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7 easiest ways to reduce AWS bills with Graviton: real case studies and proven savings

AWS bills growing faster than your revenue? You’re not alone. While most companies focus on turning off unused instances, they’re missing one of the biggest cost optimization opportunities hiding in plain sight: AWS Graviton processors.

Graviton instances deliver 40-60% better price-performance compared to traditional x86 instances, yet many AWS users haven’t made the switch. The reason? Fear of compatibility issues and uncertainty about real-world results.

This guide breaks down seven proven strategies to slash your AWS costs using Graviton instances, backed by actual case studies and measurable savings data.

What makes Graviton instances a cost optimization goldmine

AWS Graviton processors are Amazon’s custom-built ARM64 chips designed specifically for cloud workloads. Unlike traditional x86 processors that were originally designed for desktop computing, Graviton instances are engineered from the ground up for efficiency and cost-effectiveness in the cloud.

The numbers speak for themselves:

Think of it like switching from a gas-guzzling truck to a hybrid car for your daily commute—you get where you need to go more efficiently and at a fraction of the cost. But the real magic happens when you understand which workloads benefit most and how to migrate them effectively.

A vibrant, high-contrast split-screen illustration: On the left, a server rack filled with traditional x86 processors emits excessive heat and dollar bills, representing high costs and energy consumption. On the right, the same rack is upgraded to AWS Graviton ARM64 chips, depicted as sleek, green, energy-efficient processors emitting cool airflow and coins, clearly showing reduced costs and improved efficiency. Include AWS and Graviton logos clearly on the respective hardware.

Strategy 1: Target your stateless applications first

Why this works: Stateless applications are like modular building blocks—they have minimal dependencies and can switch between instance types without complex reconfiguration.

Best candidates:

  • Web servers
  • API gateways
  • Load balancers
  • Microservices

Real-world example: A fintech company migrated their REST API services from m5.large to m6g.large instances and immediately saw a 23% reduction in compute costs while maintaining identical response times. The migration took just two hours of downtime during a scheduled maintenance window.

Implementation tip: Start with your development and staging environments to validate performance before moving production workloads. This approach lets you build confidence and iron out any compatibility issues without risking customer-facing services.

Strategy 2: Modernize .NET workloads for maximum impact

Legacy .NET Framework applications can’t run on ARM processors, but modern .NET Core and .NET 5+ applications can deliver substantial savings on Graviton. This isn’t just about switching instance types—it’s about modernizing your entire application stack.

The migration path:

  1. Refactor .NET Framework applications to .NET Core or .NET 6+
  2. Test compatibility with ARM64 architecture using Amazon Linux 2 ARM64 AMIs
  3. Deploy to Graviton instances with thorough performance validation

Case study results: According to AWS prescriptive guidance, companies achieved 45% cost savings after completing this migration path. One enterprise customer reduced their monthly compute bill from $50,000 to $27,500 while improving application performance by 15%.

Pro tip: Use containerization to simplify the migration process and ensure consistent performance across environments. Docker containers abstract away many ARM64 compatibility concerns and make rollbacks easier if issues arise.

Strategy 3: Optimize containerized workloads with Graviton

Container workloads are perfect candidates for Graviton migration because containers abstract away hardware dependencies—like having a universal adapter that works with any outlet type.

Container optimization checklist:

  • Use ARM64-compatible base images (most popular images now support multi-architecture)
  • Test your container orchestration tools (Amazon EKS works seamlessly with Graviton)
  • Validate third-party dependencies for ARM compatibility before deployment

Performance boost: Benchmark tests show improved throughput and latency for containerized applications running on Graviton instances, especially for CPU-intensive tasks like image processing and data transformation.

Cost impact: Organizations typically see 25-35% cost reduction when migrating container workloads to Graviton-based instances. For a company running 200 containers on t3.medium instances, this translates to roughly $3,600 in annual savings.

Strategy 4: Leverage AWS Compute Optimizer for data-driven decisions

Manual workload analysis takes weeks and often misses optimization opportunities. AWS Compute Optimizer automates the process by analyzing your usage patterns and recommending Graviton instance types based on actual performance data.

How to use it effectively:

  1. Enable Compute Optimizer across all AWS regions
  2. Let it collect 14+ days of performance data for accurate recommendations
  3. Focus on recommendations with “High” confidence ratings first
  4. Prioritize workloads with the highest potential savings to maximize impact

Hidden benefit: Compute Optimizer often identifies over-provisioned instances, leading to additional savings beyond just switching to Graviton. One company discovered they were using m5.xlarge instances for workloads that could run efficiently on m6g.large, saving both on instance size and processor type.

Strategy 5: Target memory-intensive applications

Graviton-based memory-optimized instances (like r6g series) offer exceptional value for memory-bound applications. These instances are specifically designed for workloads that need high memory-to-CPU ratios.

Ideal workloads:

  • In-memory databases (Redis, Memcached)
  • Real-time analytics platforms
  • Large-scale data processing jobs
  • Caching layers for web applications

Performance advantage: These instances deliver better price-per-GB of memory while maintaining or improving processing performance. A Redis cluster running on r6g instances typically shows 10-15% better throughput compared to equivalent r5 instances.

Savings calculation: A typical r5.xlarge to r6g.xlarge migration saves approximately $200-300 per month per instance while providing comparable or better performance. For organizations running multiple memory-intensive workloads, these savings compound quickly.

Strategy 6: Implement CI/CD pipeline optimization

Development and testing workloads are perfect for Graviton because they don’t require the same stability guarantees as production systems—they’re your testing ground for innovation.

CI/CD optimization approach:

  • Migrate build servers to Graviton instances for immediate cost reduction
  • Use Graviton for automated testing environments where performance variability is acceptable
  • Deploy staging environments on ARM architecture to mirror production optimizations

Compound savings: Since CI/CD environments often run 24/7, even small per-instance savings multiply quickly across your development infrastructure. A typical development team running 20 build servers can save $4,000-6,000 annually by switching to Graviton.

Risk mitigation: Start with non-critical build processes to validate performance before migrating essential CI/CD components. If a test build takes 15% longer on Graviton but costs 25% less, the trade-off often makes sense for development workloads.

Strategy 7: Monitor and validate performance continuously

Migration without monitoring is like driving blindfolded. Implement comprehensive performance tracking to ensure Graviton instances deliver expected results and identify opportunities for further optimization.

Key metrics to track:

  • Response time and latency (should remain stable or improve)
  • CPU and memory utilization (may show different patterns on ARM)
  • Throughput for data-intensive applications
  • Error rates and availability (critical for production workloads)

Cost validation tools:

  • AWS Cost Explorer for detailed spend analysis
  • AWS Trusted Advisor for ongoing optimization recommendations
  • Third-party tools like nOps for detailed workload analysis and automated optimization

Success measurement: Track both performance metrics and cost savings to build confidence in your Graviton migration strategy. Create dashboards that show month-over-month cost reductions alongside performance benchmarks to demonstrate ROI to stakeholders.

Common migration pitfalls to avoid

Architecture dependencies: Some applications have hard dependencies on x86 libraries or proprietary software that doesn’t support ARM. Identify these early in your migration planning by conducting a thorough dependency audit.

Testing shortcuts: Skipping thorough testing in non-production environments leads to production issues and costly rollbacks. One company learned this lesson when they migrated a critical API without proper load testing, resulting in 30% higher response times during peak traffic.

All-or-nothing approach: Migrating everything at once increases risk exponentially. Start with low-risk workloads and build expertise gradually—think evolution, not revolution.

Ignoring networking: Some networking configurations and security groups may need adjustment when switching instance families, particularly if you have hardcoded instance-specific configurations.

Real-world savings breakdown

Based on documented case studies and AWS benchmarks, here’s what organizations typically save:

  • Small workloads (10-50 instances): $5,000-15,000 annual savings
  • Medium deployments (100-500 instances): $25,000-75,000 annual savings
  • Enterprise scale (1000+ instances): $150,000+ annual savings

These numbers assume a mixed workload migration with 60-70% of eligible workloads successfully moved to Graviton instances. Companies that take a systematic approach often exceed these benchmarks by 20-30%.

Getting started with your Graviton migration

The easiest path to Graviton savings starts with identifying your best migration candidates. Focus on stateless applications, containerized workloads, and development environments where you can test and validate performance without production risk.

Begin by running AWS Compute Optimizer on your current infrastructure to get data-driven recommendations. Start small with a pilot migration of 5-10 instances, measure the results, and build your migration playbook based on real performance data.

A detailed, step-by-step visual workflow chart: Central image of a laptop displaying AWS Console with Compute Optimizer open, surrounded by icons representing different workload types—stateless apps (web server), containers (Docker logo), memory-intensive databases (Redis symbol), and CI/CD pipelines (gear, code). Each workload icon is connected by arrows to Graviton-branded EC2 instances, with labeled sticky notes showing percentage savings (e.g., -45%, -23%). Emphasize real cost savings and clear migration pathways.

For organizations looking to accelerate their cloud cost optimization beyond just Graviton instances, Hykell provides automated optimization that can reduce overall AWS costs by up to 40%. Their approach combines Graviton migration strategies with comprehensive resource optimization, EBS tuning, and real-time monitoring to maximize your cloud cost savings without requiring ongoing engineering effort.

Start your Graviton journey today by running AWS Compute Optimizer on your current infrastructure—you might be surprised by how much you could save with a few strategic instance migrations.