Google Cloud vs AWS is the second-tier hyperscaler comparison (after Azure). GCP wins on pricing for sustained workloads (automatic sustained-use discounts), networking (premium-tier global network), data (BigQuery), and Kubernetes (GKE). AWS wins on ecosystem (most services, biggest community, deepest third-party tooling). The team that picks GCP usually has data/ML/Kubernetes workloads. The team that picks AWS picks the default that fits most workloads.
Table of contents
Pricing
GCP’s pricing model is simpler and often cheaper for sustained workloads:
- GCP: Automatic sustained-use discounts (no commitment needed), custom VM types for better price/perf, committed-use discounts for additional savings.
- AWS: On-demand is the baseline, Savings Plans and Reserved Instances for up to 72% off but require commitment.
The team that runs VMs 24/7 on GCP pays less than the equivalent on AWS on-demand. The team that commits to AWS Reserved Instances gets close, but the management overhead is real (which instance family, which region, which term).
Networking
GCP’s premium-tier network is a differentiator:
- All inter-region traffic goes over Google’s private backbone, not the public internet.
- Lower latency between regions than AWS for most routes.
- Higher cost for premium-tier ($0.08/GB inter-region vs $0.02/GB standard).
The team that runs multi-region workloads and is willing to pay for it picks GCP for the latency. The team that stays single-region picks based on other factors.
Compute comparison
| Service | AWS | GCP |
|---|---|---|
| VMs | EC2 (400+ instance types) | Compute Engine (custom machine types) |
| Serverless | Lambda | Cloud Functions, Cloud Run |
| Containers | ECS, Fargate, EKS | Cloud Run, GKE |
| Batch | Batch | Batch |
| GPUs | P-series, G-series | A-series, G-series, TPUs |
GCP’s custom machine types (any vCPU/RAM combination) are uniquely flexible. AWS’s huge instance catalog has more specialized shapes. The team that needs exactly the right vCPU/RAM ratio picks GCP. The team that needs specific hardware (Trainium, Inferentia, DL1) picks AWS.
Data and ML
GCP wins here:
- BigQuery: Serverless data warehouse, petabyte-scale, SQL-native. The closest competitor is AWS Redshift (older, more management).
- Vertex AI: Unified ML platform, AutoML, custom training, deployment.
- TPUs: Google’s custom ML chips, the cheapest large-scale ML training option.
- Bigtable, Spanner, Firestore: Google’s globally-distributed database heritage.
The team that runs data analytics or ML at scale picks GCP. The team that runs general workloads picks AWS (or stays where they are).
Kubernetes
GKE (Google Kubernetes Engine) is the most-managed K8s service:
- Autopilot mode (provider manages nodes too).
- Auto-upgrades on by default.
- Release tracks (rapid, regular, stable) for controlled upgrades.
- Best K8s integration with the cloud network (VPC-native, native load balancers).
EKS is competitive but:
- Auto-upgrades opt-in.
- Worker nodes default to EC2 (Fargate is an option).
- IAM integration via IRSA is good but more setup than GKE’s IAM.
The team that wants the most-managed K8s picks GKE. The team that is on AWS picks EKS for cloud integration.
When to pick each
Pick GCP when:
- Data analytics is a primary workload (BigQuery is unmatched).
- ML at scale (TPUs, Vertex AI).
- Multi-region low-latency (premium network).
- Sustained workloads (auto-discounts save money).
- Kubernetes at scale (GKE is most managed).
Pick AWS when:
- Broad service catalog matters.
- Large ecosystem / hiring market.
- Specific AWS services are required (Lambda@Edge, SageMaker, Step Functions).
- Existing AWS investment.
- Compliance / FedRAMP requirements (AWS has the most compliance certifications).
FAQ
Is GCP cheaper than AWS?
Usually 5-15% cheaper for sustained workloads due to automatic sustained-use discounts. The team that uses Savings Plans on AWS gets close. The team that uses spot/preemptible gets even cheaper. For bursty workloads, the comparison depends on usage pattern.
Which has better Kubernetes?
GKE. More managed (Autopilot), better default behavior (auto-upgrade, auto-repair), deeper cloud-native networking. EKS is good but requires more setup.
Can I use both?
Yes - multi-cloud. The team that uses GCP for BigQuery + data pipeline and AWS for the rest of the stack has a common pattern. The complexity is in IAM, networking, and data transfer costs.
Which has better free tier?
GCP: $300 credit for 90 days, plus always-free tier (small VMs, Cloud Functions invocations, Cloud Storage). AWS: 12-month free tier for new accounts (smaller scope). Both are reasonable. The team that picks for free-tier alone is making a short-term decision.
What about support?
Both have paid support tiers. AWS support starts at $29/month (developer). GCP support starts at $29/month as well. Enterprise tiers are 5-figure annual contracts. The team that runs production needs the higher tier.
If you are sizing the infrastructure for the kind of project this post covers, the RunxBuild hosting calculator is the right place to model the line items. The compute, the memory, the storage, the bandwidth, the database - each one is a separate number, and the team’s mental model for the platform is the sum of those numbers. The RunxBuild dashboard is where the team sees the actual usage in one place.
Useful related references: