Benchmarking Cloud Providers

Compare apples to apples.

Overview

Benchmarking cloud providers compares AWS, Azure, and GCP on equal footing for a specific workload. Vendor preference rarely survives a per-workload benchmark; the discipline is matching the instance, region, and operational fit before the dollar comparison.

• Compare apples to apples. Per-workload equivalent instance; matched on CPU class, memory, network bandwidth, not just vCPU count.
• Per-instance equivalence. Per-instance CPU generation, memory ratio, network performance; supports honest comparison.
• Per-workload benchmark. Per-workload actual benchmark using production-shaped traffic; produces evidence rather than spec sheets.
• Per-region pricing plus operational fit. Pricing varies by region; operational preference shapes whether the team can actually run the workload.

The approach

The practical approach: per-workload benchmark, per-region pricing, equivalent instance selection, operational fit assessment, documented rationale. The team’s discipline produces matched cloud rather than rumour-driven choice.

• Per-workload benchmark. Production-shaped data; vendor benchmarks rarely match the team’s actual workload shape.
• Per-region pricing. Cost per region; matters because the cheap region is sometimes too far from the user.
• Per-instance equivalence. CPU generation, memory, network; the spec sheet number lies, the benchmark does not.
• Operational fit plus documented results. Team operational preference matters; per-benchmark rationale committed for the next review.

Why this compounds

Cloud benchmarking discipline compounds across years. Each benchmark produces ongoing value; the team’s cloud expertise grows; the next architecture decision is informed by evidence.

• Better operational fit. Right cloud for the workload; the team operates the chosen platform deeply rather than spreading thin.
• Better cost efficiency. Right pricing matches workload; the bill tracks the workload shape, not the vendor’s headline rate.
• Better engineering culture. Evidence-based decisions replace tribal preference; the team converges on data, not opinions.
• Institutional knowledge. Each benchmark teaches cloud patterns; the team’s architecture muscle grows.

Cloud benchmarking discipline is an engineering discipline that pays off across years. Nova AI Ops integrates with cloud telemetry, surfaces patterns, and supports the team’s cloud architecture discipline.