Navigating GCP Instance Types: What To Use And When

Google Cloud Platform (GCP) might not always be the loudest name in the cloud room. But it’s gradually become a powerhouse for organizations running data-intensive, AI/ML, and global-scale applications.

We also can’t ignore that GCP offers a backbone powered by Google’s own infrastructure (the same one that runs YouTube, Gmail, and Search).

In this guide, we’ll walk you through GCP instance types (Compute Engine machine types), compare them to help you match the right ones to your workloads, and highlight key considerations that will keep both your engineering and finance teams happy.

We’ll also share how a cost optimization platform like CloudZero can help you manage instance-related spending. This can give you the performance you need without blowing your budget.

Why Choosing The Right Instance Type Matters

For engineers, picking the right VM instances impacts performance and uptime. For CTOs, it shapes scalability and resource efficiency. And for CFOs, well, every misstep shows up on the cloud bill.

Whether you’re running web applications, data analytics pipelines, or AI/ML workloads, the GCP instance type you choose directly impacts:

Yet, with all the options available, it’s easy to over-provision, underutilize, or simply pick the wrong fit. And in a world where cloud waste costs organizations about $44.5 billion annually, that’s not a mistake anyone can afford. 

The FinOps Foundation found that most organizations are prioritizing waste reduction for this very reason.

Credit: FinOps Foundation Key Priorities Shift Survey

The challenge isn’t unique to GCP. 

Image: AWS, Microsoft Azure, and GCP users want to reduce cloud costs, at least the unnecessary type.

A Quick Overview Of GCP Instance Types

If you’re familiar with Amazon EC2 instances (AWS) or Azure Virtual Machines, you’ll find GCP Compute Engine machine options strikingly similar.

All three top cloud providers offer a sprawling menu of instance types. They offer similar VM categories (general-purpose, compute-optimized, memory-optimized, storage-optimized, etc.), flexible pricing (on-demand, reserved, spot/preemptible), and global infrastructure. 

Related read: AWS Instance Types Compared: How to Choose The Right Option

Where GCP edges ahead for many is its focus on:

These are all features that can make a real difference in both performance flexibility and cost efficiency. 

That’s not all.

Key concepts to know about GCP machine types

With that clarified, here’s a quick overview of the VMs.

What are the different categories of GCP machines available today?

Consider these GCP instance categories (a.k.a. Compute Engine machine families, series, and examples):

Compute Engine machine family	GCP Machine series (Examples)	Best for	Key highlights
General purpose	E series (E2) C series (C3, C3D, C4) T series (Tau, T2A, Tau, T2D) N series (N1, N2, N2D, N4)	Web servers, app servers, development environments	Balanced vCPU/memory, customizable, cost-efficient
Compute-optimized	H3 series (H3) C2 series	High-performance computing, gaming servers, latency-sensitive apps	High vCPU-to-memory ratio, faster CPUs
Memory-optimized	X4 series M4 series  M3 series  M2 series M1 series	Large databases (SAP HANA), in-memory analytics	High memory-to-vCPU ratio, designed for memory-heavy workloads
Storage-optimized	D series (D2, D3, D3 Standard) Z3 series (Z2 highmem)	Workloads that are low in core usage but high in storage density	High local storage throughput and IOPS, local SSDs
Accelerator-optimized	A2 (GPU) series A3 series A4 series G2 series  TPU VMs	Designed for massively parallelized workloads that require GPUs or TPUs	GPUs (NVIDIA) or TPUs attached, optimized for Artificial Intelligence and Machine Learning

Table: GCP Cloud Engine machine families and series

Next, we’ll look closer at each category, without making things too complicated.

GCP Instance Types Explained: Comparing Instances For Your Workload Needs

GCP Compute Engine offers various machine types to meet virtually any compute need.

General purpose Compute Engine machine types

General purpose instances are the workhorses of GCP. They offer a balanced ratio of vCPUs to memory, supporting everyday workloads such as web servers, microservices, small-to-medium databases, and development environments. 

But even within this category, GCP offers several options, each with its own nuances for cost, performance, and flexibility.

Machine series	Processor	Best use case	Key features
E2	Intel or AMD	dev/test, cost-sensitive workloads	Lowest cost, no sustained use discounts needed, flexible CPU platforms
N2	Intel Xeon Cascade Lake	General workloads that need better performance	Higher performance than E2, supports custom machine types
N2D	AMD EPYC Rome	Cost-effective compute with more memory bandwidth	Up to 13% lower cost than N2, ideal for workloads needing high memory bandwidth
C3 (General Purpose flavor)	Intel Sapphire Rapids	Cutting-edge workloads that need higher performance and efficiency	Latest CPUs, improved networking, better performance per dollar

Table: GCP General purpose machines

Some additional notes to take here:

Compute-optimized Compute Engine machine types

These Compute Engine instances prioritize high vCPU-to-memory ratios, faster processors, and advanced networking performance to run compute-heavy workloads efficiently.

Choose GCP’s compute-optimized machines when raw compute power is your top priority. Think high-performance computing (HPC), gaming servers, media transcoding, or latency-sensitive applications.

Machine series	Processor	Best use case	Key features
C2	Intel Xeon Scalable (Cascade Lake)	HPC workloads, gaming servers, latency-sensitive apps	High clock speed CPUs, excellent single-threaded performance
C2D	Intel Xeon Scalable (Sapphire Rapids)	Cloud-native apps, next-gen HPC workloads	DDR5 memory, PCIe Gen5, advanced networking, Hyperdisk support
H3	AMD EPYC Genoa	Advanced HPC workloads (CFD, seismic, weather modeling)	High memory bandwidth, ultra-fast networking, high-core count

Additional notes:

Memory-optimized Compute Engine machine types

These instances offer the highest memory-to-vCPU ratios available on Compute Engine. This makes them ideal for memory-intensive applications. And that includes large-scale databases, in-memory analytics, and high-performance computing (HPC).

Machine series	Processor	Memory	Best for	Key features
M1	Intel Xeon (Skylake, Broadwell E7)	Up to 4 TB	Medium in-memory databases (e.g., SAP HANA), in-memory analytics, business warehousing workloads	Predefined machine types; supports Persistent Disk and Hyperdisk; available in select regions
M2	Intel Xeon Scalable (Cascade Lake)	Up to 12 TB	Large in-memory databases, in-memory analytics, business warehousing, genomics analysis	Predefined machine types; supports Persistent Disk and Hyperdisk; available in select regions
M3	Intel Xeon Scalable (Ice Lake)	1 to 4 TB	OLAP and OLTP SAP workloads, memory-intensive applications (e.g., genomic modeling, EDA), HPC	Predefined machine types; supports Hyperdisk Balanced storage; gVNIC networking; available in select regions
M4	Intel Xeon Scalable  (Sapphire Rapids)	Up to 3 TB	OLAP and OLTP SAP workloads, memory-intensive applications (e.g., genomic modeling, EDA), HPC	Predefined machine types; supports Hyperdisk Balanced and Extreme; gVNIC networking; available in select regions
X4	Intel Xeon Scalable  (Sapphire Rapids)	16 to 32 TB	Extra-large SAP HANA systems, enterprise-grade ultra memory-equipped IaaS, high-performance computing	Bare metal instances; supports only Hyperdisk storage; ultra-fast networking; available in select regions

Note the following:

Storage-optimized Compute Engine machine types

Storage-optimized machines are tailored for applications that demand high local storage capacity and throughput.

Machine series	Processor	Local storage	Best use case	Key features
D2	Intel Xeon E5 (Haswell)	Up to 3 TB HDD	Big data (Hadoop), sequential I/O processing	High local HDD storage, lower cost, good for batch workloads
D3	Intel Xeon Scalable (Cascade Lake)	Up to 3 TB SSD	Data warehousing, analytics workloads	High-performance local SSD storage, higher IOPS
D3 Standard	Intel Xeon Scalable (Cascade Lake)	Up to 3 TB SSD	Cost-effective storage-heavy workloads	Balanced storage and compute, SSD storage, more affordable than D3
Z3-highmem–88	Intel Xeon Scalable (Sapphire Rapids)	36 TiB Titanium SSDs	SQL/NoSQL/vector databases, data analytics, media streaming	High memory (704 GB), Titanium SSDs, up to 100 Gbps networking
Z3-highmem-176	Intel Xeon Scalable (Sapphire Rapids)	36 TiB Titanium SSDs	Large-scale database and analytics workloads	Maximum memory (1,408 GB), Titanium SSDs, up to 200 Gbps networking

D3/D3-Standard machines offer solid performance for most analytics workloads. Meanwhile, Z3 provides up to 200 Gbps networking (and up to 1.4 TB memory) and is ideal for data-intensive and distributed workloads.

Accelerator-optimized Compute Engine machine types

Accelerator-optimized machines come equipped with GPUs or TPUs (Tensor Processing Units). This ensures they provide the specialized hardware needed for compute-intensive tasks like model training, inference, scientific simulations, and rendering at scale.

Machine series	GPU model	vCPUs	Memory	Best use case	Key features
A2	NVIDIA A100 (40GB or 80GB)	12 to 96	85 to 1360 GB	ML training and inference, HPC workloads	High GPU memory bandwidth, NVLink support, up to 100 Gbps networking
A3	NVIDIA H100 (80GB) or H200 (141GB)	26 to 224	234 to 2952 GB	Large-scale ML training, foundation model serving	High GPU memory, NVLink, up to 3,600 Gbps networking
A4	NVIDIA B200 (180GB)	224	3968	Massive-scale ML training, large language models	Highest GPU memory, NVLink, up to 3,600 Gbps networking
G2	NVIDIA L4 (24GB)	4 to 94	16 to 384	Graphics-intensive applications, video transcoding, inference workloads	Supports virtual workstations, customizable vCPU and memory, up to 100 Gbps networking

And there you have them.

Criteria To Consider When Choosing Compute Engine Instance Types

Choosing the right GCP instance type is about balancing performance, scalability, and cost-efficiency to avoid under- or overprovisioning.

Here are key factors to consider:

1. Understand your workload requirements

For example, compute-bound workloads (like HPC simulations or gaming servers) perform best on compute-optimized instances (such as C3 or H3 series) with high vCPU-to-memory ratios.

Benchmarking is best practice here. CTOs and engineers will want to test workloads across different instance types before committing. Google Cloud often provides trial credits so you can experiment with configurations.

2. Plan for Scalability and Flexibility

Consider whether your app needs to scale vertically (adding more CPU/memory to a single instance) or horizontally (adding more instances). Custom Machine types allow you to fine-tune vCPU and memory independently to avoid overprovisioning.

For example, a memory-heavy app with low CPU demands can be right-sized for better cost-performance. For CFOs, this flexibility often translates to better cost-to-performance ratios.

3. Consider pricing models

Consider these:

You can blend the pricing models, too. For example, use CUDs for steady workloads and Preemptible VMs for flexible, short-lived tasks.

4. Factor in regional availability and latency

Not all Compute Engine machine types, GPUs, or storage options are available in every GCP region. Consider deploying closer to your end users to minimize latency and reduce data transfer costs. Also, use GCP’s network performance tools to measure latency across regions before deploying latency-sensitive workloads.

Related read: A Guide To GCP Regions (And How They Affect Your Costs)

5. Evaluate networking and storage performance

Some instances, such as C3, Z3, and A4, offer ultra-fast networking (up to 3,600 Gbps in A4), ideal for distributed systems or ML workloads. You can pair high-performance compute with storage options like Hyperdisk or Titanium SSDs to avoid bottlenecks.

6. Watch for “hidden costs”

Data transfer fees, licensing, and discount eligibility can significantly affect your total cost of ownership (TCO). For example, data egress fees between regions or outside GCP can add up quickly. 

Sustained Use Discounts (automatically applied) offer savings as instances run longer throughout the month. Some specialized instances (e.g., SAP-certified VMs) may include additional licensing fees, so remember to factor these into your forecasts.

In short, choosing the right GCP instance type is a multi-dimensional decision, where you’ll want to optimize for both performance and budget efficiency.

Take The Next Step: Optimize Your GCP Costs Across Instance Types With Confidence

From benchmarking your workloads to blending pricing models and avoiding hidden costs, making smart choices upfront can pay off in better performance and long-term savings.

But here’s the thing: even with the right Compute Engine machine types, cloud costs can still creep up unnoticed, especially across distributed teams and environments.

That’s where CloudZero comes in to help you choose smart and spend smarter.

CloudZero gives you real-time, granular visibility into your GCP costs. You’ll get the immediately actionable insights most cost tools miss. 

Think of cost per customer, product, and deployment. So, you’ll know exactly where your spend is going and how to optimize it without sacrificing performance or innovation.

With CloudZero, you’re never in the dark about rising costs or inefficient usage. You get the clarity to make smart decisions that protect engineering performance and financial profitability.

It’s why ambitious teams at Drift, Duolingo, Skyscanner, MalwareBytes, among others, trust CloudZero to help them spend smarter. (Drift saved over $3 million, and Upstart saved $20 million with CloudZero!)

Don’t just take our word for it. See for yourself — risk-free. to start optimizing your GCP environment without sacrificing speed or innovation.