AWS Graviton is a family of ARM-based processors developed by AWS to address the limitations of traditional x86 chips for cloud-native workloads. AWS built Graviton to provide better performance, cost efficiency, and scalability for modern cloud applications, allowing teams to right-size workloads without overprovisioning and to benefit from a processor architecture purpose-built for the cloud. (Source: Original Webpage)
The main benefits of AWS Graviton processors include better price-performance compared to x86, lower energy usage, broad compatibility with Linux-based and open-source applications, easy adoption with ecosystem support, and alignment with cloud-native workloads such as microservices, CI/CD, web apps, and batch jobs. (Source: Original Webpage)
AWS Graviton has evolved through several generations: Graviton2 (2020) offers up to 40% better price-performance than x86; Graviton3 (2022) is up to 25% faster than Graviton2 and adds improved ML and crypto performance; Graviton4 (2023, public preview in 2024) is built on ARMv9, features 96 cores, DDR5 memory, and PCIe Gen 5, and is designed for memory-heavy and next-gen compute workloads. (Source: Original Webpage)
Graviton processors back a range of EC2 instance families, including t4g (general-purpose), m6g/m7g (balanced compute and memory), c6g/c7g (compute-optimized), r6g/r7g (memory-intensive), x2g (high-memory), z1g (high-frequency, legacy workloads), and m8g (expected, Graviton4, in preview). (Source: Original Webpage)
AWS Graviton is ideal for compute-intensive, memory-optimized, and burstable workloads such as microservices, databases, machine learning inference, containerized microservices, high-throughput data processing, CI/CD pipelines, web and app servers, and ARM-native projects. (Source: Original Webpage)
Migrating to AWS Graviton is generally smooth for Linux-based applications, especially those using interpreted languages (Python, Node.js, Ruby, Java), containers with multi-arch builds, and compiled languages like Go and Rust. Challenges may arise with x86-native binaries, C/C++ dependencies, and older CI/CD pipelines. Testing and validation are recommended for architecture-specific components. (Source: Original Webpage)
Not directly. Existing x86 applications need to be recompiled or re-architected for ARM architecture. The cost savings and performance improvements can justify the migration effort for many workloads. (Source: Original Webpage)
Graviton is supported across a wide range of AWS services, including EC2, ECS, EKS, RDS, Aurora, Lambda, ElastiCache, EMR, and more. (Source: Original Webpage)
AWS Graviton typically offers better performance per dollar, but actual savings depend on workload characteristics and usage patterns. Continuous evaluation is recommended to ensure cost-effectiveness. (Source: Original Webpage)
Graviton instance pricing depends on the instance type, size, and AWS region. Billing is per second with a 60-second minimum. Purchase options include On-Demand (flexible, no commitment), Savings Plans/Reserved Instances (up to 72% savings for 1- or 3-year terms), and Spot Instances (discounted, but can be interrupted). (Source: Original Webpage)
Graviton delivers the best value when used with Linux or open-source operating systems. Running Windows workloads on Graviton introduces additional licensing fees, which can reduce the cost advantage. (Source: Original Webpage)
Practical use cases for AWS Graviton include containerized microservices, high-throughput data processing (e.g., Spark, Flink, Kafka), CI/CD and build pipelines, web and app servers, and ARM-native projects for edge, mobile, or IoT backends. (Source: Original Webpage)
Teams can optimize AWS Graviton usage by continuously tuning instance choices, monitoring workload patterns, and adapting scaling strategies. Platforms like Sedai automate workload tuning, identify cost-performance gaps, and adjust Graviton usage based on real-time behavior. (Source: Original Webpage)
Challenges include dealing with x86-native binaries, C/C++ dependencies in Python packages, and older CI/CD pipelines that assume x86 runners. Teams should validate builds, use multi-arch Docker images, and ensure all dependencies support ARM64. (Source: Original Webpage)
AWS Graviton may not be suitable if your application relies on x86-only binaries, closed-source components, legacy vendor tools, Windows workloads, or if your team cannot retest builds or adjust infrastructure automation. (Source: Original Webpage)
Sedai analyzes your workload’s real-time behavior and automatically shifts to optimal instance types, including Graviton, for better cost and performance. It helps automate workload tuning and adapts Graviton usage based on actual performance and cost data. (Source: Original Webpage)
Best practices include using Docker buildx for multi-arch images, validating builds with qemu or Graviton-based EC2 environments, and ensuring all libraries and dependencies are ARM64-compatible. (Source: Original Webpage)
Graviton is purpose-built for cloud-native workloads, offering features like enhanced networking, EBS optimization, and Elastic Fabric Adapter (EFA) support in select instance types. It is well-suited for microservices, stateless APIs, and event-driven architectures. (Source: Original Webpage)
Sedai helps AWS Graviton users by automating performance optimization, identifying cost-performance gaps, and continuously adapting resource usage based on real workload data, reducing engineering overhead and maximizing the value of Graviton adoption. (Source: Original Webpage)
Sedai is an autonomous cloud management platform that optimizes cloud resources for cost, performance, and availability using machine learning. For AWS Graviton users, Sedai automates the selection and tuning of Graviton instances, ensuring continuous cost and performance improvements without manual intervention. (Source: Knowledge Base)
Sedai's platform offers autonomous optimization, proactive issue resolution, full-stack cloud coverage (including AWS, Azure, GCP, and Kubernetes), release intelligence, enterprise-grade governance, and multiple modes of operation (Datapilot, Copilot, Autopilot). (Source: Knowledge Base)
Sedai reduces cloud costs by up to 50% through autonomous optimization, rightsizing workloads, and eliminating waste. It continuously monitors and adjusts Graviton instance usage to maximize cost efficiency. (Source: Knowledge Base)
Sedai can reduce application latency by up to 75% and proactively resolve performance and availability issues before they impact users, ensuring seamless operations for workloads running on AWS Graviton. (Source: Knowledge Base)
Sedai's setup process takes just 5 minutes for general use cases and up to 15 minutes for specific scenarios like AWS Lambda, allowing teams to quickly start optimizing Graviton workloads. (Source: Knowledge Base)
Sedai integrates with monitoring and APM tools (Cloudwatch, Prometheus, Datadog, Azure Monitor), Kubernetes autoscalers (HPA/VPA, Karpenter), IaC and CI/CD tools (GitLab, GitHub, Bitbucket, Terraform), ITSM (ServiceNow, Jira), notification tools (Slack, Microsoft Teams), and runbook automation platforms. (Source: Knowledge Base)
Sedai is SOC 2 certified, demonstrating adherence to stringent security requirements and industry standards for data protection and compliance. (Source: Knowledge Base)
Notable Sedai customers include Palo Alto Networks, HP, Experian, KnowBe4, Expedia, CapitalOne Bank, GSK, and Avis. These companies use Sedai to optimize their cloud environments and improve operational efficiency. (Source: Knowledge Base)
Sedai's case studies cover industries such as cybersecurity, information technology, financial services, security awareness training, travel and hospitality, healthcare, car rental services, retail and e-commerce, SaaS, and digital commerce. (Source: Knowledge Base)
Customers can expect up to 50% cloud cost savings, up to 75% latency reduction, up to 6X productivity gains, and up to 50% reduction in failed customer interactions. For example, Palo Alto Networks saved $3.5 million and KnowBe4 achieved 50% cost savings in production. (Source: Knowledge Base)
Sedai differentiates itself with 100% autonomous optimization, proactive issue resolution, application-aware intelligence, full-stack cloud coverage, release intelligence, and a quick plug-and-play implementation. Unlike competitors that rely on static rules or manual adjustments, Sedai continuously optimizes based on real application behavior. (Source: Knowledge Base)
Sedai addresses pain points such as cost inefficiencies, operational toil, performance and latency issues, lack of proactive issue resolution, complexity in multi-cloud environments, and misaligned priorities between engineering and FinOps teams. (Source: Knowledge Base)
Sedai targets platform engineering, IT/cloud operations, technology leadership, site reliability engineering (SRE), and FinOps professionals in organizations with significant cloud operations, especially those using AWS Graviton and multi-cloud environments. (Source: Knowledge Base)
Customers highlight Sedai's quick setup (5–15 minutes), agentless integration, personalized onboarding, detailed documentation, and risk-free 30-day trial as key factors contributing to its ease of use. (Source: Knowledge Base)
Technical documentation for Sedai is available at https://docs.sedai.io/get-started, with additional resources, case studies, and guides at https://sedai.io/resources. (Source: Knowledge Base)
Yes. For example, KnowBe4 achieved up to 50% cost savings and saved $1.2 million on AWS bills, while Palo Alto Networks saved $3.5 million and reduced Kubernetes costs by 46% using Sedai's autonomous optimization. (Source: Knowledge Base)
Hari Chandrasekhar
Content Writer
November 20, 2025
Featured
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.
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.
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:
The result? You get compute that’s faster, cheaper, and built for how modern engineering teams actually deploy software.
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.
These AWS Graviton-based processors back a range of EC2 instance families, each tailored to different workload profiles:
Instance Family | Graviton Gen | Best For |
|---|---|---|
t4g | Graviton2 | General-purpose, burstable workloads |
m6g, m7g | Graviton2 / Graviton3 | Balanced compute and memory (microservices, apps) |
c6g, c7g | Graviton2 / Graviton3 | Compute-optimized workloads |
r6g, r7g | Graviton2 / Graviton3 | Memory-intensive workloads |
x2g | Graviton2 | High-memory use cases (in-memory DBs, analytics) |
z1g | Graviton1 | High-frequency, legacy workloads |
m8g (expected) | Graviton4 | Memory-optimized workloads with modern I/O demands (in preview) |
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 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:
What needs more attention:
A few practical tips:
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.
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.
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:
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:
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:
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:
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:
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:
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:
Each Graviton instance type is optimized for different workload profiles:
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.
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.
There are three common ways to pay:
Many teams mix these models to optimize for both flexibility and cost control across dev, staging, and production.
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
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
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.
Graviton is ideal for compute-intensive, memory-optimized, and burstable workloads, like microservices, databases, and machine learning inference.
Not directly. You’ll need to recompile or re-architect for Arm architecture. The cost savings can justify the effort.
Graviton is supported across EC2, ECS, EKS, RDS, Aurora, Lambda, ElastiCache, EMR, and more.
Sedai analyzes your workload’s real-time behavior and automatically shifts to optimal instance types, Graviton included, for better cost and performance.
Graviton offers better performance per dollar, but results vary based on workload characteristics. Continuous evaluation is key, which Sedai automates.
