Strategy

Based on our experience at Lueur Externe migrating dozens of client infrastructures to Graviton, here’s the approach we recommend:

Phase 1: Audit and Assessment (1-2 weeks)

1. Inventory all running instances and their workloads
2. Identify quick wins: stateless web servers, containers, managed services
3. Flag potential blockers: legacy binaries, Windows dependencies
4. Estimate savings using the AWS Pricing Calculator with Graviton equivalents

Phase 2: Non-Production Testing (2-4 weeks)

1. Spin up Graviton instances in staging environments
2. Run your test suites on ARM64 to catch compatibility issues
3. Benchmark performance against current x86 instances
4. Verify all dependencies build and run correctly

Phase 3: Gradual Production Rollout (2-8 weeks)

1. Start with stateless services behind load balancers
2. Use mixed instance groups in Auto Scaling to run both architectures simultaneously
3. Monitor metrics closely: CPU utilization, latency, error rates
4. Gradually shift traffic to Graviton instances

# Example: Mixed architecture Auto Scaling Group
aws autoscaling create-auto-scaling-group \
  --auto-scaling-group-name prod-web-mixed \
  --mixed-instances-policy '{
    "LaunchTemplate": {
      "LaunchTemplateSpecification": {
        "LaunchTemplateId": "lt-0abc123",
        "Version": "$Latest"
      },
      "Overrides": [
        {"InstanceType": "m6g.xlarge"},
        {"InstanceType": "m6i.xlarge"}
      ]
    },
    "InstancesDistribution": {
      "OnDemandPercentageAboveBaseCapacity": 30,
      "SpotAllocationStrategy": "capacity-optimized"
    }
  }' \
  --min-size 2 --max-size 10 --desired-capacity 4

Phase 4: Optimization and Full Migration (Ongoing)

1. Right-size instances based on observed Graviton performance
2. Update IaC templates (Terraform, CloudFormation) to default to Graviton
3. Document learnings for future deployments
4. Combine with Savings Plans for maximum discount stacking

Stacking Savings: Graviton + Savings Plans + Spot

The 20% Graviton discount doesn’t exist in isolation. Smart cost optimization stacks multiple strategies:

Strategy	Typical Discount	Combined with Graviton
Graviton (vs x86 On-Demand)	~20%	Baseline
Compute Savings Plan (1yr)	~30%	~44% total
Compute Savings Plan (3yr)	~50%	~60% total
Spot Instances	~60-90%	~70-92% total

For a workload that costs $10,000/month on x86 On-Demand instances, switching to Graviton with a 3-year Compute Savings Plan could bring that down to approximately $4,000/month — a $72,000 annual saving.

Performance Benchmarks: Graviton3 vs Intel vs AMD

Real-world benchmarks consistently show Graviton holding its own or outperforming competitors:

• Nginx throughput: Graviton3 (c7g) delivers 15-20% more requests/second than c6i at the same instance size
• Java (SPECjbb): Graviton3 shows 10-30% improvement depending on JVM tuning
• Python web apps: Near-identical latency with 20% cost reduction
• PostgreSQL (pgbench): 25-35% better transactions/second per dollar on r7g vs r6i
• Video encoding (FFmpeg): Graviton3 matches or exceeds comparable x86 instances for H.264/H.265

These numbers matter because they confirm that switching to Graviton isn’t just about cheaper instances doing the same work more slowly—you’re genuinely getting more performance per dollar.

Terraform Example: Deploying a Graviton-Based Stack

For infrastructure-as-code practitioners, here’s a practical Terraform snippet deploying a Graviton-based web tier:

resource "aws_launch_template" "web_graviton" {
  name_prefix   = "web-graviton-"
  image_id      = data.aws_ami.al2023_arm64.id
  instance_type = "m7g.large"

  metadata_options {
    http_tokens = "required"
  }

  tag_specifications {
    resource_type = "instance"
    tags = {
      Name         = "web-graviton"
      Architecture = "arm64"
      ManagedBy    = "terraform"
    }
  }
}

data "aws_ami" "al2023_arm64" {
  most_recent = true
  owners      = ["amazon"]

  filter {
    name   = "name"
    values = ["al2023-ami-*-arm64"]
  }

  filter {
    name   = "architecture"
    values = ["arm64"]
  }
}

resource "aws_autoscaling_group" "web" {
  desired_capacity = 3
  max_size         = 10
  min_size         = 2

  launch_template {
    id      = aws_launch_template.web_graviton.id
    version = "$Latest"
  }

  target_group_arns = [aws_lb_target_group.web.arn]
}

Common Questions and Concerns

”Will my Docker containers work on Graviton?”

If your containers are built from source (not using pre-compiled x86 binaries), they almost certainly will. Most official Docker Hub images now provide multi-architecture manifests. Simply rebuild your images for linux/arm64 or use multi-arch builds.

”What about my monitoring and observability stack?”

All major observability tools support ARM64: Datadog, New Relic, Prometheus, Grafana agents, CloudWatch agent, and the AWS OTEL collector all have ARM64 builds.

”Is the migration risky for production?”

With a proper blue/green or canary deployment strategy, risk is minimal. The key is running both architectures simultaneously during transition and comparing metrics before fully committing.

The Sustainability Angle

Beyond cost savings, Graviton instances consume up to 60% less energy for the same performance compared to comparable x86 instances. For organizations with sustainability goals or carbon reporting requirements, this is a significant advantage. AWS includes Graviton usage in its Customer Carbon Footprint Tool.

Conclusion: The Right Time to Switch Is Now

AWS Graviton has matured from an experimental curiosity to a production-proven platform trusted by companies like Netflix, Twitter (X), Snap, and thousands of startups. With four generations of processors, broad service support (EC2, RDS, EKS, ElastiCache, Lambda), and a rich ecosystem of compatible software, the barriers to adoption have never been lower.

The math is simple:

• 20% lower instance pricing out of the box
• Better performance per core for most workloads
• Combined savings of 40-60% when stacked with Savings Plans
• Lower carbon footprint for sustainability reporting

The only cost is the engineering effort to test and migrate—an investment that typically pays for itself within 2-3 months.

At Lueur Externe, our AWS Solutions Architect-certified team has been helping businesses across France and Europe optimize their cloud infrastructure since 2003. Whether you’re running a PrestaShop e-commerce platform, a WordPress multisite, or a custom application stack, we can audit your current AWS setup, identify Graviton migration opportunities, and execute the transition with zero downtime.

Ready to cut your AWS bill by 40%? Contact Lueur Externe’s cloud infrastructure team for a free initial assessment of your Graviton migration potential.