Optimizing EC2 workloads on Graviton processors for up to 40% cost savings

AWS Graviton processors can slash your cloud costs by up to 40% while delivering equivalent or better performance than traditional x86 instances. Yet many organizations struggle to identify which workloads benefit most from migration and how to optimize them effectively.

This comprehensive guide explores proven strategies for maximizing the performance and cost-efficiency of your EC2 workloads running on Graviton processors, including automated optimization techniques that require minimal engineering overhead.

Why Graviton instances deliver superior price-performance

AWS Graviton processors are custom-built ARM-based chips designed specifically for cloud workloads. Unlike traditional x86 instances, Graviton instances provide up to 40% better price performance while consuming 60% less energy for equivalent workloads.

The efficiency gains stem from Graviton’s architecture design:

• 1:1 vCPU mapping: Each virtual CPU maps directly to a physical core without simultaneous multithreading (SMT), providing predictable linear scalability that eliminates the resource contention common in x86 hyperthreaded environments
• Native ARM optimization: Built-in support for ARM’s NEON and SVE instruction sets accelerates compute-intensive operations like video encoding, machine learning inference, and scientific computing
• Memory efficiency: Improved memory bandwidth and latency compared to x86 alternatives, particularly beneficial for database and caching workloads

Consider a video encoding workload: Graviton4-powered C8g instances achieve 12% better performance than Graviton3 and 73% improvement over Graviton2 for the same tasks. This performance leap translates directly into reduced processing time and lower infrastructure costs.

Selecting the right Graviton instance types for your workloads

Successful Graviton optimization begins with matching instance families to your specific workload characteristics. Each Graviton instance family is purpose-built for different computational patterns.

Instance family selection guide

T4g instances work best for:

• Web applications with variable traffic patterns that benefit from burstable CPU credits
• Microservices architectures where consistent baseline performance meets most demands
• Development and testing environments requiring cost-effective compute resources
• Workloads requiring burstable CPU performance without paying for constant high-performance capacity

M6g instances excel at:

• Database workloads (MySQL, PostgreSQL, MongoDB) requiring balanced CPU and memory
• In-memory caching systems like Redis or Memcached
• Backend enterprise applications with mixed computational demands
• General-purpose workloads needing predictable performance across CPU, memory, and network

C7g instances optimize:

• High-performance computing (HPC) applications demanding maximum CPU power
• Scientific modeling and simulation requiring sustained compute performance
• CPU-intensive batch processing jobs like data analytics or financial modeling
• Machine learning inference workloads where CPU performance directly impacts latency

R6g instances handle:

• Memory-intensive databases requiring large RAM allocations
• Real-time analytics platforms processing large datasets in memory
• High-performance distributed computing frameworks like Apache Spark
• Large-scale caching layers supporting high-throughput applications

Using AWS tools for instance selection

The AWS Graviton Savings Dashboard analyzes your existing workloads and identifies candidates for migration. This tool evaluates compatibility factors including:

• Application architecture requirements and ARM64 compatibility
• Performance benchmarking results comparing current and projected performance
• Potential cost savings calculations based on current usage patterns
• Migration complexity assessments to prioritize low-risk, high-impact opportunities

AWS Compute Optimizer provides additional right-sizing recommendations by analyzing CloudWatch metrics from your current instances and suggesting optimal Graviton alternatives. This automated analysis removes guesswork from the migration planning process.

Optimizing software for ARM architecture

Migrating to Graviton requires ensuring your applications run efficiently on ARM64 architecture. This optimization phase often delivers the largest performance improvements beyond the inherent efficiency of Graviton processors.

Application compilation strategies

Rebuild with ARM-optimized toolchains: Compile applications using GCC or Clang with ARM64 optimization flags. This simple step typically improves performance by 10-15% compared to running x86 binaries through emulation. The compilation process also identifies any x86-specific dependencies that need addressing.

Leverage ARM-optimized libraries: Replace standard libraries with ARM-optimized versions that take advantage of ARM’s specialized instruction sets:

• OpenBLAS for linear algebra operations in scientific computing
• FFTW for fast Fourier transforms in signal processing
• ARM Performance Libraries for HPC workloads requiring maximum mathematical performance

Container optimization: When using containerized workloads, build ARM64-native images and deploy them through Amazon ECR. This approach eliminates emulation overhead and simplifies the migration process by maintaining your existing container orchestration workflows.

Real-world optimization example

A financial services company migrated their risk calculation engine from x86 to Graviton3 instances. The migration involved more than just changing instance types—they rebuilt their entire computational pipeline for ARM64.

By recompiling their C++ codebase with ARM optimization flags and switching to ARM Performance Libraries, they achieved:

• 25% reduction in computation time for complex risk models
• 35% lower infrastructure costs due to improved price-performance
• 40% improvement in energy efficiency, supporting their sustainability goals

The key insight: performance gains required active optimization, not just a simple instance swap.

Implementing automated optimization strategies

Manual optimization becomes impractical at scale. Automated strategies ensure consistent performance while minimizing operational overhead, making Graviton adoption sustainable across large infrastructure deployments.

Auto Scaling optimization

Configure Auto Scaling Groups to prioritize Graviton instances for cost-sensitive workloads. The improved efficiency of Graviton processors enables several key optimizations:

Threshold optimization: Increase CloudWatch alarm thresholds for Graviton instances to account for their improved efficiency. Since Graviton instances deliver more performance per dollar, you can run at higher utilization levels while maintaining the same application performance. This reduces scaling frequency and overall instance counts.

Mixed instance policies: Use Auto Scaling’s mixed instance types feature to combine Graviton and x86 instances based on availability and cost considerations. This strategy provides flexibility during high-demand periods while maximizing cost savings during normal operations.

Spot instance integration: Leverage Graviton spot instances for batch processing and fault-tolerant workloads. The improved price-performance ratio of Graviton instances often results in more stable spot pricing, as fewer competitors bid on these newer instance types.

Right-sizing automation

Implement automated right-sizing using AWS native tools to continuously optimize your infrastructure:

AWS Compute Optimizer integration: Set up automated workflows that analyze Compute Optimizer recommendations and flag over-provisioned instances for Graviton migration. This proactive approach identifies optimization opportunities before they impact your budget.

Custom CloudWatch metrics: Monitor application-specific performance indicators to identify opportunities for downsizing to smaller Graviton instance types without performance degradation. For example, track database query response times to determine if a smaller R6g instance can handle your workload.

Instance scheduling: Deploy Graviton instances during peak demand periods when their cost efficiency provides maximum benefit. This time-based optimization strategy can reduce costs during predictable traffic patterns.

Cost optimization strategies and monitoring

Effective cost optimization requires continuous monitoring and adjustment of your Graviton deployment strategy. The goal is sustained savings without performance degradation.

Performance monitoring best practices

CloudWatch metrics analysis: Track key performance indicators that reveal optimization opportunities:

• CPU utilization patterns across instance families to identify right-sizing opportunities
• Memory consumption trends that might indicate better instance family matches
• Network throughput variations that could suggest network-optimized instance types
• Application response times to ensure migrations maintain user experience

Benchmarking protocols: Establish baseline performance metrics before migration and conduct regular performance testing using tools like:

• Sysbench for database performance comparisons
• Apache Bench for web application testing under load
• Custom application-specific benchmarks that measure business-critical operations

Think of benchmarking as your migration insurance policy—it provides concrete evidence that Graviton instances meet or exceed your performance requirements.

Cost tracking and optimization

Reserved instance strategy: Purchase Reserved Instances for predictable Graviton workloads to maximize cost savings. The combination of Graviton’s inherent efficiency and Reserved Instance discounts can deliver savings exceeding 50% compared to on-demand x86 instances.

Savings tracking: Implement cost allocation tags to track Graviton-specific savings across different projects and teams. This visibility helps justify expansion of Graviton adoption and identifies the most successful migration patterns for replication across your organization.

Performance per dollar metrics: Calculate and monitor performance per dollar metrics for different workloads. This data-driven approach helps you prioritize which workloads to migrate next and demonstrates the business value of your optimization efforts.

Real-world success stories and proven results

Leading companies have achieved substantial cost reductions through strategic Graviton optimization, proving that significant savings are achievable across diverse industries and use cases.

Netflix reduced analytics infrastructure costs by 20% by migrating data processing workloads to Graviton instances, while simultaneously improving job completion times. Their approach focused on batch processing workloads where the improved price-performance directly translated to faster insights at lower cost.

Snap achieved 30% cost savings for real-time processing workloads by combining Graviton instances with optimized container deployments and automated scaling policies. Their success demonstrates that even latency-sensitive applications can benefit from Graviton optimization.

These results demonstrate that proper Graviton optimization delivers both immediate cost benefits and long-term operational improvements. The companies didn’t just swap instance types—they redesigned their infrastructure strategies around Graviton’s strengths.

Advanced automation with third-party tools

While AWS native tools provide excellent optimization capabilities, third-party solutions can enhance automation and provide additional insights that accelerate your optimization program.

Infrastructure as Code: Use Terraform or CloudFormation templates to standardize Graviton deployments across environments. This approach ensures consistent optimization settings and simplifies management at scale. Template-driven deployments also make it easier to roll out Graviton optimizations across multiple accounts or regions.

Monitoring integration: Tools like Datadog and New Relic provide detailed performance insights that complement AWS CloudWatch, helping identify optimization opportunities that might otherwise go unnoticed. These platforms often offer better visualization and alerting capabilities for complex multi-service applications.

Cost management platforms: Specialized cloud cost management solutions can automate the identification and implementation of Graviton optimization opportunities across complex multi-account environments. These tools often provide more sophisticated cost allocation and optimization recommendations than native AWS tools.

Maximizing your Graviton optimization results

Successful Graviton optimization requires a systematic approach combining proper instance selection, software optimization, and automated management strategies. Organizations that implement comprehensive optimization programs typically achieve 30-40% cost reductions while improving application performance.

The key to sustained success lies in automation and continuous monitoring. Manual optimization efforts often lose momentum over time, but automated systems ensure your infrastructure remains optimized as your workloads evolve. Start with your most predictable workloads, measure the results carefully, and expand your Graviton adoption based on proven success patterns.

Consider Graviton optimization as an investment in your infrastructure’s future. The performance and efficiency improvements compound over time, delivering increasing value as your cloud usage grows.

Ready to unlock significant savings from your AWS infrastructure? Hykell specializes in automated cloud cost optimization, helping organizations reduce AWS expenses by up to 40% through proven strategies including Graviton optimization, right-sizing, and automated resource management—all without compromising performance or requiring additional engineering effort.