Google Cloud Platform (GCP) and Amazon Web Services (AWS) are two of the most widely used cloud computing providers. Both offer scalable infrastructure and hundreds of cloud services across compute, storage, and networking.

This guide compares pricing models, core services, use cases, and performance to help technical teams choose the best fit for their integration needs and operational priorities.

GCP vs AWS: Side-by-side comparison

GCP vs AWS: Feature deep dives

GCP vs AWS: Compute

Both AWS and GCP have a wide selection of compute services:

Virtual machines:

• GCP provides various compute services, such as Google Compute Engine (GCE), Google App Engine (GAE).
• AWS’s leading compute service is the Elastic Compute Cloud (EC2), equivalent to GCE.
• AWS provides hundreds of instance types optimized for GPU, memory, CPU, and storage. It also has custom Graviton ARM CPUs, which offer better performance at a lower cost.
• GCP offers an equivalent set of instances with fewer present options. However, GCP allows custom vCPU and RAM combinations.

Containers and orchestration

• GCP provides the Google Kubernetes Engine (GKE) for containerized workloads and integrates with Anthos for hybrid and multi-cloud portability.
• AWS has the Elastic Kubernetes Service (EKS). While both are fully managed Kubernetes services, GKE provides multi-cloud portability.
• AWS also provides ECS, a non-Kubernetes container platform that uses AWS's native orchestrator.

Serverless

• GCP offers both Cloud Functions and Cloud Run, which enable you to run code without provisioning or managing servers.
• AWS Lambda supports more runtime environments, with a maximum of 15 minutes per function
• GCP Cloud Functions 2nd gen allows for up to 60 minutes per function.

Specialized compute hardware

• Both GCP and AWS provide multiple options for GPUs and accelerators. GCP offers Tensor Processing Units (TPUs), while AWS provides a range of NVIDIA GPUs. AWS also includes custom ML chips like AWS Trainium (purpose-built for AI training) and AWS Inferentia for inference.
• AWS provides a broader variety of compute options and instance types than GCP. However, GCP has a more simplified, developer-friendly approach and more customization.

GCP vs AWS: Services

GCP’s primary services are more streamlined around a specific domain, while AWS has some overlapping services for a similar task to give developers more options.

Each platform has its key differentiators:

GCP has BigQuery, a serverless data warehouse platform for big data querying. BigQuery stands out among competitors such as Amazon Redshift. Another standout service is Vertex AI, which provides advanced machine learning capabilities.

AWS stands out for its broad choice of databases that help developers meet diverse workload needs. These include:

• Amazon RDS: Managed relational database service for PostgreSQL, MariaDB, MySQL, and SQL Server.
• Amazon DynamoDB: NoSQL performance with millisecond performance.
• Amazon ElastiCache: Caching service with microsecond latency.
• Amazon Neptune: Graph database for graph analytics with a serverless option.
• Amazon Keyspaces - Apache Cassandra–compatible database service.
• Amazon DocumentDB: Document database service compatible with the MongoDB API.
• Amazon Aurora: Distributed SQL database.
• Amazon MemoryDB: In-memory database compatible with Redis OSS and Valkey.

It also offers the AWS Snowball for offline data transfer, Ground Station for satellite communication, and Amazon Braket for quantum computing experiments. It also provides multiple services focused on IoT, such as IoT Core, IoT SiteWise, and IoT Greengrass.

GCP vs AWS: Data and analytics

GCP’s data capabilities are mostly pegged to BigQuery. Its architecture is designed to handle high-speed analytics without configuring servers. Users can connect to data sources and start running SQL queries. It also has built-in machine learning via BigQuery ML, where you can train data models at scale.

Amazon Redshift is the closest to GCP’s BigQuery, but it works more like a traditional data warehouse. While it’s a managed service, it is not completely hands-off, as you have to perform tasks like vacuuming tables and sorting keys. It performs well for structured data.

While Redshift integrates well within AWS, BigQuery integrates with other Google services such as Google Sheets and Looker Studio, alongside external data sources such as Cloud Storage JSON.

Machine learning platform

• GCP’s Google Vertex AI provides a unified AI and machine learning experience. It gives developers pre-built and custom models to build, train, tune, deploy, and monitor ML workloads at scale.
  • Vertex AI also integrates with BigQuery, allowing you to easily train models on data in BigQuery using SQL and export to Vertex pipelines.
  • Vertex AI is more beginner-friendly, mainly due to the AutoML feature.

• AWS uses Amazon SageMaker for machine learning workloads. SageMaker provides access to Amazon Redshift data warehouses, S3 data lakes, and other third-party sources. It enables workloads like data labeling, hyperparameter tuning, and endpoint model deployment.
  • SageMaker has excellent integrations that give it a more fine-grained control, especially for ML engineers.

GCP vs AWS: Networking and performance

Both GCP and AWS have reliable network architecture, but with subtle differences:

Global network architecture

• Google Cloud has a fully global, private network by default. Creating a Virtual Private Cloud (VPC) becomes a global resource spanning all Google Cloud regions. Besides, Google has an extensive private fiber network that provides low-latency, reliable transit between areas.
• AWS VPCs are region-specific, meaning that traffic between regions incurs data transfer fees. Also, most networking services, such as the Application Load Balancer, are regional.

Regions and availability zones

• GCP is available in 200+ territories, with 40+ regions and 120+ availability zones, enabling you to choose where to deploy your applications for low latency and high availability.
• AWS has slightly fewer, with 35+ regions, each with a minimum of three physically isolated AZs, and a total of 110+. Each AZ has independent hardware, cooling, and physical security, ensuring workloads are fault-tolerant.

Edge locations

• Despite GCP having more regions and availability zones, AWS has far more edge locations. Whenever you deploy an application on AWS, you can use CloudFront to cache content and serve users with reduced latency.
• GCP’s Cloud CDN has fewer, with just over 180 Edge locations. AWS also provides local zones and mini data centers in metro areas to provide more fault tolerance and low latency.

GCP vs AWS: Security and compliance

Google Cloud and AWS follow a shared responsibility, whereby providers secure infrastructure and services, while users secure their applications, configurations, and data.

Identity and access management (IAM)

Both platforms use fine-grained, policy-driven IAM and support single sign-on (SSO) with external identity providers and short-lived credentials for centralized governance.

Zero-trust architecture:

• Google’s BeyondCorp zero-trust model secures virtual locations without a VPN. It provides authentication for both users and devices, and authorizes access to core Google infrastructure and resources. It also allows for SSO and access control policies.
• AWS provides a similar trust architecture through AWS Verified Access, IAM Identity Center, and the Amazon VPC Lattice. While both methods achieve zero-trust, Google’s BeyondCorp is more established.

Compliance

• AWS: ~100 compliance programs; GovCloud for government workloads.
• GCP: ~80 compliance programs; Assured Workloads for regulated industries.
• AWS provides GovCloud, a highly compliant and dedicated region for sensitive data commonly used by government and defense departments. In contrast, GCP has Assured Workloads that let organizations apply security controls to achieve specific compliance requirements. The AWS Control Tower allows you to create and manage many accounts with best practices, while AWS Organizations lets you set rules to restrict some child account activities.

GCP vs AWS: Ecosystem and partner support

Both GCP and AWS have extensive ecosystems and partner support:

Partner network

• AWS’s partner program (APN) is extensive, with 12,000+ consulting partners and 25,000 marketplace products across 70+ categories.
• GCP Partner Advantage is a smaller partner ecosystem, but it’s growing quickly and aligned with open source and Google-native tools.

Community and talent

• AWS has a wide community and forums, with thousands of AWS-certified architects and developers on the job market. This is due to the extensive AWS certification programs across multiple specialty levels.
• GCP is also scaling its certification tracks, with programs ranging from foundational to professional.

GCP vs AWS: Multi-cloud and hybrid readiness

GCP requires licensing and setup effort. It offers various multi-cloud solutions to migrate and optimize applications across environments. Google Anthos, a container-based application management platform, allows users to build applications consistently across Google Cloud, on-premises, and other public cloud platforms like Azure and AWS.

AWS focuses on bringing AWS on-prem via AWS Outposts. However, it requires AWS-provided hardware, while Anthos runs on existing hardware. Anthos is cloud-neutral, while Outposts is deeply integrated with AWS.

GCP vs AWS: Complexity

Both GCP and AWS have CLI tools, APIs, and documentation to help developers build and deploy applications.

User experience and UI

• GCP’s cloud management console is intuitive and easy to use. It provides insights into your Google Cloud deployments, quotas, and infrastructure health in one place. It has a navigational menu and a unified search bar, all with a streamlined feel.
• AWS management console navigation feels denser, mostly due to the large number of services.

Command-line and in-browser shells

• GCP provides the Cloud Shell directly integrated in the console, alongside a code editor, providing a convenient way to run commands or edit config files.
• AWS offers AWS CloudShell, which is pre-authenticated with their CLIs and editors for a quick start, with similar in-browser CLI access.

CLIs and SDKs

• The AWS CLI, however, is well-documented to support each AWS service. AWS also offers feature-complete SDKs in all major programming languages, such as Python, JavaScript, Ruby, .NET, and more, making it easy to call AWS services from within your applications.
• GCP’s CLI, the gcloud CLI, is also well-designed and consistent. Both CLIs support everyday operations such as streaming logs, managing IAM policies, provisioning resources, etc.

Documentation

• GCP and AWS both have comprehensive documentation and sample code to help developers get started easily.
• AWS’s documentation is in-depth, while GCP’s is slightly easier to follow.
• AWS offers numerous how-to guides and a large, active community, and Google also provides interactive tutorials through Cloud Skills Boost (formerly Qwiklabs).

GCP vs AWS: Pricing and discounts

GCP and AWS have a pay-as-you-go pricing model, whereby you pay for the resources used.

There are no upfront costs, but the billing and discount approaches have complex pricing nuances. It’s essential to use each provider’s calculator and bill analysis tool, such as the AWS Cost Explorer, for precise estimates, as many factors contribute to actual costs.

Compute pricing

GCP GCE and AWS EC2 charge for compute by the second for many VM types. On-demand prices for a small baseline instance are low.

• GCP: e2-micro VMs (2 vCPU, 1GB RAM) cost approximately $0.008-$0.010 per hour, or $6-7 per month.
• AWS: EC2 t3.micro VMs (1 vCPU, 1GB RAM) cost about $0.0116 per hour, or around $8.70 monthly.

Storage pricing

• Google Cloud Storage Standard (US regions, e.g., us-east1): $0.020 per GB-month.
  • Higher rates for multi-region tiers.
  • Lower rates for archive tiers (Nearline/Coldline/Archive).
• AWS S3 Standard (US regions): $0.023 per GB-month for the initial 50TB/month.
  • Increased costs for multi-region storage.
  • Infrequent access and archival tiers also have reduced costs.

Networking (bandwidth) pricing

Incoming data (ingress) is free on both clouds.

Baseline data egress rates to the public internet (US regions) are:

• GCP: $0.12 per GB for the first GB
• AWS: $0.09/GB for the first 10TB/month, with reduced rates at higher volumes.

Discounts and free tiers

New GCP users receive $300 in free credits for 90 days, plus Always Free limits.

AWS offers 12 months of always-free usage (with limits)alongside always-free usage limits for many services.

GCP automatically applies Sustained Use Discounts that provide up to 30% off for running VMs. Users with planned, steady use (of 1 or 3 years) receive the Committed Use Discounts that provide up to 57% on specific amounts of vCPU/RAM usage. You can also achieve between 60-91% of Compute Engine cost savings with Spot VMs (for short-lived workloads), which can be terminated anytime.

AWS provides discounted rates in exchange for 1 or 3-year commitments, with cost savings of up to 72%, which apply to Reserved Instances and Savings Plans. AWS has Spot instances for fault-tolerant workloads, which cost 90% less than regular on-demand VMs.

When to use GCP

🠆 AI/ML & analytics

🠆 Cost-sensitive, steady workloads

🠆 Multi-cloud/hybrid

🠆 Mobile app dev

AI/ML & real-time analytics

Thanks to services such as BigQuery, Vertex AI, and Cloud Dataflow, GCP is a top choice for AI and machine learning workloads. It lets teams build data pipelines faster, including workloads like natural language processing and image recognition.

Cost-sensitive workloads with steady usage

GCP offers more straightforward pricing and automatic Sustained Use Discounts, reducing VM costs for long-term use. This makes it suitable for SaaS applications with lower VM costs. Also, GCP has a more straightforward billing system, which helps predict and manage costs.

Multi-cloud or hybrid strategy

Anthos provides consistent policy and workload management across GCP, on-premises, and AWS or Azure. Some GCP products, like BigQuery Omni, allow cross-cloud analytics without moving data, which is ideal if you want to aggregate data from multiple clouds. GCP’s alignment with other open platforms, such as TensorFlow, Docker, and Kubernetes, supports portability.

Mobile app development

GCP offers a developer-friendly environment for teams developing mobile, web, and microservice architectures. Cloud Functions enables you to run event-driven functions, while Cloud Run allows the deployment of serverless containers.

Cloud Run is suitable for web backend and REST APIs, while Firebase provides a backend platform with authentication, real-time database, and more. Additionally, you can use Google Kubernetes Engine (GKE) to run containerized workloads.

When to use AWS

🠆 Enterprise-scale services

🠆 Global low latency

🠆 Partner ecosystem

🠆 Faster dev/time-to-market

Enterprise-scale and diverse services

It has almost every solution you need to run applications. When it comes to computing, you can choose the instance types you want, store data in S3, and run containerized workloads, all on a large scale.

AWS can support some of the largest deployments, like Netflix streaming and Formula 1 real-time race analytics. Large software deployment and hosting platforms like Supabase and Vercel are built on AWS.

Global low-latency applications

AWS is highly distributed, with regions (data centers) and AZs. This architecture enables you to deploy resources closest to users. You can also deploy in multiple availability zones for fault tolerance and high availability. AWS edge services such as CloudFront, Wavelength, and Local Zones provide ultra-low latency, which is critical for real-time applications.

Deep partner integrations and marketplace software

If your architecture relies on third-party software, the AWS Partner Network provides access to thousands of developers, consultants, and solution architects at your disposal. The AWS marketplace offers thousands of software products, meaning you can deploy enterprise-grade software.

Faster time-to-market managed services

AWS offers a comprehensive suite of services to accelerate application development and ship faster. For example, you can use Amazon Cognito to handle authentication, Amazon SNS for real-time notifications, and Elemental MediaConvert for video transcoding, among many others.

Storage, backup, and archival

AWS has multiple storage tiers, enabling you to store data depending on its use case and retrieval frequency. You can use the Standard tier for frequently accessed data, or the Glacier Deep Archive for data that’s rarely accessed, with multiple tiers in between.

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How Fivetran supports GCP and AWS adoption

GCP and AWS are both solid, reputable platforms. Each has multiple specialized offerings, a global presence, and secure architecture. Regardless of your choice, you’re sure to get reliable performance, scalability, and data security.

Whether you choose AWS or GCP, Fivetran gives your team a consistent, automated way to create and manage data pipelines with minimal configuration. On GCP, Fivetran connects to BigQuery, manages databases and tables, and handles schema changes automatically.

On AWS, Fivetran connects with Amazon Redshift, provisions target schemas and tables, and performs incremental merges, enabling warehouses with minimal maintenance.

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