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Performance and cost comparison between AWS Graviton and Intel instances

Your AWS bill could drop by 50% tomorrow. The choice between Graviton, Intel, and AMD processors isn’t just about performance—it’s about maximizing your cloud budget while maintaining the speed your applications demand.

AWS Graviton processors have fundamentally changed the cloud economics game. Built on ARM architecture, these custom silicon chips offer compelling cost advantages over traditional Intel and AMD x86 processors. But the real question isn’t whether Graviton is cheaper—it’s whether the performance trade-offs align with your specific workloads and business objectives.

Understanding the architectural differences

The fundamental distinction between these processor types lies in their design philosophy. Graviton processors use ARM-based architecture, custom-built by AWS for cloud-native workloads. This gives AWS complete control over optimization for their infrastructure, resulting in significant cost efficiencies that traditional chipmakers simply can’t match.

Side-by-side infographic showing three physical processor chips labeled 'AWS Graviton (ARM)', 'Intel Xeon (x86)', and 'AMD EPYC (x86)', each with brief callouts about their core architecture, mapping style (1 vCPU = 1 core vs. hyperthreading), and typical cloud workloads. Visually distinguish the chips and include concise icons or text highlighting cost and performance differences.

Think of it like the difference between buying a custom-tailored suit versus one off the rack. Intel and AMD processors rely on x86 architectures that have been optimized over decades for single-thread performance and backward compatibility. While this makes them excellent for legacy applications and compute-intensive single-threaded tasks, it also means you’re paying for capabilities you might not need in cloud environments.

Graviton instances map one vCPU to one physical core, unlike Intel and AMD instances that use hyperthreading to present multiple virtual cores per physical core. This architectural difference becomes crucial when you’re running multi-threaded workloads that can fully utilize available cores—imagine having dedicated lanes on a highway versus sharing lanes through a complex interchange system.

The EC2 processor differences extend beyond just core mapping. Graviton’s custom silicon approach allows AWS to optimize power consumption, memory bandwidth, and interconnect performance specifically for cloud workloads, rather than trying to serve every possible computing scenario.

Graviton vs Intel: the performance reality

According to benchmarks from Tom’s Hardware, Graviton4 consistently outperforms both AMD EPYC Genoa and Intel Xeon Sapphire Rapids in HPC workloads. The latest Graviton4 processors leverage 96 Arm Neoverse V2 cores with DDR5-5600 memory, delivering substantial performance gains over their predecessors.

For real-world applications, the performance story varies dramatically by workload type:

Multi-threaded workloads: Graviton instances excel here, with Cribl’s comprehensive analysis showing Graviton3 instances delivering approximately 40% better price-performance than Intel c5 instances when fully utilized. This advantage stems from Graviton’s one-to-one core mapping, which eliminates the overhead and resource contention that can occur with hyperthreading.

Single-threaded applications: Intel and AMD maintain advantages in scenarios requiring high single-core performance. AMD instances particularly shine here, offering 6% better price-performance than Intel for single-threaded workloads. If your application can’t leverage multiple cores effectively—think legacy database engines or certain analytics tools—this performance difference can be significant.

Memory-intensive tasks: Graviton4’s enhanced memory bandwidth and larger cache sizes provide significant advantages for applications that process large datasets or require frequent memory access. The DDR5-5600 memory support delivers triple the performance scalability compared to previous generations, making it ideal for high-performance computing scenarios.

Consider a data processing pipeline that transforms millions of records daily. On Graviton instances, this workload can leverage all available cores simultaneously, while on hyperthreaded Intel instances, you might see performance degradation as virtual cores compete for shared physical resources.

The cost advantage breakdown

The financial impact of processor choice extends far beyond the hourly instance rates. A comprehensive total cost of ownership analysis reveals substantial differences that can reshape your entire cloud budget:

Graviton vs Intel: You can expect approximately 50% cost reduction when migrating comparable workloads from Intel to Graviton instances. This isn’t just marketing hyperbole—it’s the result of AWS’s vertical integration and custom silicon optimization.

Graviton vs AMD: The savings are less dramatic but still significant, with roughly 20% cost reduction over AMD instances for similar performance levels. AMD’s competitive pricing makes this gap smaller, but Graviton’s efficiency advantages still translate to meaningful savings.

Real-world example: A compute workload costing $91,000 annually on Graviton would cost approximately $108,000 on AMD instances and around $182,000 on Intel instances—representing potential savings of $17,000 to $91,000 per year. For a growing startup, these savings could fund additional development resources or extend runway significantly.

A bar chart illustration showing three colored bars representing annual AWS compute costs: $182,000 for Intel, $108,000 for AMD, and $91,000 for Graviton, with a clear label for each bar. Overlay icons representing specific workloads (e.g., database symbol for Intel, analytics graph for AMD, multi-core processor for Graviton) and use upward or downward arrows to indicate relative price and performance advantages.

These savings compound when you consider Spot instance pricing dynamics. Interestingly, Intel instances can offer superior value in Spot markets, with some configurations providing 65% savings compared to Graviton2 and 27% savings over AMD in Spot pricing scenarios. This occurs because Spot pricing reflects real-time demand, and Graviton’s popularity can sometimes drive up Spot prices relative to less-demanded Intel instances.

AMD’s competitive positioning

AMD instances occupy an interesting middle ground between Graviton’s cost efficiency and Intel’s single-threaded performance. The c5a instance family demonstrates AMD’s strategic strengths:

Single-thread superiority: AMD instances outperform Graviton2 by approximately 14% in single-threaded scenarios, making them ideal for applications that can’t fully utilize multiple cores. This performance advantage becomes critical for database workloads with complex query engines that rely heavily on single-core performance.

Price-performance balance: While more expensive than Graviton, AMD instances often provide better value than Intel for workloads that benefit from their architecture, particularly in database and analytics applications. AMD’s Zen architecture excels at floating-point operations and memory-intensive calculations.

Memory performance: AMD’s memory subsystem excels in high-throughput scenarios, making these instances particularly suitable for data-intensive applications. The architecture’s superior memory bandwidth can significantly impact performance for applications that frequently access large datasets stored in memory.

Consider a real-time analytics platform processing streaming data. While Graviton might handle the multi-threaded data ingestion more cost-effectively, AMD instances could provide better performance for the complex analytical queries that require high single-core performance and memory throughput.

Compatibility considerations

The biggest challenge with Graviton adoption isn’t performance or cost—it’s compatibility. Graviton processors cannot natively run x86 applications. This means your applications must either be compiled for ARM architecture or run through emulation, which significantly impacts performance and can introduce unexpected behavior.

Modern containerized applications and cloud-native frameworks generally support ARM architecture, making migration straightforward. Popular languages like Python, Java, Node.js, and Go compile seamlessly to ARM. However, legacy applications, proprietary software, or workloads with x86-specific dependencies may require significant engineering effort to migrate.

This compatibility constraint makes Intel and AMD instances essential for organizations with substantial x86-dependent workloads, regardless of the potential cost savings from Graviton. Before migrating, you’ll need to audit your software stack for:

  • Proprietary libraries compiled only for x86
  • Legacy applications without ARM support
  • Third-party software dependencies
  • Hardware-specific optimizations that rely on x86 instruction sets

The migration effort varies dramatically. A modern microservices architecture built with Docker containers might migrate in days, while a monolithic application with proprietary database drivers could require months of engineering work.

Workload-specific recommendations

Choose Graviton for:

  • Microservices and containerized applications that can leverage horizontal scaling
  • Web servers and API endpoints serving high-volume traffic
  • Batch processing and HPC workloads requiring maximum multi-core utilization
  • CI/CD pipelines and development environments where cost efficiency matters most
  • Applications that can leverage multi-core parallelism, such as image processing or data transformation pipelines

Choose AMD for:

  • Database workloads requiring high single-thread performance, particularly OLTP systems
  • Analytics applications with high memory throughput requirements
  • Applications needing x86 compatibility with better price-performance than Intel
  • Financial modeling or scientific computing that benefits from AMD’s floating-point performance
  • Workloads that process large datasets but rely on single-threaded algorithms

Choose Intel for:

  • Legacy x86 applications that cannot be migrated due to technical or business constraints
  • Single-threaded applications requiring maximum per-core performance
  • Workloads with specific Intel instruction set dependencies (AVX-512, etc.)
  • Latency-sensitive applications where every millisecond matters, such as high-frequency trading
  • Enterprise software with Intel-optimized libraries or vendor support requirements

Migration strategy and optimization

Successfully transitioning between processor types requires careful planning and testing. Start with non-critical workloads to validate performance and compatibility, then gradually migrate production systems based on your results.

Think of migration like changing the engine in a race car while it’s still running—you need to understand every component’s dependencies and performance characteristics before making the switch. Begin with a comprehensive workload analysis to identify applications that would benefit most from processor migration.

Consider using AWS’s compute optimization features in conjunction with processor selection. Auto Scaling Groups can automatically adjust instance types based on demand, while Spot instances can provide additional cost savings regardless of processor choice. This hybrid approach allows you to optimize for both performance and cost across different workload patterns.

Testing should include not just performance benchmarks but also compatibility validation, dependency checking, and load testing under realistic conditions. A web application might perform well in development on Graviton but encounter issues with specific libraries under production load.

The key is matching your workload characteristics to the processor architecture that offers the best price-performance ratio for your specific use case. This isn’t a one-size-fits-all decision—different workloads within the same organization may benefit from different processor types, creating a heterogeneous infrastructure optimized for each application’s unique requirements.

Maximizing your cloud optimization strategy

Processor selection represents just one component of comprehensive cloud cost optimization. While choosing between Graviton, Intel, and AMD can deliver substantial savings, the biggest opportunities often lie in identifying underutilized resources, optimizing storage configurations, and implementing automated scaling policies.

The most successful cost optimization strategies combine intelligent processor selection with broader infrastructure optimization. This includes rightsizing instances, implementing automated scaling, optimizing storage tiers, and continuously monitoring resource utilization patterns.

Hykell specializes in automated AWS cost optimization that goes beyond processor selection to analyze your entire cloud infrastructure. Their automated approach identifies optimization opportunities across EC2, EBS, and Kubernetes deployments while ensuring your applications maintain peak performance. With their performance-first optimization strategy, you can achieve up to 40% cost reduction without compromising the speed and reliability your business depends on.

Ready to optimize your AWS infrastructure beyond processor selection? Calculate your potential savings and discover how automated cloud optimization can transform your AWS costs while maintaining the performance your applications demand.