Google Cloud vs AWS: 2025 Enterprise Cost Comparison
Choosing between Google Cloud and AWS often comes down to cost, but list prices alone are misleading. This guide compares real total cost of ownership across compute, storage, networking, databases, and managed services — including discount structures, free tiers, and the hidden costs that show up after your first year.
Every enterprise cloud decision eventually comes down to cost, and every vendor's cost comparison tells a story that happens to favor that vendor. Google's whitepapers show GCP as 40% cheaper. AWS case studies show customers saving 60% by moving to AWS. Both are technically true in the scenarios they've cherry-picked.
This article tries to give you an honest, nuanced comparison across the services that actually drive the majority of enterprise cloud spend. We'll cover compute, storage, networking, databases, and managed services — including discount structures, free tiers, and the hidden costs that don't show up in list price comparisons.
Fair warning: neither cloud is definitively cheaper across the board. The right answer depends heavily on your workload profile, your negotiating leverage, and your team's existing expertise.
Compute: EC2 vs Compute Engine
Compute is typically the largest line item in enterprise cloud bills, so let's start here.
On-Demand Pricing Comparison
Comparing equivalent instances (4 vCPU, 16 GB RAM) in a major European region:
| Service | Instance Type | On-Demand ($/hour) | Monthly (~730h) |
|---|---|---|---|
| AWS | m6i.xlarge | $0.196 | $143 |
| AWS | m7g.xlarge (Graviton) | $0.163 | $119 |
| GCP | n2-standard-4 | $0.194 | $142 |
| GCP | t2a-standard-4 (ARM) | $0.155 | $113 |
At list prices, compute is roughly equivalent. GCP is 1-2% cheaper on comparable Intel instances; both providers offer meaningful savings on ARM-based instances (Graviton for AWS, T2A for GCP).
Discounts: Where It Gets Interesting
This is where GCP historically has had a genuine advantage.
GCP Sustained Use Discounts (SUDs): Automatically applied when you run an instance for more than 25% of a month. By 30 days of continuous usage, you've received a 30% discount with zero commitment. This is unique to GCP — AWS has no equivalent automatic discount.
GCP SUD schedule for N2 instances:
0-25% of month: 100% of list price
25-50% of month: ~80% of list price
50-75% of month: ~60% of list price
75-100% of month: ~40% of list price
Effective monthly: ~70% of list price (30% discount)
AWS Reserved Instances/Savings Plans vs. GCP Committed Use Discounts (CUDs):
| Feature | AWS | GCP |
|---|---|---|
| 1-year discount | 40% (no upfront, Compute Savings Plan) | 37% (resource CUD) |
| 3-year discount | 66% (all upfront, EC2 Reserved) | 55% (resource CUD) |
| Flexibility | Savings Plans flexible across instance types | Resource CUDs tied to machine family |
| Commitment unit | Dollar amount or vCPU/memory | vCPU and GB of memory |
| SUD stacking | N/A | CUDs apply on top of SUDs |
The CUD + SUD combination on GCP can yield effective discounts of 55-70% on compute for long-running workloads — competitive with or better than AWS 3-year Reserved Instances, without the multi-year commitment.
Preemptible vs Spot Instances
Both clouds offer deeply discounted compute for fault-tolerant workloads:
| Feature | AWS Spot | GCP Spot/Preemptible |
|---|---|---|
| Discount | 60-90% | 60-91% |
| Interruption notice | 2 minutes | 30 seconds |
| Max runtime | No limit | 24 hours (Preemptible) / No limit (Spot) |
| Price variability | Variable (market) | Fixed (published) |
GCP's fixed spot pricing is a meaningful operational advantage — you don't need to deal with spot pricing fluctuations or bid management. AWS Spot has broader instance type availability in some regions.
Storage Costs
Object Storage: GCS vs S3
Google Cloud Storage (Standard, us-central1):
- Storage: $0.020/GB/month
- Class A ops (writes): $0.05 per 10,000
- Class B ops (reads): $0.004 per 10,000
AWS S3 (Standard, us-east-1):
- Storage: $0.023/GB/month
- PUT/COPY/POST/LIST: $0.005 per 1,000
- GET/SELECT: $0.0004 per 1,000
GCS is slightly cheaper on storage ($0.02 vs $0.023/GB) and significantly cheaper on read operations. S3 is cheaper on write-heavy workloads when factoring in operation pricing differences.
Both offer tiered storage classes:
- Hot → Cool (S3 Standard-IA / GCS Nearline): ~50% cheaper, minimum 30-day storage
- Archive (Glacier / GCS Coldline/Archive): 80-90% cheaper, retrieval costs apply
Block Storage: PD vs EBS
| Feature | GCP Persistent Disk (SSD) | AWS EBS (gp3) |
|---|---|---|
| Price (us-central1 / us-east-1) | $0.17/GB/month | $0.08/GB/month |
| Baseline IOPS | 3 IOPS/GB | 3,000 (provisioned) |
| Provisioned IOPS | $0.17/provisioned IOPS/month | $0.005/IOPS/month |
| Snapshots | $0.026/GB/month | $0.05/GB/month |
EBS gp3 is notably cheaper on raw GB pricing. GCP PD is competitive once you factor in IOPS needs and the fact that GCP performance scales linearly with volume size up to 30,000 IOPS with no extra charge.
Networking Costs
Egress costs are where cloud bills often hide surprising charges. Both AWS and GCP charge for data leaving the cloud, though the structures differ.
Internet Egress
GCP Internet Egress (US):
0 - 1 TB/month: $0.12/GB
1 - 10 TB/month: $0.11/GB
10+ TB/month: $0.08/GB
AWS Internet Egress (US):
0 - 1 GB/month: Free
1 GB - 10 TB/month: $0.09/GB
10 - 50 TB/month: $0.085/GB
50 - 150 TB/month: $0.07/GB
AWS is slightly cheaper at scale (>10 TB/month). GCP is cheaper at small volumes due to no free tier limit but competitive pricing below 10 TB. At very high egress volumes, negotiated rates with both providers bring costs down significantly.
Inter-Region and Cross-Zone Traffic
This is where costs are easy to miss:
| Traffic Type | AWS | GCP |
|---|---|---|
| Same region, different AZ/zone | $0.01/GB each way | $0.01/GB each way |
| Cross-region (within US) | $0.02/GB | $0.01/GB |
| Cross-continent | $0.02-0.09/GB | $0.08/GB |
| VPN egress | $0.05/GB | $0.05/GB |
GCP charges less for cross-region traffic within the US (half the AWS rate). For applications that distribute data across multiple regions, this can be a meaningful difference.
Database Costs
Managed Relational Databases
PostgreSQL comparison (4 vCPU, 26 GB RAM, HA, 100 GB storage):
| Service | Instance | HA | Storage | Monthly Estimate |
|---|---|---|---|---|
| AWS RDS PostgreSQL | db.m6g.xlarge | Multi-AZ | gp3 100GB | ~$450 |
| GCP Cloud SQL | db-custom-4-26624 | HA | SSD 100GB | ~$480 |
| AWS Aurora PostgreSQL | db.r6g.large equivalent | Multi-AZ | 100GB | ~$380 |
| GCP AlloyDB | 4 vCPU | HA | 100GB | ~$420 |
Pricing is comparable for equivalent managed PostgreSQL. Aurora has a meaningful price advantage for read-heavy workloads due to its storage efficiency model. AlloyDB is GCP's answer to Aurora, offering PostgreSQL compatibility with significantly higher performance.
BigQuery vs Redshift vs Snowflake
Data warehousing is one area where GCP has a genuine structural cost advantage at scale:
BigQuery on-demand pricing:
- Storage: $0.02/GB/month (active), $0.01/GB/month (long-term)
- Query: $6.25/TB scanned
- No charge for queries that return cached results
Redshift pricing (ra3.xlplus, 4 vCPU, 32 GB RAM):
- Instance: $1.086/hour = ~$790/month
- Serverless: $0.375/RPU hour + storage
BigQuery's on-demand model costs nothing when you're not running queries. For teams that run analytical queries daily but not continuously, BigQuery is almost always cheaper than Redshift. Redshift's provisioned model wins when you have sustained 24/7 query workloads, as a fully utilized ra3 cluster can be cheaper than BigQuery at very high query volumes.
For a detailed comparison across all three data warehouses, see our BigQuery vs Redshift vs Snowflake comparison.
Managed Services Comparison
Kubernetes: EKS vs GKE
| Feature | AWS EKS | GCP GKE |
|---|---|---|
| Control plane | $0.10/hour ($73/month) | Free (Standard), $0.10/hour (Autopilot compute only) |
| Node types | EC2 | Compute Engine |
| Managed node groups | Yes | Yes |
| Auto-provisioning | Karpenter (CNCF) | Node Auto-Provisioning (built-in) |
GKE Standard's free control plane is a meaningful advantage for organizations running multiple clusters. EKS charges $0.10/hour per cluster regardless of workload — $73/month per cluster adds up across a large cluster fleet.
Serverless Functions
| Feature | AWS Lambda | GCP Cloud Functions / Cloud Run |
|---|---|---|
| Requests | $0.20 per 1M | $0.40 per 1M (functions) / $0 per request (Cloud Run with min instances) |
| Compute (GB-seconds) | $0.0000166667 | $0.0000025 (Cloud Run CPU) |
| Free tier | 1M requests, 400,000 GB-seconds/month | 2M requests/month (Cloud Functions) |
GCP's Cloud Run model (charge for CPU/memory allocation during request processing) can be significantly cheaper than Lambda for CPU-intensive workloads. Lambda's pricing model doesn't distinguish by CPU utilization.
Hidden Costs and Gotchas
AWS Hidden Costs
- NAT Gateway: $0.045/hour + $0.045/GB processed — adds up quickly for VPC-based workloads making external API calls
- CloudWatch Logs Ingestion: $0.50/GB — application logging can become expensive at scale
- Data Transfer between services: Cross-AZ transfers within the same region ($0.01/GB each direction) are easy to overlook
- ELB idle charges: Classic/ALB charge even when idle
GCP Hidden Costs
- Interconnect port fees: Cloud Interconnect has both partner and direct connection fees beyond traffic charges
- GKE enterprise features: Dataplane V2, Workload Identity, and Binary Authorization are included, but GKE Enterprise tier (formerly Anthos) adds licensing costs
- Vertex AI endpoint idle charges: Dedicated endpoints charge for deployment hours even when no predictions are made
Making the Decision
Neither cloud is universally cheaper. Based on workload profiles:
Choose GCP if you:
- Run BigQuery analytics heavily (clear GCP advantage)
- Have sustained compute workloads that benefit from SUD + CUD stacking
- Need free GKE control planes across many clusters
- Are building ML/AI workloads on Vertex AI or using Gemini API
Choose AWS if you:
- Have existing AWS expertise and tooling investment
- Need the broadest global region coverage
- Are using AWS-native services (Aurora, SageMaker, Bedrock) with significant optimization
- Have existing Reserved Instance / Savings Plan commitments
Consider multi-cloud if you:
- Need specific capabilities only available on each platform (e.g., BigQuery + AWS Marketplace software)
- Have regulatory requirements that mandate geographic redundancy across providers
- Are willing to invest in the operational complexity
For GCP-specific cost optimization strategies beyond raw pricing, see our Google Cloud FinOps guide and GCP rightsizing guide.