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Hyperscalers design CPUs because small gains in performance per watt, server utilization or cost can add up across enormous fleets. Their aim is not necessarily to replace Intel and AMD everywhere: it is to tune more of the computing stack to their own workloads, power limits and cloud services. Most of their custom cloud CPUs use Arm technology, combining licensed architecture or cores with in-house design and system integration.
What hyperscalers gain by designing CPUs
A hyperscaler operates computing infrastructure at exceptional scale, usually across many data centers and regions. AWS, Microsoft Azure and Google Cloud sell that infrastructure to customers; Meta operates enormous internal systems for its social platforms and AI services. Their products and silicon strategies differ, but each can have workloads large and predictable enough to justify more control over the hardware.
A standard commercial processor has to serve a wide market. A cloud provider can instead target the work that fills its own fleet: web services, databases, storage, analytics, cloud-native applications or the CPU-side work around AI. It can choose trade-offs in core count, memory, cache, power use and input/output that better fit those jobs.
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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware match- Fleet economics: Better throughput or utilization can improve the cost of running a service and the economics of the cloud instance sold to customers.
- Power and capacity: Lower energy use can reduce electricity and cooling needs. In a data center with a constrained power supply, efficiency can also make room for more computing capacity.
- Workload fit: Different services need different balances of cores, memory bandwidth, latency and I/O. Purpose-built options let a provider avoid treating every workload as if it needed the same processor.
- Supply and bargaining flexibility: An in-house roadmap gives a provider another option alongside commercial CPU vendors. That is strategic leverage, not proof that it intends to stop buying their products.
- Cloud differentiation: A provider can use its chips to build distinct instance families or improve the economics of services that rely on them. Whether that appears as a lower customer bill depends on the instance, region, discounts and other costs.
Chip design has high fixed costs: teams, verification, fabrication preparation, packaging, boards, firmware, software support and years of maintenance. A hyperscaler can spread those costs across a vast fleet and customer base. A small business generally cannot. The economics depend on actual fleet use and savings; providers do not publish enough detail to calculate a universal break-even point for a CPU project.
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“Custom CPU” does not mean starting from scratch
CPU customization spans several layers. Buying a standard Intel or AMD processor means using a broadly market-oriented design. Licensing Arm technology provides a different base: a company may license the instruction-set architecture, CPU cores or platform designs, then build parts of the chip and system around it. A provider can customize cache, memory, interconnect, security and I/O, and integrate the CPU with its servers, network, storage, virtualization and cloud software.
A fully independent instruction-set ecosystem is not what the main public cloud CPU programs described here represent. AWS Graviton, Google Axion and Microsoft Cobalt are Arm-based. Google says Axion combines its silicon work with Arm’s Neoverse V2 platform (Google’s Axion announcement).
The strategic prize is often system co-design, not a novel CPU core in isolation. A processor’s value depends on how it works with memory, networking, storage, security, the hypervisor, schedulers, software and the power and cooling design of the data center. Microsoft describes Cobalt as part of a broader infrastructure approach spanning silicon and cloud services (Microsoft’s purpose-built infrastructure announcement). Google likewise presents Axion as part of its custom-silicon strategy (Google’s Axion strategy overview).
Why Arm is attractive—and why x86 remains important
Arm gives providers a widely used 64-bit server architecture and a route to customize hardware without creating a new software ecosystem from nothing. Linux, containers, common compilers and many cloud applications support Arm. The provider can retain a broadly familiar programming model while tailoring the surrounding processor and system.
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That does not make Arm automatically faster, cheaper or more efficient than x86. Results depend on the chip, instance configuration, software and workload. X86 also remains essential for applications tied to existing binaries, proprietary software, drivers, vendor certifications or assumptions about particular instructions. Some workloads may favor the performance of a commercial x86 processor; others are not worth the engineering effort to port.
The result is a choice of architectures, not a clean replacement cycle. A cloud fleet can use Arm for suitable scale-out jobs, x86 for compatibility-sensitive or otherwise better-fitting workloads, and accelerators for specialized computation.
How the major providers use custom silicon
| Provider | Platform and role | What the public claims establish |
|---|---|---|
| AWS | Graviton: Arm-based CPUs offered through EC2 instances | AWS says Graviton5 has 192 cores and up to 25% better performance than Graviton4. This is a vendor-reported generational comparison, not a universal ranking (AWS Graviton overview). |
| Google Cloud | Axion: custom Arm CPU used in C4A virtual machines | Google reports up to 10% better performance per vCPU than the latest Arm-based cloud instances in its stated comparison; results for selected databases and inference workloads are workload-specific (Axion product information). |
| Microsoft Azure | Cobalt 100 and Cobalt 200: Arm-based cloud CPUs | Microsoft describes Cobalt 100 as a 128-core processor and reported up to 40% better performance than its prior-generation Arm-based Azure VMs. In June 2026, Microsoft announced early-access Cobalt 200 VMs and up to 50% generational performance improvement. The latter was an early-access announcement, not evidence of general availability everywhere (Cobalt 100 availability; Cobalt 200 announcement). |
| Meta | MTIA: custom AI accelerators, primarily for Meta’s internal workloads | Meta’s public roadmap emphasizes inference, recommendation and ranking, with broader training ambitions. Meta reports that MTIA 300 to MTIA 500 increased HBM bandwidth 4.5 times and compute FLOPS 25 times; these are Meta’s specifications and roadmap statements, not independent benchmarks (Meta’s MTIA roadmap). |
AWS Graviton targets cloud-scale workloads
AWS launched the first Graviton in 2018 and developed the family for AWS cloud workloads. Its significance is not simply an attempt to sell a faster processor: Amazon can align the chip with server designs and EC2 services, then offer customers another architecture for suitable applications. AWS positions Graviton around price-performance and energy efficiency and reports the Graviton5 figures in the table above (AWS Graviton history and product information).
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Linux services, containers, web applications, microservices and some databases are natural candidates to evaluate, but fit is application-specific. Native libraries, commercial software support and performance of single-threaded work can change the outcome. AWS describes CPU work around agentic AI—including reasoning, planning, networking and file operations—as another reason its CPUs matter (AWS on Graviton and agentic AI).
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Google Axion fits into a broader platform
Google announced Axion in April 2024 as a custom Arm-based CPU for Google Cloud. The C4A VM family brings Axion to customers; Google’s reported comparisons include the per-vCPU result in the table and larger gains on selected database and machine-learning inference workloads. Those results should not be generalized across applications or configurations. Google also uses custom processors and offloads elsewhere in its infrastructure, so Axion is one element of a larger hardware-and-software strategy (Axion announcement; Axion in Cloud SQL and AlloyDB).
Microsoft Cobalt connects processor and cloud service design
Microsoft introduced Cobalt 100 in November 2023, made Cobalt 100-based VMs generally available in October 2024, and said in June 2026 that the VMs had been deployed in 32 Azure regions. These are Microsoft’s dated availability statements; customers should verify the current VM and region they need. Microsoft has reported performance gains for Cobalt 100 and internal services such as Teams and Defender for Endpoint, but those are vendor-reported results, not independent benchmarks (Microsoft’s Cobalt 100 results).
Cobalt 200’s early-access positioning is notable because Microsoft names AI inference, data pipelines and web/API tiers as target uses: workloads that support AI systems rather than performing all the dense model computation themselves. Microsoft continues to use commercial infrastructure alongside its own silicon; its Azure AI and HPC plans describe AMD and NVIDIA products as well as Microsoft designs (Microsoft on Azure AI and HPC infrastructure).
Meta illustrates a different silicon strategy
Meta should not be treated as if it offers a general-purpose custom CPU cloud platform comparable to EC2, Google Cloud or Azure. Its public custom-silicon emphasis includes MTIA accelerators for internal workloads such as recommendation, ranking and inference. That illustrates a broader point: a hyperscaler may design a general-purpose CPU, a specialized accelerator, or both, depending on the work it owns and the scale at which it runs.
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AI needs CPUs as well as accelerators
GPUs and other accelerators perform much of the dense mathematical computation in AI, but a production AI system still needs CPUs to prepare and move data, communicate over networks, access storage, schedule jobs, handle APIs, run tools and code, enforce isolation, and process outputs. An agent that makes several model calls and takes actions between them can involve substantial CPU-side work.
That is why a balanced AI system matters more than a contest between CPU and GPU. Microsoft presents Cobalt 200 as aimed at inference support, data pipelines and web/API tiers; AWS likewise emphasizes CPU work around agentic systems (Microsoft Cobalt 200; AWS on agentic AI).
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Why custom CPUs are not the right answer for every workload
Custom silicon brings engineering costs and long development cycles. It also requires software support over the life of the chip. A provider can optimize for its common workloads, but a customer with a different workload may not see the same advantage. At the same time, commercial Intel and AMD processors serve a broader set of needs, benefit from mature x86 compatibility and validation, and may suit applications requiring particular software support or strong single-threaded performance.
For customers, a CPU-family choice also affects portability. Optimizing software for one architecture can raise migration work later, while maintaining multiple builds and testing paths adds operational effort. A custom instance family is therefore best evaluated per workload, not adopted as a blanket platform decision.
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How to evaluate an Arm-based cloud instance
- Identify the workload’s architecture requirements. Find out whether the application has x86-only binaries, native extensions, drivers or processor-specific instructions. A container is not automatically architecture-neutral: images containing native binaries need an Arm build or a multi-architecture image.
- Confirm support across the software stack. Check the operating system, language runtime and JIT, database extensions, monitoring and security agents, commercial software licenses, and CI/CD build pipeline with each vendor. Check regional availability and the exact VM family as well.
- Port and test a representative build. Rebuild native dependencies, run correctness tests, and exercise realistic concurrency. Keep an x86 option for components that cannot yet move or fail validation.
- Benchmark production-shaped work. Measure throughput, tail latency, startup time, scaling, and cost per completed request or job. Include the real memory size, storage and network needs, software settings and virtualization configuration; vCPU count and clock speed alone do not settle the comparison.
- Compare total cost and portability. Include storage, networking, licensing, support and applicable billing terms rather than comparing VM list prices alone. Weigh measured gains against the cost of maintaining architecture-specific builds and a fallback path.
Cloud prices depend on machine type, region, operating system and billing arrangement, and attached services can materially affect total cost. Use each provider’s current calculator and pricing details for the configuration under consideration: AWS EC2 pricing, AWS Pricing Calculator, Google Compute Engine pricing, Google Cloud Pricing Calculator, Azure VM pricing and Azure Pricing Calculator.
For compatibility and migration details, consult the relevant provider documentation: AWS Graviton migration resources, Azure Arm VM overview and Google Cloud Arm-based VM documentation.
How to read performance claims
Vendor figures such as “up to” percentage gains are useful signals about what a provider is trying to optimize, but they do not establish that every customer will see the same result. The comparison processor, VM size, memory capacity, software, compiler settings, utilization and test method all matter. Internal service results and vendor-selected benchmarks are not independent tests.
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Use benchmarks that resemble the production job and measure the outcome that matters to the business: cost per request, throughput at a required tail latency, or completed work per unit of infrastructure. A high vCPU count, theoretical compute figure or low hourly VM price is not a substitute for that measurement. Independent cloud CPU studies can provide context, but their dates, configurations and methods must match the question being asked (comparative cloud CPU performance and pricing study).
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