Software compatibility with AWS Graviton instances: what works and what doesn't

Are you considering migrating workloads to AWS Graviton instances but unsure if your software stack will make the transition? This question keeps many cloud engineers awake at night, especially when cost savings of 20-40% are on the table.

AWS Graviton instances represent a fundamental shift from traditional x86 architecture to ARM-based processors. While this transition unlocks significant cost optimizations and up to 60% less energy consumption compared to comparable x86 instances, it requires careful evaluation of software compatibility to avoid costly migration headaches.

Understanding AWS Graviton architecture fundamentals

AWS Graviton processors are custom-built ARM-based chips designed specifically for cloud workloads. Unlike traditional x86 processors from Intel or AMD, Graviton instances run on ARM64 architecture, which means your software must be compiled for or compatible with this instruction set.

The latest generation, Graviton3, delivers up to 25% better price-performance compared to Graviton2 for compute-intensive tasks. However, the performance gains only materialize when your applications can effectively leverage the ARM64 architecture.

Think of it like switching from a gasoline car to an electric vehicle - while the destination remains the same, the underlying mechanics require different considerations and potentially different maintenance approaches. The fuel efficiency improvements are substantial, but you need charging infrastructure (ARM64-compatible software) rather than gas stations (x86 binaries).

Operating system compatibility with Graviton instances

Supported Linux distributions

Graviton instances offer robust support for major Linux distributions, all requiring ARM64 builds:

• Amazon Linux 2: Fully optimized for Graviton with native ARM64 support and pre-configured performance tuning
• Ubuntu: ARM64 builds available for recent versions (18.04 LTS and newer)
• Red Hat Enterprise Linux (RHEL): ARM64 support for RHEL 8 and newer
• SUSE Linux Enterprise: Native ARM64 builds supported with enterprise-grade optimization
• CentOS Stream: ARM64 builds available for versions 8 and 9
• Debian: ARM64 support across recent stable releases

These distributions come pre-optimized for ARM64 architecture, ensuring optimal performance on Graviton instances without requiring custom compilation.

Windows support limitations

Here’s where many organizations hit their first roadblock: Windows Server is not supported on Graviton instances. This represents a significant limitation for enterprises running Windows-based workloads.

If your infrastructure relies heavily on Windows Server applications, you’ll need to maintain separate x86 instances for these workloads while migrating compatible Linux applications to Graviton instances for cost optimization. This hybrid approach still delivers meaningful savings for the Linux portion of your infrastructure.

Programming languages and runtime compatibility

Fully compatible languages

Most modern programming languages offer excellent Graviton compatibility with minimal migration effort:

Python: Native ARM64 support with minimal performance differences. Popular packages like NumPy, Pandas, and TensorFlow have ARM64 builds available through pip, often delivering comparable or superior performance to x86 equivalents.

Java: OpenJDK and Oracle JDK both support ARM64 seamlessly. However, some legacy Java libraries compiled specifically for x86 may require updates or alternative ARM64 versions.

Node.js: Full ARM64 support with npm packages generally compatible. Performance often matches or exceeds x86 equivalents, particularly for I/O-intensive applications.

.NET Core on Linux: Microsoft provides native ARM64 support for .NET Core on Linux distributions. However, legacy .NET Framework applications remain incompatible due to Windows dependency and lack of ARM64 support.

Go: Excellent ARM64 support with native compilation capabilities. Performance typically equals or surpasses x86 implementations, making Go applications ideal candidates for Graviton migration.

Potential compatibility challenges

Some scenarios require additional consideration and planning:

• Legacy x86-only binaries: Older applications without ARM64 builds need recompilation or may require vendor updates
• Proprietary software: Vendor-specific applications may lack ARM64 versions, requiring vendor engagement or alternative solutions
• Specialized libraries: Some scientific computing or machine learning libraries may have limited ARM64 optimization, though this gap continues to narrow

Consider creating a compatibility matrix for your application portfolio, categorizing each component as “ready,” “needs testing,” or “requires alternatives.”

Database compatibility and performance

Supported database systems

Major database platforms offer robust Graviton support with impressive performance characteristics:

Amazon RDS: PostgreSQL, MySQL, and MariaDB engines fully support Graviton instances with automatic ARM64 optimization. Amazon Aurora also provides native ARM64 optimization with enhanced memory efficiency.

Self-managed databases: PostgreSQL, MySQL, MongoDB, and Redis all offer ARM64 builds with performance often matching or exceeding x86 equivalents. The improved memory bandwidth of Graviton processors particularly benefits memory-intensive database operations.

NoSQL databases: DynamoDB integration works seamlessly regardless of compute architecture, while self-managed solutions like Cassandra and Elasticsearch provide ARM64 support with growing ecosystem maturity.

Performance considerations

Database workloads on Graviton instances typically deliver excellent price-performance ratios. A financial services company migrating their PostgreSQL cluster to Graviton3 instances reported 35% cost reduction while maintaining identical query performance, demonstrating the real-world benefits of ARM64 optimization for database workloads.

Memory-intensive database operations often benefit from Graviton’s efficient memory subsystem, particularly on memory-optimized instance types like R6g and R6gd.

Container and orchestration compatibility

Docker and containerization

Docker containers present one of the smoothest migration paths to Graviton instances, though preparation is key. Most popular container images now include ARM64 builds, but verification is essential.

Multi-architecture images: Many official Docker Hub images support both x86 and ARM64, automatically selecting the appropriate version based on the host architecture. Popular images like nginx, postgres, and redis offer seamless ARM64 support.

Custom containers: Applications requiring custom Docker images need ARM64 builds. This typically involves updating your CI/CD pipeline to build for multiple architectures using Docker Buildx or similar tools.

Here’s a practical example: A media company successfully migrated their microservices architecture by updating their build pipeline to create multi-arch images, reducing their container infrastructure costs by 30% without changing a single line of application code.

Kubernetes optimization

AWS EKS (Elastic Kubernetes Service) fully supports Graviton instances. Mixed-architecture clusters allow gradual migration, running ARM64 and x86 nodes simultaneously while you validate workload compatibility.

This approach enables you to deploy ARM64-ready workloads on Graviton nodes while maintaining x86 nodes for applications still requiring compatibility validation. Node selectors and taints ensure workloads land on appropriate architecture.

For organizations seeking comprehensive Kubernetes optimization beyond architecture migration, Hykell’s automated optimization platform can identify additional cost savings opportunities across your entire EKS environment, from rightsizing to resource utilization improvements.

AWS services integration

Managed services compatibility

Graviton instances integrate seamlessly with core AWS services without requiring configuration changes:

Compute services: EC2 Auto Scaling groups, Elastic Load Balancing, and CloudWatch monitoring work identically to x86 instances. Your existing automation and monitoring continue functioning without modification.

Storage services: EBS volumes, EFS file systems, and S3 integration operate without any architectural considerations. Storage performance often improves due to Graviton’s efficient I/O handling.

Networking: VPC, security groups, and networking configurations remain unchanged. Network performance typically equals or exceeds x86 equivalents.

Lambda functions: AWS Lambda supports ARM64 execution environments (Graviton2), often delivering 19% better price-performance than x86 equivalents for supported runtimes including Python, Java, and .NET Core.

Service-specific considerations

Some AWS services require specific attention during Graviton migration planning:

• CloudFormation templates: Update instance type specifications from x86 to Graviton equivalents (e.g., m5.large to m6g.large)
• AMI selection: Ensure ARM64-compatible machine images are specified in launch configurations
• Third-party monitoring: Verify ARM64 agent availability for tools like Datadog, New Relic, or Splunk before migration

Cost optimization strategies with Graviton instances

Instance type selection

Graviton instances span multiple categories to match diverse workload requirements:

General purpose: M6g, M6gd, M7g instances for balanced compute, memory, and networking requirements

Compute optimized: C6g, C6gd, C7g instances for CPU-intensive applications like web servers and scientific computing

Memory optimized: R6g, R6gd, X2gd instances for memory-intensive workloads including in-memory databases and real-time analytics

Storage optimized: I4g instances for high sequential read/write operations such as distributed file systems and data warehousing

Performance and cost analysis

Organizations typically achieve 20-40% cost reductions when migrating compatible workloads to Graviton instances. However, these savings only materialize when applications can effectively utilize ARM64 architecture without performance degradation.

A real-world example: An e-commerce platform migrated their web tier from m5.xlarge to m6g.xlarge instances, achieving 35% cost reduction while actually improving response times by 15% due to Graviton’s efficient memory handling for their Node.js applications.

Consider conducting proof-of-concept testing with representative workloads before committing to large-scale migrations. This approach helps identify compatibility issues and quantify actual cost savings for your specific use cases.

Migration planning and best practices

Assessment methodology

Start by auditing your current application portfolio systematically:

1. Identify Windows dependencies: These workloads cannot migrate to Graviton and will remain on x86 instances
2. Catalog x86-specific software: Research ARM64 alternatives or recompilation requirements for each component
3. Test containerized applications: Verify ARM64 container image availability for all dependencies
4. Evaluate third-party tools: Ensure monitoring, security, and management tools support ARM64 architecture

Create a migration readiness matrix categorizing each application as “green” (ready), “yellow” (needs testing), or “red” (requires significant work or alternatives).

Gradual migration approach

Rather than attempting wholesale migration, consider a phased approach that minimizes risk:

Phase 1: Migrate stateless web applications and microservices with known ARM64 compatibility Phase 2: Move databases and data processing workloads after performance validation
Phase 3: Transition remaining compatible applications based on lessons learned

This strategy allows you to gain experience with Graviton instances while minimizing risk to critical systems. Each phase builds confidence and operational knowledge for subsequent migrations.

Common deployment scenarios and compatibility

Web applications and microservices

Modern web applications built with Python, Node.js, or Java typically migrate seamlessly to Graviton instances. Containerized microservices architectures particularly benefit from ARM64’s efficient resource utilization and improved price-performance ratios.

A SaaS company successfully migrated their entire microservices stack comprising 200+ containers, achieving 40% infrastructure cost reduction while improving overall system responsiveness.

Data processing and analytics

Big data frameworks like Apache Spark, Hadoop, and Kafka offer ARM64 support with growing maturity. However, some specialized analytics tools may require vendor verification for ARM64 compatibility before migration.

Memory-intensive analytics workloads often see particular benefits from Graviton’s memory architecture, with some organizations reporting 25% performance improvements alongside cost reductions.

Machine learning workloads

TensorFlow, PyTorch, and other major ML frameworks support ARM64 with continuing optimization improvements. However, GPU-accelerated training workloads may need to remain on x86 instances depending on specific acceleration requirements and CUDA compatibility.

Inference workloads often perform excellently on Graviton instances, particularly for CPU-based model serving where the improved price-performance directly translates to lower serving costs.

Monitoring and optimization considerations

Performance monitoring

Graviton instances integrate with standard AWS monitoring tools, but consider ARM64-specific metrics when evaluating performance. Memory bandwidth utilization and instruction efficiency may differ from x86 baselines, requiring adjusted performance expectations.

CloudWatch metrics remain identical, but establish new performance baselines during migration to accurately assess ARM64 performance characteristics for your specific workloads.

Cost tracking

Track cost savings carefully during migration phases. While Graviton instances offer lower hourly rates, ensure your specific workloads achieve expected performance levels to validate total cost of ownership improvements.

Many organizations implement cost monitoring dashboards comparing pre-migration x86 costs with post-migration Graviton expenses, tracking both infrastructure savings and any performance impacts.

Making the Graviton migration decision

Graviton instances represent a compelling cost optimization opportunity for organizations running compatible workloads on AWS. The potential for 20-40% cost reductions makes migration attractive, but success depends on thorough compatibility assessment and careful migration planning.

Focus your initial efforts on applications with clear ARM64 support, containerized architectures, and workloads that can benefit from Graviton’s memory and compute efficiency. Avoid rushing Windows-dependent applications or legacy systems with unclear ARM64 compatibility.

The migration to Graviton instances often serves as a catalyst for broader infrastructure modernization, encouraging containerization, application updates, and architecture improvements that deliver benefits beyond just cost savings.

Ready to explore how much your organization could save by optimizing your AWS infrastructure, including potential Graviton migrations? Hykell’s automated cost optimization platform analyzes your entire AWS environment to identify savings opportunities across instance types, storage configurations, and service utilization - helping you achieve up to 40% cost reductions without compromising performance.