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Do not choose a new CPU first and hope the workload follows. Start by documenting the current workload, its service objectives and every software, binary, device and deployment assumption. Then build and test the same workload on the proposed architecture, benchmark it under equivalent conditions, and move only a controlled slice while the original capacity remains available for rollback.
An architecture change can fail at several different layers: a package may not exist for the target, a binary download may contain only the source architecture, an intrinsic or system call may behave differently, or the application may compile yet miss its latency or throughput target. The following process separates compatibility, correctness, performance and economics so that a “successful build” is not mistaken for a safe migration.
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1. Define the decision and capture a baseline
Write down the specific workload under consideration and the decision you must make. “Move our platform to a different CPU” is too broad; “run the image-processing API for 20,000 requests per minute on architecture X while keeping p99 latency below 150 ms” is testable. Record the source architecture, proposed target, operating-system release, kernel, deployment model, service-level objectives, current capacity and the reason for considering a change.
Record the workload’s operating envelope
- Business function, criticality, availability target and acceptable degradation during a trial.
- Representative request or batch data, normal concurrency, peak or burst behavior and job duration.
- CPU, memory, storage, network and I/O consumption at steady state and during peaks.
- Accelerators, GPUs, storage controllers, encryption modules, SmartNICs and other devices, including required drivers and firmware.
- Current instance or server shape, node count, autoscaling rules, placement constraints and capacity headroom.
- Programming languages, runtime versions, compiler toolchain, build system and compiler flags.
- Operating-system packages, shared libraries, databases, agents, observability components, security tools and proprietary or vendor software.
Arm’s porting methodology emphasizes making this inventory before porting. Its page has no publication date shown, so verify every version and support statement against the current vendor documentation when planning the change.
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Make the baseline repeatable
Save the exact application revision, image digest, configuration, dataset, software versions, node type and traffic generator settings used for the baseline. Capture throughput, average and tail latency, error rate, completion time, CPU and memory utilization, I/O, network use, power or host-level resource data when available, and the capacity required to meet the objective. A baseline that cannot be reproduced cannot establish whether the target is better, equivalent or merely running a different test.
2. Audit software support and architecture assumptions
Compatibility is more than whether the operating system boots. Confirm that every component is supported at the exact versions you intend to deploy, and identify whether support means “builds,” “runs,” or “covered by the vendor in production.” Ask vendors for target-architecture matrices for databases, runtimes, drivers, monitoring and security agents, and document exceptions rather than relying on a generic “Linux supported” statement.
Inspect code and build logic
Arm’s code-assessment guidance identifies common portability hazards. Search source, build files and scripts for:
- x86 SIMD intrinsics such as SSE or AVX, architecture-specific headers and inline assembly.
- Platform-specific system calls, instruction assumptions, byte-order or alignment assumptions and hand-written context-switching code.
- Compiler flags, linker options, build conditions and package names that select only the source architecture.
- Scripts that download a prebuilt executable, JIT, browser, agent or database client without checking the machine architecture.
- Tests, installers and deployment hooks that inspect an architecture string and have no target branch.
Replace an architecture-specific path with a portable implementation where practical, or isolate it behind a tested target-specific module. Keep a list of intentional exceptions and the test that proves each one works.
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Check runtimes, libraries and vendors
Portable languages reduce—but do not remove—the audit. Arm’s cloud-migration material says many modern applications using Java, Go, Python and Node.js can run with little change, but it still instructs teams to verify images and dependencies. Treat that as a screening observation, not a compatibility guarantee. Native extensions, cryptography providers, database drivers, language wheels, profilers and observability agents may still contain architecture-specific code.
For proprietary software, obtain a written support statement for the target architecture and your exact release. Confirm licensing terms, hardware-bound activation, supported virtual-machine types and whether the vendor will investigate target-only failures. Record an owner for every dependency; an unowned binary is a rollback risk.
Verify container and artifact architecture
Every container image and downloadable artifact must publish the target architecture. Inspect image manifests rather than assuming a tag is multi-platform; a tag may resolve to a single source-architecture image. For example, use your registry’s manifest inspection capability (such as docker buildx imagetools inspect <image:tag>) and verify that the target variant is present, has the expected digest and is signed according to your policy.
Check the base image, package repositories, native language dependencies, sidecars, init containers, admission or scanning tools and build cache. Ensure the cluster scheduler and registry preserve the correct variant when a pod lands on each architecture. If an image is rebuilt, record the compiler, linker and dependency versions so a target-only result can be reproduced.
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3. Build and validate on the actual target
Static scanning and emulation are useful for finding likely problems, but neither proves production readiness. A successful compile is only an early checkpoint: the application can still fail at installation, startup, runtime, integration, device access or its service objective.
Add target builds to the delivery pipeline
- Provision a target-architecture builder or runner and pin its operating-system, compiler and SDK versions.
- Build every image and binary for the target, producing a distinct digest or a multi-architecture manifest with auditable provenance.
- Run unit, language-runtime and dependency tests on the target runner; do not run only cross-compiled tests on the source machine.
- Scan the final artifacts for embedded source-architecture binaries and verify that package managers resolved target-compatible versions.
- Publish the target artifact to a non-production registry and deploy it to target hardware using the same manifests and configuration intended for production.
Exercise behavior, not just startup
On target hardware, test installation, startup and shutdown, normal requests, invalid input, retries, timeouts, failover, data correctness, upgrades and recovery from dependency or node failure. Include integration with databases, queues, storage, networking, TLS, monitoring and security controls. If the workload uses an accelerator or device, exercise the complete driver and firmware path; a CPU-only test cannot establish that the device interaction works.
Arm’s migration guidance recommends assessing dependencies and validating performance on Arm hardware early. Its validation material calls for functional correctness, platform compatibility, hardware interaction and performance comparison. Keep failures separated by layer so a package-resolution issue is not misdiagnosed as an application bug.
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Compare architectures with the same application revision where possible, equivalent dependency versions, identical datasets, configuration, concurrency, traffic mix and test duration. Include warm-up, steady-state and the peak or burst condition that drives your service objective. Changing the workload while changing the CPU produces an uninformative result.
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Use measures tied to the service
| Question | Measure to capture | Why it matters |
|---|---|---|
| Does the service respond quickly enough? | Median, p95, p99 and worst-case latency, plus timeout and error rate | Averages can hide tail behavior that violates the objective. |
| How much work can one node do? | Requests per second, records per second, jobs per hour or completion time | Capacity determines node count and operating cost. |
| What limits the result? | CPU utilization, run queue, memory bandwidth and pressure, cache or stall indicators, storage and network throughput | Profiling identifies whether the target needs code, library or configuration changes. |
| What happens under stress? | Behavior at normal, sustained peak and burst load; recovery after overload | A target that wins a short test may fail during a real traffic spike. |
| What does the deployment consume? | Nodes, vCPUs, memory, storage, accelerator time and power or host resource data where available | Resource use must be converted into the cost of the actual deployment. |
Profile both platforms before tuning. Identify whether differences come from compiler choices, a library implementation, synchronization, memory access, I/O or an external dependency. After a meaningful compiler, library or configuration change, repeat the same benchmark and retain the previous result for comparison.
Set acceptance criteria before seeing the result
Define pass/fail thresholds for correctness, availability, tail latency, throughput, resource headroom and cost per unit of work. Include a confidence rule, such as repeated runs with variation within an agreed range, rather than accepting a single best run. The reviewed guidance supports target-platform testing and profiling but provides no universal x86-versus-Arm score; there is no architecture-independent performance result that can substitute for your workload test.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.5. Calculate economics for the real environment
Architecture economics are workload- and location-specific. Calculate the target’s complete cost for the capacity measured in testing: compute instances or servers, memory, storage, network transfer, accelerators, licenses, support, CI runners, observability, power, migration engineering and the spare capacity required for resilience. Compare cost per request, transaction or completed job, not just hourly CPU price.
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Use current provider and region pricing for the exact instance family and commitment terms. Cloud-hosted target instances from AWS, Google Cloud, Microsoft Azure and Oracle Cloud Infrastructure may be candidates to test, but availability, supported images, accelerator options and prices change by provider, region and date. Do not assume that a lower price per vCPU lowers total cost if the workload needs more nodes, different memory ratios or extra engineering.
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One public example illustrates why case studies are not forecasts: Arm’s cloud-migration page attributes a 35% inference-cost reduction to Vociply AI after migrating LLM workloads to Arm-based AWS Graviton. That is a vendor-hosted claim about one customer and workload, not an independently verified or general saving. Use your measured capacity and current invoices instead.
6. Roll out with placement controls and a rollback path
Prepare a mixed-architecture environment
Confirm that your cluster release supports both architectures and that every required image, daemon, admission component, CSI or networking plugin, monitoring agent and security tool has a target variant. Keep source-architecture capacity online. Label or otherwise identify target nodes, and use node affinity, taints and tolerations, or an equivalent scheduler control so that only explicitly selected workloads can land there.
Move in increasing-risk stages
- Laboratory: deploy the target artifact to isolated target hardware and complete functional, integration, device and benchmark tests.
- Staging: replay representative data and failure scenarios with production-like dependencies, alerting and autoscaling.
- Limited production: route a small, explicitly measured slice to target nodes. Compare service indicators with the source pool and watch logs, saturation, retries, tail latency and cost.
- Expansion: increase the target share only when each acceptance threshold remains satisfied for the agreed observation period.
- Completion: retain enough source capacity to restore service until rollback criteria, data-recovery checks and operator runbooks have been exercised.
The Arm OpenShift migration example demonstrates mixed x86 and Arm nodes, scheduling controls and target-node tests before source nodes are removed. The example is specific to OpenShift 4.18 on AWS. It also uses native builds rather than cross-architecture builds; do not generalize that product- and version-specific workflow to every cluster.
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Define rollback triggers
Automate rollback or traffic drain for sustained error-rate, availability or tail-latency breaches; failed correctness checks; target-node saturation; missing observability; or a dependency failure that cannot be repaired within the change window. Keep the previous image digest, configuration and source capacity available. Verify whether data written by the target can be read safely by the source before the first production request, and document how to reverse schema, queue and cache changes.
7. Diagnose common failure modes
| Symptom | Likely checks | Recovery approach |
|---|---|---|
| Build fails while resolving a package | Repository support, package version, native extension and compiler flags | Select a supported target release, rebuild the dependency or replace the component; record the decision. |
| Image pulls but will not start | Manifest architecture, embedded binaries, entrypoint scripts and sidecars | Publish the missing target variant and test the complete pod, not only the main container. |
| Application starts but returns incorrect results | Alignment, byte order, unsafe code, SIMD paths, floating-point assumptions and integration data | Use failing cases to isolate the target-specific path, fix it and rerun the full correctness suite. |
| Latency or throughput misses the target | Profile CPU, memory, I/O, synchronization, libraries and compiler output under the same load | Tune or replace the bottleneck, then repeat the apples-to-apples benchmark before expanding. |
| Only device-enabled deployments fail | Driver, firmware, runtime, permissions and device-plugin architecture support | Validate the complete hardware stack or keep that workload on the source architecture. |
| Rollback cannot restore service | Source capacity, image retention, data compatibility and scheduler rules | Rehearse rollback in staging and keep the source pool and prior artifacts until the rehearsal succeeds. |
8. Make a documented go/no-go decision
Use a scorecard with evidence for each architecture rather than a single CPU label. Require named owners and links to the test record for:
- Support at exact OS, runtime, library, driver and vendor versions.
- Correctness, integration and operational behavior, including accelerators.
- Throughput, latency, reliability and capacity at realistic load.
- Memory, I/O, network, power or host-resource behavior relevant to the service.
- Total operating cost for the selected region, instance or server configuration.
- Migration effort in code, CI, images, observability, staffing and rollback.
Approve the change only when the target meets the pre-set service and correctness criteria, the cost model uses current configuration-specific data, and operators can control placement and restore the source path. If a dependency lacks support, a device path remains untested, or the target misses a critical objective, keep the workload on the source architecture or narrow the migration scope instead of treating uncertainty as approval.
Quick Recap
9. A practical pre-switch checklist
- Workload, source and target architectures and service objectives are explicitly named.
- Baseline traffic, data, software versions, configuration and capacity measurements are reproducible.
- All binaries, images, packages, runtimes, agents, drivers and vendors have target support or a tested replacement.
- Architecture-specific code, compiler flags, scripts and downloadable artifacts have been reviewed.
- Target builds run in CI and final artifacts are verified for the correct architecture.
- Functional, integration, data-correctness, failure and hardware-interaction tests pass on real target hardware.
- Equivalent source and target benchmarks meet predeclared latency, throughput, reliability and resource thresholds.
- Current pricing and capacity data produce a cost per unit of useful work for the selected deployment.
- Mixed-cluster placement, monitoring, limited traffic, rollback triggers and source capacity are ready.
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