Cloud infrastructure comparison

AWS vs Google Cloud Platform

Two of the world's leading cloud providers. AWS is the dominant incumbent; Google Cloud brings deep data analytics, Kubernetes heritage, and AI research capability. Knowing how to discuss the trade-offs clearly is an essential skill for IT professionals working in English.

TL;DR

AWS is the market leader (~32% share), with the broadest catalogue of managed services, the largest ecosystem of tooling and talent, and the most mature enterprise support model.
Google Cloud (GCP) excels at data analytics (BigQuery), container orchestration (GKE), AI/ML (Vertex AI, TPUs), and offers a clean resource hierarchy for IAM that suits large organisations.
Neither is universally better. AWS wins on breadth and ecosystem size; GCP wins on data warehousing, Kubernetes maturity, and certain AI/ML use cases.

Side-by-side comparison

Aspect	AWS	Google Cloud (GCP)
Market share	~32% (global leader)	~11% (third, growing)
Compute	EC2 (200+ instance types), Lambda	Compute Engine, Cloud Run, Cloud Functions
Managed Kubernetes	EKS (Elastic Kubernetes Service)	GKE — the original managed Kubernetes
Data warehousing	Amazon Redshift	BigQuery (serverless, pay-per-query)
Object storage	Amazon S3 (de-facto standard API)	Cloud Storage (S3-compatible API)
IAM model	JSON policy documents on users/roles	Resource hierarchy bindings (org → folder → project)
On-premises extension	AWS Outposts (physical hardware)	Anthos (software platform, multi-cloud)
AI/ML platform	SageMaker, Bedrock, Trainium chips	Vertex AI, TPUs, Gemini models
Pricing discounts	Reserved Instances, Savings Plans (opt-in)	Sustained-use discounts (automatic)
Service breadth	250+ services — widest catalogue	~150+ services — deep in key areas

What is AWS?

Amazon Web Services launched in 2006 and defined the modern public cloud. It offers compute (EC2), storage (S3), databases (RDS, DynamoDB), serverless (Lambda), networking, security, and hundreds of higher-level managed services. AWS's principal advantages are its sheer breadth — nearly every enterprise workload has a purpose-built AWS service — and its ecosystem: a vast marketplace of third-party tools, a large pool of certified engineers, and an extensive partner network.

AWS popularised concepts that are now cloud-industry standards: the S3 object-storage API (widely replicated by competitors), IAM role-based access, availability zones within a region, and the managed relational database service. When engineers say "we're on the cloud", they often mean AWS by default.

What is Google Cloud Platform?

Google Cloud Platform is Google's public cloud, built on the same global infrastructure that powers Search, YouTube, and Gmail. GCP's heritage gives it distinctive strengths: a planet-scale private fibre network, Kubernetes (which originated from Google's internal Borg scheduler), and world-class data analytics through BigQuery.

GCP is the third-largest cloud provider by market share but has been growing faster than its rivals. It is particularly strong in organisations already using Google Workspace, in data engineering teams that value BigQuery's serverless model, and in AI research teams that want access to TPUs or Google's latest Gemini models through Vertex AI.

Key differences explained

BigQuery vs Amazon Redshift — data warehousing

BigQuery is a fully managed, serverless data warehouse. You do not provision a cluster — you load data and run SQL. Billing is per TB of data scanned (or flat-rate for heavy users). For ad-hoc analytics on terabytes of data, BigQuery is often faster to set up and cheaper to operate than alternatives. Amazon Redshift is a mature columnar warehouse that requires provisioning a cluster (or using Redshift Serverless). Redshift gives more control over physical layout and concurrency scaling, which matters for high-concurrency BI workloads. Many data engineers consider BigQuery the benchmark for serverless analytics.

The architectural discussion here overlaps with the broader SQL vs NoSQL debate — choosing a data warehouse involves understanding query patterns, schema flexibility, and operational overhead.

GKE vs EKS — Kubernetes origins matter

Kubernetes was designed by Google engineers and open-sourced in 2014. GKE was the first managed Kubernetes service, meaning Google's team has the deepest expertise in the project. GKE's Autopilot mode removes the need to manage node pools entirely — Google handles capacity, security patching, and scaling. AWS EKS is well-maintained and enterprise-ready, but historically required more manual configuration for networking (VPC CNI, load balancer controllers) and control-plane visibility. If Kubernetes orchestration is your primary concern, GKE has a slight edge in defaults and maturity. For teams already invested in the AWS ecosystem, EKS integrates seamlessly with existing IAM roles and VPC infrastructure. Before committing to either, it is worth reviewing Docker vs Kubernetes to confirm you need a managed cluster at all.

IAM: policy documents vs resource hierarchy

AWS IAM attaches JSON policy documents to identities (users, roles, groups) or resources. This is extremely flexible but can become complex at scale — policy debugging often requires the IAM policy simulator. Google Cloud IAM uses a resource hierarchy: permissions are granted by binding a role to a member at a specific level (organisation, folder, project, or individual resource). Child resources inherit parent bindings. Engineers working across large organisations often find the GCP model easier to reason about for least-privilege governance, while AWS's policy system offers finer per-action granularity.

Anthos vs AWS Outposts — extending to on-premises

Both products address the need to run cloud workloads in on-premises data centres. AWS Outposts delivers physical rack-mounted hardware into your facility; it runs genuine AWS APIs locally, integrating with your VPC and managed services as if the rack were a remote AWS region. Google Anthos is a software platform: it deploys onto existing on-premises hardware (or other clouds) and provides consistent Kubernetes-based management via GKE on-prem, a service mesh (Anthos Service Mesh, built on Istio), and Cloud Config Management. Anthos is inherently multi-cloud — you can manage workloads on AWS, Azure, and on-premises from a single control plane. Outposts is tightly AWS-centric but provides a more seamless experience if your stack is already all-AWS. The architectural implications here parallel decisions about microservices vs monolith — distributed control comes with distributed complexity.

AI and ML offerings

Google Cloud's AI credentials are strong in specific areas: TPUs (Tensor Processing Units), which Google designed for neural-network training, are available nowhere else. Vertex AI provides a unified platform for training, deploying, and monitoring models. Access to Gemini (Google's flagship LLM family) via the API is a differentiator for teams building generative AI products. AWS responds with Amazon SageMaker — one of the most comprehensive MLOps platforms available — plus Bedrock (access to multiple foundation models including Anthropic, Meta, and Amazon's Titan), and Trainium/Inferentia custom silicon. Both clouds are competitive; the choice often comes down to which foundation models and tooling suit your team's existing workflows.

How engineers talk about AWS vs GCP

These are phrases you will hear in cloud architecture discussions, job interviews, and team stand-ups:

"We're heavily invested in the AWS ecosystem" — the team uses many interlocking AWS services (IAM, VPC, ECS, RDS) and switching clouds would require significant re-platforming effort.
"BigQuery is our source of truth for analytics" — the data warehouse is the canonical, trusted store for reporting and BI queries.
"GKE is the most battle-tested managed Kubernetes offering out there" — engineers praise GKE's operational maturity, referencing its origins inside Google's production environment.
"We're going multi-cloud with Anthos as the control plane" — using Anthos to abstract over multiple cloud environments and on-premises infrastructure from a single management interface.
"Lambda is getting expensive at scale — we're evaluating a move to GKE" — cost optimisation discussion comparing serverless functions to containerised workloads running on managed Kubernetes.
"GCP's sustained-use discounts kick in automatically — no need to reserve capacity upfront" — explaining GCP's pricing model as an advantage over AWS Reserved Instances, which require upfront commitment.
"Cloud Storage is S3-compatible, so our existing tooling just works" — noting that GCP Cloud Storage supports the S3 API, allowing tools built for AWS to work on GCP with minimal changes.
"The IAM hierarchy maps cleanly to our org structure" — praising GCP's folder and project hierarchy for managing permissions across teams and departments without duplicating policy documents.

Decision guide: AWS or GCP?

Widest managed-service catalogue and largest talent pool → AWS
Serverless data warehousing and large-scale analytics → GCP (BigQuery)
Kubernetes as your primary workload orchestrator → GCP (GKE), though EKS is competitive
TPU access or deep TensorFlow and Gemini integration → GCP (Vertex AI)
Broadest choice of foundation models for generative AI → AWS (Bedrock)
On-premises extension with a managed hardware appliance → AWS Outposts
Multi-cloud or on-premises management via software platform → GCP (Anthos)
Organisation already standardised on Google Workspace → GCP
Need to hire from the largest pool of certified cloud engineers → AWS
Automatic discounts without upfront capacity commitment → GCP (sustained-use discounts)

CLI examples side-by-side

Common operations using each cloud's command-line tool:

AWS CLI

# List S3 buckets
aws s3 ls

# Deploy a new container revision to ECS
aws ecs update-service \
  --cluster prod \
  --service api \
  --force-new-deployment

# Check estimated monthly cost
aws ce get-cost-and-usage \
  --time-period Start=2024-01-01,End=2024-02-01 \
  --granularity MONTHLY \
  --metrics BlendedCost

gcloud CLI

# List Cloud Storage buckets
gcloud storage ls

# Deploy a container to Cloud Run
gcloud run deploy api \
  --image gcr.io/my-project/api:latest \
  --region europe-west2 \
  --platform managed

# Run a BigQuery query inline
bq query --use_legacy_sql=false \
  'SELECT COUNT(*) FROM
   `my_project.dataset.events`
   WHERE DATE(ts) = "2024-01-15"'

Frequently asked questions

Which has a larger market share — AWS or Google Cloud?

AWS leads with roughly 31–33% of the global cloud infrastructure market (2024 figures), making it the dominant provider. Google Cloud Platform holds around 10–12%, having grown steadily but remaining third behind AWS and Azure. Market share matters less than fit for your workload, but AWS's lead means more community resources, more third-party integrations, and a wider hiring pool.

What is BigQuery and does AWS have an equivalent?

BigQuery is Google Cloud's fully managed, serverless data warehouse. It separates storage from compute, scales automatically, and uses a pay-per-query pricing model. It is widely regarded as the industry benchmark for large-scale analytical queries. AWS's closest equivalent is Amazon Redshift — a managed columnar data warehouse. Redshift requires you to choose and manage a cluster size (or use Redshift Serverless), whereas BigQuery needs no cluster management at all.

Who invented Kubernetes, and does that matter for GKE?

Kubernetes was originally designed by engineers at Google, based on their internal Borg system, and was open-sourced in 2014. Google Kubernetes Engine (GKE) was the first managed Kubernetes service and is still considered the most mature. AWS's equivalent is EKS (Elastic Kubernetes Service). In practice, both run upstream Kubernetes and differences come down to default networking, IAM integration, and control-plane management rather than fundamental capability.

How do AWS IAM and Google Cloud IAM differ?

Both systems use roles, policies, and service accounts to control access to cloud resources. AWS IAM uses JSON policy documents attached to users, groups, or roles. Google Cloud IAM uses a resource hierarchy (organisation → folder → project → resource) and binds roles to members at each level; child resources inherit parent bindings. Many engineers find Google's hierarchy more intuitive for large organisations, while AWS's policy language offers finer-grained control per service.

What is the difference between Anthos and AWS Outposts?

Both products extend the cloud to on-premises infrastructure, but their approach differs. Anthos (Google) is a software platform — it runs on-prem on your own hardware or on other clouds (including AWS and Azure), letting you manage workloads consistently via Kubernetes and a service mesh. AWS Outposts ships physical rack-mounted hardware from Amazon that runs genuine AWS APIs locally. Anthos is more multi-cloud and software-centric; Outposts tightly integrates hardware that literally brings AWS to your data centre.

Which cloud is better for AI and ML workloads?

Google Cloud has a strong claim for AI/ML: it offers TPUs (Tensor Processing Units), Vertex AI, and deep integration with TensorFlow and Gemini models. AWS counters with SageMaker (a broad MLOps platform), Trainium and Inferentia custom chips, and Bedrock for foundation model access. For teams heavily using Google's own models or TensorFlow, GCP is a natural fit. For teams wanting the widest choice of foundation models and the largest managed ML ecosystem, AWS SageMaker is mature and well-documented.

Is pricing simpler on AWS or Google Cloud?

Neither is straightforward, but they differ in approach. AWS has a vast pricing matrix with per-service dimensions; reserved instances and savings plans require upfront commitment modelling. Google Cloud introduced sustained-use discounts that apply automatically without commitment, which many engineers find more transparent for variable workloads. GCP's BigQuery charges per TB of data scanned, which can be surprisingly cheap for infrequent large queries but expensive if queries are poorly optimised.

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