Single service comparison

Azure Load Testing centers on Apache JMeter as its execution engine: you author standard JMX scripts, the service provisions managed JMeter infra-engine instances across Azure regions, and it ingests JMeter plugins and CSV datasets. CodeArts PerfTest is Huawei's cloud-hosted performance testing service but exposes its own protocol simulators (HTTP, WebSocket, TCP) through a GUI/script model rather than running arbitrary JMeter pods, so test-artifact portability is not guaranteed and JMX scripts typically need re-creation or adapter rewriting rather than lift-and-shift.

The two services differ in observability and pipeline integration. Azure Load Testing natively emits Application Insights correlation, server-side metrics, and Live Metrics, and integrates with Azure DevOps/GitHub Actions as a release gate with pass/fail criteria. CodeArts PerfTest reports are viewed within CodeArts and integrate with CodeArts Pipeline, but cross-tool correlation to APM/trace backends (APM, AOM) and the metric criteria syntax differ; validate equivalent gate semantics before relying on pass/fail criteria.

Operational responsibility and regional reach differ. Azure Load Testing runs load from selectable Azure source regions with engine-instance scaling and VNet-injection for private endpoints. CodeArts PerfTest runs on Huawei Cloud in supported CodeArts regions; its pressure-generator scaling, VPC/private connectivity model, and engine placement are bound to Huawei Cloud region and network capabilities, so target-region availability and private-access patterns must be re-planned for the destination topology.

Start with an assessment that inventories existing JMX plans, CSV datasets, parameterization, pass/fail criteria, APM correlations, and pipeline gate usage. Confirm target CodeArts regions where CodeArts PerfTest is available and whether private/VPC access to the system under test is supported there; this determines whether PerfTest alone is sufficient or whether you must compose it with CodeArts Pipeline plus APM/AOM for telemetry.

Re-author tests in CodeArts PerfTest using its script/GUI model rather than importing JMX directly; map transactions, think times, dataset files, assertions, and ramp profiles to the PerfTest equivalents. Re-establish APM correlation against Huawei backends (APM for application traces, AOM for metrics/logs) and replicate pass/fail criteria in PerfTest so release gates behave consistently with the original pipeline intent.

Validate with a parallel-run approach: execute the same workload against a staging target on both platforms and compare response-time percentiles, throughput, error rates, and resource utilization, accounting for engine placement and network-path differences. Cutover only after PerfTest gates match the prior thresholds and stakeholders accept the simulation fidelity; keep the Azure test as a reference until at least one full release cycle is gated by PerfTest.

Recalculate the cost model and note gaps. Azure typically charges per engine-VM hours plus extra load-engine units and managed VNet injection, while Huawei bills CodeArts by resource package or per-test runtime/pressure units; reprice for peak concurrency, test frequency, retention, and any cross-region traffic. Expect gaps in JMeter-plugin support, third-party CI integrations, and result-export formats—budget adapter work and do not assume feature parity.