AWS Graviton processors deliver strong performance-per-dollar value for general-purpose, compute-intensive, and memory-heavy workloads. This guide explores the evolution from Graviton2 to Graviton4, breaks down instance types, and explains when AWS Graviton is a smart choice. It also covers pricing, purchasing models, and migration considerations. Platforms like Sedai help teams unlock further value by automating performance optimization based on real workload data, without adding engineering overhead.

Cloud workloads are under constant pressure to do more with less. More traffic, tighter budgets, and faster response times are the new baseline. But many teams are still running on overprovisioned or outdated x86 infrastructure that adds cost without adding value.

In this guide, we’ll explore what AWS Graviton is, why it was built, and when it makes practical sense to adopt it. You’ll also see how Sedai helps teams make informed, automated decisions about using Graviton based on actual performance and cost data.

Why AWS Built Graviton Instead of Waiting on x86

AWS Graviton was created because traditional x86 chips weren’t keeping up with cloud-native demands. Scaling often meant overpaying for unused compute just to hit performance targets. The architecture wasn’t built with cloud efficiency in mind.

By building its own ARM-based processors, AWS gained tighter control over performance and cost. Graviton lets teams right-size workloads without relying on brute-force provisioning. It’s a shift from legacy compute to something purpose-built for the cloud.

Next, let’s explore exactly what AWS Graviton is and how it drives those performance gains.

Why Teams Are Actually Choosing AWS Graviton

AWS Graviton isn’t winning because it’s trendy. It’s winning because it works, especially for teams trying to optimize spend without trading off performance.

Here’s what teams are getting out of the switch:

• Better price-performance: Graviton often outperforms x86 at a lower cost, especially for containerized and multithreaded workloads
• Lower energy usage: Optimized for efficiency, which means reduced cloud bills and lower environmental impact.
• Broad compatibility: Most Linux-based and open-source apps run on Graviton with little to no refactoring.
• Easy adoption: Tools like Graviton Fast Start and ecosystem support make it easy to test and migrate.
• Cloud-native alignment: Purpose-built for workloads like microservices, CI/CD, web apps, and batch jobs.

The result? You get compute that’s faster, cheaper, and built for how modern engineering teams actually deploy software.

AWS Graviton Generations and Instance Types: What’s Available Now

After introducing its own custom silicon, AWS has steadily evolved the AWS Graviton processor family through three major production-ready generations: Graviton2, Graviton3, and Graviton4. Each leap brings improvements in performance, efficiency, and architecture support.

• Graviton2 (2020): Based on 64-bit Neoverse N1 cores. Offered up to 40% better price-performance compared to x86.
• Graviton3 (2022): Up to 25% faster than Graviton2. Added double the floating point throughput, triple the ML performance, and advanced crypto acceleration.
• Graviton4 (2023, public preview in 2024): Built on the ARMv9 architecture, featuring 96 cores, DDR5 memory, and PCIe Gen 5. Designed for memory-heavy and next-gen compute workloads.

These AWS Graviton-based processors back a range of EC2 instance families, each tailored to different workload profiles:

All AWS Graviton instances are built on the Nitro system, with support for features like EBS optimization, enhanced networking, and Elastic Fabric Adapter (EFA) in select types. These are not entry-level chips, they’re engineered to run production-grade systems at scale.

Knowing which generation and instance type to start with is key to unlocking AWS Graviton’s full cost-performance advantage, especially if you're tuning for compute, memory, or I/O-specific gains.

Migrating to AWS Graviton: What to Watch Out For

Migrating to AWS Graviton isn’t a drop-in replacement for every workload, but for most Linux-based applications, it’s surprisingly smooth. The key friction points typically surface when your stack includes architecture-specific binaries or unmanaged dependencies.

Here’s what usually works without issue:

• Interpreted languages like Python, Node.js, Ruby, and Java (as long as your packages don’t include x86-native extensions)
• Containers, especially if you're using multi-arch builds (docker buildx) or ARM64 images from public registries
• Compiled languages like Go and Rust, which have excellent ARM support and minimal extra configuration needed

What needs more attention:

• x86-native binaries that haven’t been rebuilt for ARM64
• C/C++ dependencies in Python packages (e.g., numpy, scipy)—these may require re-compilation or compatible wheels
• Older CI/CD pipelines that assume x86 runners or build images
  

A few practical tips:

• Use docker buildx to build multi-arch images
  
• Validate builds with qemu or Graviton-based EC2 dev environments
  
• Look out for unmaintained libraries or packages that don’t publish ARM builds

How to Know If AWS Graviton Is Worth the Switch

Not every workload is a slam dunk for AWS Graviton. But if you’re running cloud-native apps with some flexibility in your stack, you’re likely leaving performance (and money) on the table by not considering it.

It’s usually a smart switch if:

• You're running Linux-based workloads on EC2, ECS, or EKS
• Your services are built with ARM-friendly languages like Go, Rust, Java, or Python (with minimal C bindings)
• You already use or can adopt Docker multi-arch builds and modern CI/CD practices
• You care about long-term price-performance optimization and are open to tuning
• You're running stateless APIs, event-driven services, or big data processing at scale

You might hold off if:

• Your application relies on x86-only binaries, closed-source components, or legacy vendor tools
• You’re locked into Windows workloads or non-ARM-supported distros
• Your team doesn’t have the time to retest builds or adjust infra automation
• You're running something fragile and critical with zero tolerance for change or testing

Graviton gives you room to optimize, but it's not about flipping a switch blindly. If your environment is flexible and your workloads are compute-bound, it's a clear win. If you're locked into rigid tooling or OS limitations, it's probably not worth the effort yet.

Practical Use Cases for AWS Graviton

Once you've cleared compatibility and control hurdles, the next question is what exactly should you run on AWS Graviton? The short answer: anything compute-heavy, scalable, and flexible.

Here’s where teams are seeing real performance and cost benefits:

Containerized Microservices

Whether you’re running on ECS, EKS, or Kubernetes-on-EC2, containerized workloads are quick wins for Graviton. ARM64 support is baked into Docker, and with multi-arch builds, most services don’t require major rewrites.

Best for:

• APIs
• Event-driven services
• Backend microservices (Go, Rust, Java, Python)

High-Throughput Data Processing

Big data workloads, especially those using Spark, Flink, Kafka, or ClickHouse, see significant gains from Graviton’s enhanced memory bandwidth and better performance per watt.

Best for:

• Stream processing
• ETL jobs
• Real-time analytics
• Log ingestion pipelines

CI/CD and Build Pipelines

Graviton instances make solid runners for fast, cost-efficient builds, especially for projects already targeting ARM (mobile, edge, or containerized deployments). Some teams run ARM-native test jobs in parallel with x86 to compare runtime behavior.

Best for:

• Self-hosted GitHub Actions runners
• ARM-native mobile or edge builds
• Parallelized test pipelines

Web and App Servers

Traditional web applications like Nginx, Node.js, Spring Boot, or Django transition well to Graviton, especially if you’re already containerized or running on AL2/Ubuntu.

Best for:

• Stateless web servers
• Application backends
• API gateways

ARM-Native Projects

If you’re building for edge devices, mobile hardware, or IoT gateways, Graviton helps maintain consistent performance characteristics between dev, test, and production environments.

Best for:

• Embedded systems backends
• Mobile app backends
• Edge-focused services

How AWS Graviton Pricing Actually Works

Graviton instances are known for being cost-effective, but savings only materialize when pricing choices align with workload demands and usage patterns.

Here’s what engineers should keep in mind:

1. Instance Pricing Depends on Workload

Each Graviton instance type is optimized for different workload profiles:

• M6g: Balanced performance for general-purpose workloads like app servers or small databases
• C6g: Suited for compute-heavy workloads such as batch processing or ad tech
• R6g: Ideal for memory-intensive tasks like caching and in-memory databases

Pricing scales with instance size (for example, c6g.medium to c6g.16xlarge) and also varies by region. A configuration that is affordable in North Virginia might cost significantly more in Singapore.

2. Billed Per Second of Usage

You are billed per second with a 60-second minimum. This model is efficient for workloads that are bursty, short-lived, or event-driven, such as CI pipelines, auto-scaling APIs, or development environments.

3. Choosing the Right Purchase Model

There are three common ways to pay:

• On-Demand: Offers flexibility with no long-term commitment. Best suited for testing, staging, or unpredictable traffic.
• Savings Plans and Reserved Instances: Provide cost savings of up to 72% in exchange for committing to one- or three-year terms. Ideal for steady, predictable workloads.
• Spot Instances: Leverage excess AWS capacity at a significant discount, but with the risk of unexpected termination. Recommended for fault-tolerant or stateless workloads like CI/CD or data processing jobs.

Many teams mix these models to optimize for both flexibility and cost control across dev, staging, and production.

4. Operating System Licensing Can Skew Costs

Graviton delivers the best value when paired with Linux or open-source operating systems. Running Windows workloads introduces additional licensing fees, which can erode the cost advantage. If you're planning a large migration, it's important to align OS choices with cost targets early on.

Also read: Top 10 AWS Cost Optimization Tools 

How Teams Use Sedai to Optimize AWS Graviton

Many teams have made the switch to AWS Graviton for better performance and cost efficiency, but managing those gains over time is where things get challenging. Instance choices, workload patterns, and scaling demands can shift quickly, and without the right visibility, teams risk underutilizing the very advantages they moved for.

That’s why more companies are turning to platforms like Sedai. These tools help automate workload tuning, identify cost-performance gaps, and continuously adapt Graviton usage based on real-time behavior. It’s not about replacing engineers, it’s about giving them the insight and automation needed to make smarter, faster decisions at scale.

Also read: Cloud Optimization: The Ultimate Guide for Engineers 

Conclusion

Graviton has come a long way from being a niche alternative to x86. With stronger performance across generations, tailored instance types, and lower costs, it’s now a serious choice for modern cloud workloads.

But migrating is only part of the equation. To truly get value from AWS Graviton, teams need to continually tune for performance and efficiency, especially as environments grow more complex. Platforms like Sedai help automate that effort, so engineers can focus on building rather than chasing down performance issues.

Curious how Sedai could fit into your AWS Graviton setup? Take a closer look at how it works.

FAQs

1. What types of workloads benefit most from AWS Graviton?

Graviton is ideal for compute-intensive, memory-optimized, and burstable workloads, like microservices, databases, and machine learning inference.

2. Can I run existing x86 applications on Graviton processors?

Not directly. You’ll need to recompile or re-architect for Arm architecture. The cost savings can justify the effort.

3. Which AWS services support Graviton?

Graviton is supported across EC2, ECS, EKS, RDS, Aurora, Lambda, ElastiCache, EMR, and more.

4. How does Sedai help optimize AWS Graviton usage?

Sedai analyzes your workload’s real-time behavior and automatically shifts to optimal instance types, Graviton included, for better cost and performance.

5. Is AWS Graviton always cheaper than x86 alternatives?

Graviton offers better performance per dollar, but results vary based on workload characteristics. Continuous evaluation is key, which Sedai automates.