OCI vs AWS GCP Azure cost performance benchmarks

Introduction

The cloud buyer’s perennial question is how OCI vs AWS GCP Azure compare on cost and performance. This article walks through head-to-head benchmarks and practical testing approaches across general compute, storage, networking, and AI workloads so you can make data-driven choices rather than relying on list prices alone.

Benchmarking approach and test design

Benchmarks must be repeatable and apples-to-apples. For each provider choose the same region (or closest equivalent), comparable virtual CPU and memory profiles, identical OS images, and the same disk types (e.g., network SSD). Use these practical tests:

• Compute: run a consistent CPU-bound workload (e.g., stress-ng or sysbench) measuring throughput and CPU steal.
• Storage: use fio with representative I/O patterns (random 4K read/write, sequential 128K) to capture IOPS and bandwidth.
• Network: run iperf3 for intra-region and cross-region throughput and measure latency with ping and HTTP request services.
• AI/ML: train a small model (e.g., ResNet on a few epochs of CIFAR-10) and record time-to-train, GPU utilization, and memory behavior.

Calculate cost-efficiency using a simple formula: cost per useful unit = (hourly price * wall-clock hours) / useful work (requests, samples processed, epochs). That yields an apples-to-apples metric like dollars per 1,000 inferences or dollars per training epoch.

Compute and storage findings with examples

Across many real-world tests, OCI vs AWS GCP Azure show distinct trade-offs. OCI often undercuts list compute and block storage prices for standard VM types, which can translate to lower cost per CPU-hour for steady-state workloads. AWS excels in breadth and mature ecosystem integrations (spot markets, instance families), which can reduce cost if you adopt flexibility. GCP frequently offers strong sustained use discounts and networking-optimized machine types that can be cost-effective for data-heavy services. Azure is competitive when enterprise agreements and reserved capacity are negotiated.

Example: a CPU-bound job that completes in 10 hours on OCI at a lower hourly rate may still be more cost-efficient than a faster AWS shape if the AWS per-hour premium outpaces the runtime improvement. For storage, if fio shows OCI delivering similar IOPS at a 20–30% lower price, that improves database TCO directly. Always test your actual workload; synthetic IOPS numbers matter less than latency and tail latency for real databases.

Network and data egress considerations

Network costs and performance are often the invisible drivers of cloud spend. Egress fees, inter-region transfer charges, and load-balancer pricing can dominate when you move large datasets or serve many end-users. In many comparisons:

• AWS historically charges per-GB egress beyond free tiers; optimizations like CloudFront can reduce costs for public content delivery.
• GCP uses tiered pricing and often simplifies sustained transfer discounts for consistent patterns.
• OCI has positioned itself with aggressive egress allowances in some regions and lower typical inter-VM transfer rates, which can unlock savings for data-rich apps.
• Azure pricing depends heavily on region and whether traffic crosses virtual networks or peering boundaries; enterprise terms can change the effective cost.

Practical tip: measure actual egress by running representative data flows and multiply measured GB by provider invoices to validate your forecast. Consider architecture changes (edge caching, compression, regionalization) that reduce billable transfers.

AI and GPU workloads

AI workloads are sensitive to raw GPU throughput, memory bandwidth, and interconnect performance. Benchmarks should include both single-GPU throughput and multi-GPU scaling tests (e.g., distributed training with NCCL). Key practical insights:

• All major providers expose NVIDIA-class GPUs (T4, A100, etc.) and managed services. The effective price per GPU-hour varies widely by region and instance configuration.
• Measure time-to-train and scaling efficiency: a provider with slightly higher hourly GPU rates can still be cheaper if it finishes jobs faster or offers better network interconnect for multi-GPU training.
• Storage I/O and dataset locality are critical: slow dataset reads can negate GPU advantages. Use local NVMe or high-throughput network storage depending on your pipeline.

Example calculation: if a training job runs 25% faster on Provider A but Provider B is 30% cheaper per GPU-hour, compute the end-to-end cost per job—not just hourly rates—to decide.

Making a decision: practical next steps

To choose among OCI vs AWS GCP Azure for your workloads, follow this prioritized checklist:

• Identify dominant workload type (compute bursty, steady-state, I/O-heavy, or AI).
• Run the four targeted tests (compute, storage, network, AI) in each provider’s comparable region and record cost per useful unit.
• Factor in operational costs: management tooling, managed services, and team familiarity.
• Model 12–36 month TCO including reserved/committed discounts and expected egress/data growth.

Automate benchmarking with scripts and CI jobs so you can re-run tests as prices and instance families change.

Conclusion

OCI vs AWS GCP Azure each have strengths: OCI often wins on list price and predictable blocks, AWS on breadth and spot optimisation, GCP on sustained use and network features, and Azure on enterprise integration. The right choice depends on measured cost per unit of work rather than hourly sticker price. Run targeted, repeatable benchmarks across compute, storage, network, and AI to quantify cost-performance for your specific workloads and update them regularly.