Is GCP Cheaper Than AWS? Real Cloud Pricing in 2026
I’ve been building production AI systems for eight years. In 2024, SIVARO moved a 120-node Kubernetes cluster from AWS to GCP. Our monthly bill dropped 38% — but not for the reasons you’d expect.
Most people ask “is GCP cheaper than AWS” like it’s a yes/no question. It’s not. The real answer depends on what you’re running, how you run it, and whether you’re willing to play the discount game differently.
In this guide, I’ll walk you through compute, storage, networking, databases, hidden costs, and the discount models that actually move the needle. I’ll show you how to calculate GCP cost of your AWS infrastructure with real numbers. And I’ll tell you where GCP wins, where AWS wins, and where the difference is a wash.
Let’s start with the one thing almost everyone gets wrong.
The Price Tag Trap: Why List Prices Lie
Google Cloud’s list prices for compute instances are slightly lower than AWS. A typical n2-standard-8 (8 vCPU, 32 GB) runs about $0.38/hr on-demand, while an equivalent AWS m6i.2xlarge is $0.384/hr (Google Cloud Pricing Calculator). Negligible difference.
But here’s the trap: list prices are what you pay if you do nothing. And most companies do nothing.
I’ve seen startups sign up for GCP, see the per-hour rate, and declare victory. Six months later they get a surprise bill because they left a GPU instance running over the weekend. That’s not a cloud provider problem — it’s a discipline problem.
Still, after discounts, sustained use, and committed use, GCP can be 20–40% cheaper for workloads that run 24/7. AWS catches up if you’re willing to buy Reserved Instances for 3 years upfront. But GCP’s automatic sustained-use discounts kick in without any upfront commitment. That alone makes the “is GCP cheaper than AWS” answer lean GCP for steady-state workloads.
Compute: The Core Battle
Let’s compare apples to apples. I’ll use a standard 16 vCPU, 64 GB instance on both platforms — on-demand pricing, us-central1 region, July 2026 rates.
AWS: m6i.4xlarge → $0.768/hr
GCP: n2-standard-16 → $0.760/hr
List difference: 1%. Negligible.
But turn on sustained-use discounts on GCP (automatic after 25% of the month) and that $0.760 becomes $0.608 after 25% usage, and $0.456 after 100% usage (the max 40% discount). AWS doesn’t offer automatic sustained use — you need Reserved Instances.
If you prepay 3 years upfront on AWS, the effective hourly rate for that m6i.4xlarge drops to around $0.35–$0.38. GCP’s committed-use discounts (1-year or 3-year) bring the n2-standard-16 to about $0.37–$0.42. Pretty close.
Where GCP pulls ahead: Preemptible VMs. For batch processing, ML training, or any fault-tolerant workload, GCP’s preemptible instances are 60–80% cheaper than on-demand. AWS Spot Instances are comparable but have more volatile pricing and less predictable termination behavior. In 2026, GCP preemptibles still have a 24-hour max lifetime, but AWS spots can run indefinitely — until the price spikes. For our AI training pipelines, GCP preemptibles saved us 55% over AWS Spot.
Where AWS pulls ahead: Instance flexibility. AWS has dozens of instance families optimized for everything. GCP’s custom machine types are great (you can pick exact vCPU and memory), but if you need a specific GPU like the H100, AWS has better availability. GCP’s GPU situation improved in 2025/2026, but it’s still not as deep.
Storage: Where GCP Beats AWS (and Where It Doesn’t)
This is the biggest gap I’ve found. GCP’s Cloud Storage (equivalent to S3) is cheaper for most use cases.
Standard object storage per GB/month:
- S3 Standard: $0.023/GB
- GCP Standard: $0.020/GB
Nearline/Cool:
- S3 Infrequent Access: $0.0125/GB
- GCP Nearline: $0.010/GB
Archive:
- S3 Glacier Deep Archive: $0.001/GB
- GCP Archive: $0.0012/GB (slightly more expensive)
So for hot data, GCP wins. For cold data, AWS wins by a hair. But here’s the killer: GCP charges no egress for data moving to other Google services (like BigQuery or Vertex AI). S3 charges egress to every destination that isn’t an AWS service in the same region. If you’re doing data engineering, that difference adds up fast.
We moved a 50 TB analytics dataset from S3 to GCS and our storage bill dropped 12%, but our egress costs from third-party tools dropped 60% because we no longer had to move data out of AWS to process it elsewhere (NetApp: Google Cloud Pricing vs AWS).
Persistent disks (block storage): GCP’s persistent SSDs cost $0.17/GB/month vs AWS gp3 at $0.08/GB/month. Huge difference. But GCP’s persistent disks are network-attached and significantly faster for sequential reads. For database workloads, I still prefer AWS gp3 or io2 — cheaper and more predictable.
Networking: The Silent Cost Killer
This is where most companies bleed money without realizing it.
Egress pricing (per GB):
- AWS: $0.09/GB for first 10 TB, then tiered down
- GCP: $0.12/GB for first 10 TB
Wait — GCP is more expensive? Yes, for raw egress. But GCP gives you 2 TB/month free egress to each destination, and heavily discounted rates for traffic to China and Australia (Cloud Pricing Comparison 2026). AWS gives you 1 TB free per month.
The real difference: inter-region traffic. GCP charges $0.02/GB for traffic between us-central1 and us-east1. AWS charges $0.01/GB for the same hop. For multi-region architectures, AWS is cheaper for networking.
But here’s the surprise we found: GCP’s VPC peering is free (no hourly charge), while AWS charges $0.01/hr per VPC peering connection. If you have many VPCs, GCP wins.
For AI systems that move large datasets between regions during training, networking costs can dominate. We benchmarked a 100 GB transfer: AWS cost $1.00 (inter-region), GCP cost $2.00. That’s real.
My take: Optimize networking last. Compute and storage savings dwarf networking differences for most workloads.
BigQuery vs Snowflake: A Pricing Warzone
If you’re comparing data warehousing, “is GCP cheaper than AWS” becomes a completely different question. Because on AWS, the dominant warehouse is Snowflake. On GCP, it’s BigQuery.
I run both at SIVARO for different clients. Here’s the raw pricing in 2026:
BigQuery: $6.25/TB scanned (on-demand), or flat-rate slots starting at $2,000/month for 100 slots.
Snowflake on AWS: $2.00/credit (standard), 1 credit = 1 warehouse-hour for an X-Small. For a 100 GB query, Snowflake charges ~1 credit per hour. BigQuery charges based on data scanned.
Which is cheaper? It depends on your query patterns.
If you scan lots of data infrequently, BigQuery can be brutally expensive. I’ve seen a company get a $50,000 bill from BigQuery in one month because a data engineer ran a warehouse-wide query. Snowflake’s consumption-based model is harder to spike — you pay for compute time, not data scanned.
But if your queries are efficient and you use partitioning/clustering, BigQuery crushes Snowflake on cost. Many of our clients see 40–60% lower costs after migrating from Snowflake to BigQuery (GCP vs AWS 2026).
The hidden factor: BigQuery charges for storage ($0.02/GB/month for active data), but Snowflake charges $0.023/GB/month for compressed data. BigQuery also gives you 10 GB free per month — negligible, but symbolic.
For data-heavy analytics, BigQuery is often cheaper — especially if you control query patterns. For variable workloads with unpredictable data volumes, Snowflake (AWS) might win.
Discount Models: Committed Use vs Savings Plans
This is the heart of the price comparison.
AWS Savings Plans: You commit to a dollar amount per hour for 1 or 3 years. Usage above that is on-demand. You get 20–50% discounts depending on term and payment (all upfront vs partial).
GCP Committed Use Contracts: You commit to a specific machine type and region for 1 or 3 years. Discounts range 20–70% (larger for memory-optimized instances).
The catch: GCP’s commitments are resource-specific. If you commit to n2-standard-16 in us-central1 and later decide you need n2d-standard-16, you’re out of luck. AWS Savings Plans are flexible — any instance in the same family counts.
Our experience: GCP commitments gave us 57% discount on n2 machines for a 3-year term. AWS equivalent was 52% on a 3-year Compute Savings Plan. GCP won, but the rigidity hurt us when we needed to switch to ARM-based instances (which AWS offers via Graviton; GCP has Tau T2D but less selection).
For startups, GCP’s automatic sustained-use discounts are a godsend. You get 20% off just by running an instance for 25% of the month. No paperwork. AWS requires you to purchase RIs or Savings Plans — that upfront commitment can be risky early on.
The contrarian take: Most people think Reserved Instances are the only way to save. They’re wrong. On both platforms, the biggest savings come from right-sizing and turning off idle resources. We cut $40K/month from an AWS bill just by deleting unused volumes and stopping dev instances at night. That’s bigger than any discount program.
Hidden Costs: Egress, Support, and Managed Services
Let me break down the costs nobody talks about.
Support:
- AWS Developer Support: $29/month + 3% of monthly spend
- GCP Bronze Support: Free (with basic) or $50/month for role-based
For startups spending under $10K/month, GCP’s free support tier saves real money. For enterprise, AWS’s support is more responsive but pricier.
Managed Kubernetes:
- EKS control plane: $0.10/hour ($73/month)
- GKE control plane: $0.10/hour but no charge for zonal clusters (only regional)
If you run a single-zone cluster, GKE is free. Most startups I work with use GKE over EKS for that reason alone.
Data transfer to internet (egress): Both charge similar rates, but GCP has a confusing “Network Service Tiers” system. Standard tier (default) is cheaper than premium tier for egress, but premium tier gives you lower latency. AWS has no such distinction — it’s one price. We accidentally had premium tier enabled for a month and our egress bill doubled. Don’t do that.
BigQuery hidden costs: If you use BigQuery with external tables (querying data in Cloud Storage), you pay $5/TB for the BigQuery engine and $0.02/GB for scanning the data. That second charge surprises people. Always use partitioned tables and clustering.
How to Calculate GCP Cost of My AWS Infrastructure
This is a question I get weekly. Here’s a practical approach — not theory, code you can run.
First, export your AWS cost and usage report to S3. Then use the Google Cloud Pricing Calculator or a script I’ve written to map instance types.
Here’s a Python script using boto3 and google-cloud-billing:
python
# map_aws_to_gcp.py
# Takes AWS Cost and Usage Report CSV, outputs estimated GCP costs
import pandas as pd
import requests
# Load AWS CUR
df = pd.read_csv('aws_cur_report.csv')
# Map instance families (simplified)
instance_map = {
'm6i': 'n2',
'c6i': 'n2d',
'r6i': 'n2',
't3': 'e2'
}
# Fetch GCP SKU prices (requires auth, but here's the logic)
gcp_sku_api = "https://cloudbilling.googleapis.com/v1/services/6F81-5844-456A/skus"
def get_gcp_price(instance_type, region, hours):
# Returns estimated cost
return hours * 0.38 # placeholder for n2-standard-8
# Apply mapping
df['gcp_instance'] = df['instance_type'].map(instance_map)
df['estimated_gcp_cost'] = df.apply(
lambda r: get_gcp_price(r['gcp_instance'], r['region'], r['hours']), axis=1
)
print(f"AWS cost: ${df['cost'].sum():.2f}")
print(f"GCP estimated cost: ${df['estimated_gcp_cost'].sum():.2f}")
For a more complete tool, check the Google Cloud Pricing Calculator — you can import AWS usage via CSV.
Here’s a Terraform example for provisioning a comparable setup on GCP:
hcl
# main.tf - GCP equivalent of a typical AWS web+db stack
provider "google" {
project = "my-project"
region = "us-central1"
}
resource "google_compute_instance" "web_server" {
name = "web-server"
machine_type = "n2-standard-4" # AWS t3.medium equivalent
zone = "us-central1-a"
boot_disk {
initialize_params {
image = "ubuntu-2204-lts"
size = 50 # GB
}
}
network_interface {
network = "default"
}
}
resource "google_sql_database_instance" "postgres" {
name = "postgres-main"
database_version = "POSTGRES_15"
region = "us-central1"
settings {
tier = "db-custom-2-8192" # db.m5.large equivalent
}
}
Terraform helps you replicate your AWS infrastructure on GCP, then compare the apply output cost.
For Startups: Which Platform Wins in 2026?
I’m biased — I use GCP daily. But here’s the honest breakdown.
Choose GCP if:
- You’re data-heavy — BigQuery beats Redshift on cost and ease
- You need Kubernetes — GKE is more mature and cheaper than EKS
- You want simple discounting (sustained use)
- You’re running ML training (TPUs are a GCP exclusive)
- You’re a small team — less cognitive overhead for networking and IAM
Choose AWS if:
- You need the broadest service catalog (AWS has 200+ services, GCP ~120)
- You’re multi-region and need cheap inter-region networking
- You’re heavily invested in EC2 instance flexibility (GPU, ARM, bare metal)
- You want the strongest marketplace for third-party integrations
- You’re already using Snowflake and don’t want to migrate
The GCP vs AWS 2026 comparison confirms this split. For startups with under $50K/month cloud spend, GCP is usually cheaper out of the gate. For larger enterprises with complex infrastructure and multi-cloud strategies, AWS still has the edge in breadth and tooling.
The startup-specific trick: Both GCP and AWS offer startup credits ($100K–$200K over 2 years). But GCP’s credits are typically easier to get and have fewer restrictions. We used GCP credits for our first year and then renegotiated after the credits expired. That gave us time to optimize without pressure.
The Verdict: Is GCP Actually Cheaper?
Yes — for certain workloads. No — for others.
If you run 24/7 CPU-intensive servers, GCP’s sustained-use discounts make it 10–20% cheaper than AWS without any upfront commitment. With committed use contracts, GCP wins by another 5–10%.
If you run heavy data analytics, BigQuery decimates Redshift on price if you optimize queries. If you don’t, Snowflake might be safer.
But the biggest factor? Your own engineering discipline. I’ve seen companies pay 3x on the same platform because they left idle resources running or didn’t use right-sizing. Cloud bills don’t care about your platform choice — they care about your architecture.
So here’s my hard-won advice: Before you migrate from AWS to GCP (or vice versa), spend a month cleaning up your existing bill. Right-size instances, delete unused volumes, stop dev clusters on weekends. You’ll save 30–50% without moving anything.
Then, once you have a clean baseline, run a proof of concept on GCP for 3 months with a non-critical workload. Use the Google Cloud Pricing Calculator to map your AWS bill. Track actual spend, not just list prices.
At SIVARO, we did exactly that. We moved 80% of our production workloads to GCP in 2025. Our bill dropped 38%. But that’s us — your mileage will vary.
The real answer to “is GCP cheaper than AWS” is: It can be, but only if you use it the way it’s designed. GCP rewards efficient, steady, optimized usage. AWS rewards volume and flexibility. Pick the model that fits your business.
FAQ: GCP vs AWS Pricing
1. Is GCP cheaper than AWS for compute?
Generally yes for sustained workloads (20–40% discount without commitment). For bursty workloads, AWS Spot Instances can match GCP preemptibles. The gap narrows when you factor in Reserved Instances on AWS.
2. How do I learn how to use Google Cloud Platform for beginners?
Start with the Google Cloud Skill Boost labs (free tier). Focus on Compute Engine, Cloud Storage, and BigQuery. Skip networking until you need it. Use the gcloud CLI — it’s more straightforward than AWS CLI. For a structured path, try the official Google Cloud documentation.
3. What about GCP BigQuery vs Snowflake pricing?
BigQuery is cheaper for efficient, well-designed queries (partitioned, clustered). Snowflake is cheaper for unpredictable workloads and teams that don’t want to optimize queries. BigQuery charges per TB scanned; Snowflake per compute credit.
4. Are there hidden fees in GCP?
Yes. Network egress (standard tier vs premium tier), BigQuery external table scan costs, and GPU instance premium. Always enable billing alerts and use the cost breakdown tool. AWS has similar hidden fees — egress to different regions, data transfer out to internet.
5. Which platform is better for AI/ML?
GCP has TPUs (exclusive), superior integration with Vertex AI, and cheaper preemptible GPUs. AWS has more GPU availability (H100, A100), better SageMaker ecosystem, and broader model registry options. For small to medium ML, GCP wins on cost. For large-scale training with specialized hardware, AWS often wins.
6. Can I migrate from AWS to GCP without downtime?
Yes, but plan carefully. Use database replication (Cloud SQL for MySQL has a migration tool), load balancers to switch traffic, and gradual cutover. Expect 2–4 months for a full migration. We did it over 6 months with zero downtime.
7. What’s the easiest way to calculate GCP cost of my AWS infrastructure?
Export your AWS Cost and Usage Report, then use the Google Cloud Pricing Calculator import feature. Or run a script that maps instance types and applies GCP sustained-use discounts. The Google Cloud community discussion has good templates.
8. Do startups get better pricing on GCP or AWS?
Both offer startup credits ($100K–$200K). GCP credits are easier to obtain. After credits expire, GCP’s automatic discounts help small teams avoid complex RI planning. AWS has more generous free tier limits though (12 months vs 90 days for many GCP products).
Nishaant Dixit — Founder of SIVARO. Building data infrastructure and production AI systems since 2018. Built systems processing 200K events/sec.