AWS vs. Google Cloud 2026 – Full Comparison

Amazon Web Services and Google Cloud Platform are the two most technically advanced public cloud providers in the world. AWS leads in market share; Google Cloud leads in AI/ML tooling and per-second billing. Choosing between them depends on your workload, team skills, and budget.

Quick Verdict

Feature-by-Feature Comparison

Compute: EC2 vs. Compute Engine

AWS EC2 offers the largest instance type catalogue — over 600 instance types across more than 40 families. This breadth is AWS's biggest advantage for specialized workloads. Google Compute Engine is more focused but competitive on price, offering sustained use discounts automatically without reserved instance commitments.

For general-purpose web workloads, Google's E2 instances typically cost 10–20% less than equivalent EC2 instances. For memory-optimized workloads, AWS's R-series instances have more variety. For GPU compute, both platforms are competitive, but Google Cloud's TPUs are unmatched for TensorFlow-native AI training.

Storage: S3 vs. Cloud Storage

Amazon S3 is the de facto standard for object storage and has the most integrations of any storage service. Google Cloud Storage is slightly cheaper per GB and has strong multi-region redundancy options. Both offer lifecycle management, versioning and fine-grained IAM controls.

For egress costs — the biggest hidden cost in cloud storage — AWS charges $0.09/GB for the first 10TB/month out of S3. Google Cloud charges $0.08/GB for the same. Over significant scale, this difference adds up.

Networking and CDN

Google Cloud's global network is one of its strongest differentiators. Google owns more than 1 million miles of private fiber and submarine cables. Traffic between Google Cloud regions travels on Google's backbone, not the public internet, resulting in consistently lower latency between regions compared to AWS.

AWS CloudFront has a larger edge network and more integration depth with AWS services like Lambda@Edge. For applications already deep in the AWS ecosystem, CloudFront is the natural CDN choice. For new workloads, Google Cloud CDN offers slightly better price transparency.

AI / ML: SageMaker vs. Vertex AI

This is where Google Cloud clearly wins in 2026. Vertex AI provides unified access to Google's ML infrastructure including TPU v4 and v5 pods, pre-trained foundation models via Model Garden, and seamless BigQuery ML integration. AWS SageMaker is more mature as a managed training and deployment platform, but lacks equivalent hardware acceleration for large-scale model training.

For inference at scale, both platforms are competitive. For training foundation models or fine-tuning LLMs, Google Cloud's TPU access gives it a meaningful edge.

Pricing Models

AWS pricing is complex. Reserved Instances (1-year or 3-year commitments) offer up to 72% savings vs. on-demand. Savings Plans offer similar discounts with more flexibility. Spot Instances offer the deepest discounts (up to 90%) with interruption risk.

Google Cloud pricing is simpler. Sustained Use Discounts apply automatically — no commitment required. Committed Use Discounts (1-year or 3-year) offer up to 57% savings. There are no Spot equivalent interruptions on standard preemptible VMs within a 24-hour window.

Free Tiers

AWS offers a 12-month free tier for new accounts plus an "Always Free" tier. Google Cloud offers a permanent Always Free tier with no expiry plus $300 in credits for 90 days for new accounts. For experimentation, Google Cloud's free tier is slightly more generous — 1 e2-micro instance free permanently, 5GB Cloud Storage, 1 million Cloud Functions invocations per month.

Final Verdict

AWS wins for: breadth of services, enterprise compliance, largest partner ecosystem, most managed services, AWS Lambda maturity.
Google Cloud wins for: AI/ML workloads, Kubernetes (GKE), per-second billing everywhere, network quality, BigQuery analytics, simpler pricing for standard VMs.

For most new projects in 2026, start on Google Cloud if your workloads are compute- or ML-heavy. Start on AWS if you need access to the full managed services catalogue or your team already has AWS expertise.