AWS EC2 vs GCP Compute Engine: Cloud Server Computing Power Performance, Custom Configuration and Billing Depth Measurement
In the process of landing and migrating enterprise cloud architecture, selecting computing resources is always the first and most important step. Whether it's supporting highly concurrent Web services, distributed computing for processing big data, or AI model training, the computing power performance, configuration flexibility, and long-term cost structure of the underlying cloud server directly determine the ROI of the project.
As the two largest cloud computing markets in the world, AWS's EC2(Elastic Compute Cloud) and Google Cloud's GCP Compute Engine(GCE) represent the highest level of public cloud computing facilities at present. But in the actual landing process, the two "character" is very different. This article will be from
Calculation force performance
,
Custom configuration flexibility
and also
Billing and Discount Policy
In three dimensions, the core differences between AWS EC2 and GCP Compute Engine are deeply dismantled to help architects and operations teams avoid the selection trap.
1. computing power performance comparison: hardware richness vs vertical optimization.
Judging from the hardware accumulation of infrastructure, AWS EC2 undoubtedly has the largest instance family in the world, while GCP is catching up with its powerful underlying network architecture and self-developed chips.
1. Processor and computing instance richness.
AWS EC2: The architecture types are extremely diverse. Not only does it fully cover the latest generation cores of Intel Xeon and AMD EPYC, AWS's self-developed ARM architecture processor Graviton (such as Graviton3/Graviton4) is its biggest trump card. When dealing with conventional Web services, microservices and cache nodes, Graviton instances have a very clear competitive advantage in terms of cost performance. For AI training and reasoning, AWS also deployed self-developed Trainium and Inferentia chips.
GCP Compute Engine: Instance types are relatively clear and concise, mainly divided into general-purpose (N series, C series), computing optimization (C2/C3), memory optimization (M series) and accelerated computing (A series/T series). GCP also provides the latest architectures of AMD and Intel, but it mainly relies on Ampere Altra processor (Tau T2A) in ARM field, and the self-developed chip focuses more on TPU(Tensor Processing Unit). With Google's powerful AI/ML ecological blessing, the distributed deep learning scene performs extremely well.
2. Storage and network IO throughput
Network performance: GCP's underlying network backbone is its recognized strength. GCE is connected by default to Google's globally owned private backbone network for cross-region (Cross-Region) communication and low latency demanding applications
Excellent network jitter control. AWS provides very high network bandwidth of up to 400 Gbps through Placement Groups (Placement Groups) and EFA(Elastic Fabric Adapter), which is more suitable for HPC (High Performance Computing) and large-scale clusters.
Disk IO: AWS's EBS(Amazon Elastic Block Store) provides a complete gradient from general SSD (gp3) to provisioned IOPS (io2 Block Express); GCP also provides persistent disks (Persistent Disk) and Hyperdisk, but the configuration process of GCP is slightly more intuitive in the linear experience of IOPS expansion.
2. custom configuration flexibility: standardized specifications vs free combination
When your business needs a server with a non-standard specification of "2-core 13G memory", the two platforms deal with it in very different ways.
--------------------------------------------------------------------------------------
| Compare Dimensions | AWS EC2 | GCP Compute Engine |
--------------------------------------------------------------------------------------
| Instance specification mode | Fixed echelon (example: c6i.xlarge, 2xlarge)| Default specification Custom specification |
| Resource Adjustment Granularity | CPU and memory are bound and scaled out by multiples | CPU and memory ratios can be independently specified as needed |
| Adjust configuration downtime requirements | You need to stop the instance to change the Instance type | You also need to stop the instance to change the configuration |
--------------------------------------------------------------------------------------
1. AWS EC2: Standard "Echelon" Specification
AWS EC2 uses a strict instance specification sheet ". For example
c6i.xlarge
Corresponding within 4 vCPU / 8 GB
Save,
c6i.2xlarge
This doubles to 8 vCPU / 16 GB RAM. The advantage of this model is that
High degree of standardization
The performance boundary is clear and the benchmark is easy to land. But the disadvantages are also obvious: if your application eats too much memory but does not need more CPU cores, you will have to buy a larger CPU for enough memory space, resulting in a waste of computing power.
2. GCP Compute Engine: Flexible "Custom Machine Types"
One of the core highlights of GCP is to allow users
Free combination of CPU and memory
.
In GCE, you can arbitrarily specify a personalized configuration such as "3 vCPU 11.5GB memory. This mechanism can achieve extremely high utilization rate for specific resource offset services (such as large cache nodes and high concurrency light logic services), completely avoiding the resource redundancy of "buying CPU for memory.
3. billing model and discount strategy: complex fine vs automatic money saving.
The essence of public cloud is to pay on demand, but if you don't understand the billing rules, the cost of computing power can easily exceed the standard. In terms of account recharge and budget management, AWS and GCP have moved towards two completely different design philosophies.
AWS EC2 billing mechanism:
[On-Demand]-(Prepaid/Committed)-> [Reserved Instance (RI) / Savings Plans] (Most aggressive)
The ---> [Spot instance] (interrupted at any time, very low discount)
GCP Compute Engine billing mechanism:
[On-Demand]-(Automatic Trigger)-> [Continuous Use Discount (SUD)] (No Manual Commitment Required)
-(Long-Term Commitment)--> [Committed Use Discount (CUD)] (1 year or 3 years)
(Preemptive) ── ─ ─> [Spot VMs]
1. Basic billing and second-level settlement
AWS EC2: Most Linux instances are billed per second (minimum 60 seconds), and the pricing structure is transparent. For large customers or multinational enterprises, a unified budget recharge and settlement is usually arranged. When planning an enterprise budget, be sure to pay attention to the fund flow and quota control after the AWS Amazon account is recharged to ensure that you have sufficient credit limit and payment channel support when deploying large-scale clusters.
GCP Compute Engine: also uses per-second billing (
minimum billing 1 minute). GCP has a slight advantage in visualizing billing details, and the console can calculate the current month's cost trend in real time.
2. Comparison of long-term discount schemes
Savings Plans/Reserved Instances (RIs) for AWS: AWS offers significant discounts (up to 70% +), but the threshold is relatively high. You need to explicitly commit to one or three years of usage (calculated in $/Hour). Although the Compute Savings Plans provides cross-region and cross-instance family flexibility, you still need to make detailed usage predictions in advance.
GCP's Continuous Use Discount (SUD) vs. Committed Use Discount (CUD):SUD (Sustained Use Discounts): GCP's most laudable "no sense of saving money" mechanism. If the running time of an instance exceeds a certain percentage in the current month, the system will automatically give a step-by-step discount without signing a contract in advance. CUD (Committed Use Discounts): Similar to AWS's reserved commitment, signing a contract for 1 or 3 years can be exchanged for a substantial discount, supporting commitment by resource (CPU/RAM).
3. Preemptible/Spot Instance (Spot Instances)
Both offer low-cost solutions that take advantage of idle computing power: AWS's
EC2 Spot
and GCP's
Spot VMs
. The price is generally only 10% ~ 30% of that of on-demand instances, but cloud vendors may withdraw resources at any time after 30 seconds to 2 minutes of notice, which is suitable for stateless services, CI/CD cluster construction and distributed rendering.
4. architecture selection advice: how should you choose?
Choosing a cloud server is not "who is better", but "who is better for your workload".
Scenarios where AWS EC2 is preferred:
Large cloud ecosystem dependency: The business is deeply integrated with other AWS exclusive services (such as Aurora database, DynamoDB, Lambda, etc.).
Extreme hardware customization requirements: You need to adopt a self-developed ARM architecture (Graviton) to pursue the ultimate cost performance, or an HPC computing cluster that requires extremely high bandwidth.
Compliance and global reach: AWS has a broader Availability Zones (AZ) infrastructure in remote regions with high specific compliance requirements.
Scenarios where GCP Compute Engine is preferred:
Non-standard resource ratio: Applications have special requirements for the ratio of CPU to memory, which can significantly save costs through custom specifications (Custom Machine Types).
AI/Big Data Deep Fusion: Strongly Relies on Google's BigQue
ry, Kubernetes (GKE), or native TPU for deep learning computations.
Focus on network quality and simple billing: the pursuit of low latency in cross-border networks, and hope to automatically enjoy cost-effective continuous use discounts without making complex 3-year contract commitments.
Today, when cloud computing enters deep water, the cost of computing power has changed from a "technical problem" to an "operational and financial (FinOps) problem". Reasonable hardware selection combined with accurate billing planning is the golden rule to ensure the stability and efficiency of enterprise cloud architecture.
