AWS vs Google Cloud: Platform Comparison

Overview

Amazon Web Services and Google Cloud Platform are two of the three major public cloud providers (alongside Microsoft Azure). AWS launched in 2006 and has maintained market leadership ever since. Google Cloud entered the market later but has grown rapidly, especially in data analytics, machine learning, and Kubernetes workloads.

For developers and startups choosing between them, the decision comes down to what you are building, your team's expertise, and which managed services align with your architecture.

Compute

AWS EC2 is the most mature virtual machine platform with the widest selection of instance types — from general purpose to GPU-optimized to ARM-based Graviton processors. Google Compute Engine offers similar capabilities with a simpler interface and automatic sustained-use discounts.

For containers, Google Kubernetes Engine (GKE) is widely considered the best managed Kubernetes service. Google invented Kubernetes, and it shows — GKE offers features like Autopilot mode, automatic node management, and seamless integration with Google's networking stack. AWS EKS is capable but requires more configuration.

For serverless, both platforms have strong offerings. AWS Lambda is the most widely adopted serverless compute service. Google Cloud Run offers a compelling alternative — it runs any container as a serverless service with automatic scaling, which provides more flexibility than Lambda's function-based model.

Data and Analytics

This is where Google Cloud shines. BigQuery is a serverless, petabyte-scale analytics warehouse that has no true equivalent on AWS. It separates storage and compute, offers built-in ML capabilities, and charges per-query rather than for provisioned capacity.

AWS counters with Redshift, Athena, and EMR, but these require more assembly. For real-time streaming, both platforms have strong options (Kinesis vs Pub/Sub + Dataflow), though Google's Dataflow (based on Apache Beam) is more developer-friendly.

AI and Machine Learning

Google Cloud has a clear edge in AI/ML. Vertex AI provides a unified platform for training, deploying, and managing ML models. Google's TPUs (Tensor Processing Units) offer specialized hardware for training large models. And with the Gemini API, Google provides direct access to their most capable foundation models.

AWS SageMaker is a comprehensive ML platform with good tooling, and Bedrock provides access to multiple foundation models (Claude, Llama, Titan). AWS has broader model selection through Bedrock, while Google has tighter integration with their own models and infrastructure.

Pricing

Both platforms are priced competitively, but the models differ. AWS pricing requires careful attention — Reserved Instances, Savings Plans, and Spot Instances can save 30-70% but require commitment and planning. Google Cloud automatically applies sustained-use discounts when you run instances for more than 25% of a month, which is simpler.

Google Cloud's $300 free credit for new accounts is more generous than AWS's free tier for experimentation. For production workloads, both platforms require careful cost management — unexpected bills are common on either provider.

When to Choose AWS

• You need the broadest possible service catalog
• Enterprise compliance and certification requirements are critical
• Your team already has AWS expertise
• You need hybrid cloud with on-premises integration (Outposts)
• You want the largest ecosystem of third-party tools and consultants

When to Choose Google Cloud

• Data analytics and BigQuery are central to your architecture
• You are running Kubernetes-native workloads
• AI/ML training and inference are core requirements
• You value developer experience and simpler pricing
• You are a startup that wants generous free-tier credits to get started

The Bottom Line

AWS is the safe, proven choice with the widest service breadth. Google Cloud is the best choice for data-intensive and AI-driven applications. Both are production-ready and capable of handling any scale. If your workload is heavily Kubernetes, data, or ML-focused, Google Cloud deserves serious consideration. For everything else, AWS's maturity and ecosystem are hard to beat.