Is GCP Better Than AWS? A Practitioner’s Guide

I spent six years at a company that ran on AWS. Then I switched a client to GCP in 2021, thinking it would be a nightmare. It wasn’t. Some things were better. Some were worse. Most people ask “is GCP better than AWS?” like it’s a yes-or-no question. It’s not.

Let me show you what I’ve actually seen.

The Short Answer (Spoiler)

GCP isn’t better than AWS. AWS isn’t better than GCP. They’re different tools for different jobs. If you’re building data infrastructure or production AI systems — the stuff I do at SIVARO — your choice matters. A lot.

Here’s what I’ve learned from shipping real systems, not reading vendor blogs.

Where GCP Absolutely Crushes AWS

Data and Analytics

Google didn’t build a cloud business and then add data tools. They built internal data tools for their own massive scale — think Search, YouTube, Maps — and then sold them as cloud services. That makes a difference.

BigQuery is the standout. I’ve run petabyte-scale analytics on both Redshift (AWS) and BigQuery (GCP). BigQuery handles it without manual tuning. No distribution keys. No sort keys. No vacuum operations. It just works.

Here’s a real example. In 2022, we moved a 4TB dataset from Redshift to BigQuery. The Redshift setup required a cluster of 8 nodes, cost $4,000/month, and needed weekly maintenance. BigQuery cost $1,200/month for the same workload, with zero maintenance.

The query syntax difference:

sql
-- AWS Redshift: requires explicit distribution style
CREATE TABLE user_events (
    user_id INT,
    event_time TIMESTAMP,
    event_type VARCHAR(50)
)
DISTSTYLE KEY
DISTKEY (user_id)
SORTKEY (event_time);

-- GCP BigQuery: just create and query
CREATE TABLE user_events (
    user_id INT64,
    event_time TIMESTAMP,
    event_type STRING
);

That simplicity isn’t a gimmick. It’s the result of different architectural philosophies. Redshift is a columnar database. BigQuery is a serverless data warehouse that separates compute from storage at a fundamental level. You pay for queries. Not for idle compute.

Dataflow is Google’s stream and batch processing engine, built on Apache Beam. AWS has Kinesis and Glue. I’ve used both. Dataflow handles exactly-once processing out of the box. Kinesis requires you to build that yourself in your consumer code. In production, that’s a massive difference.

Vertex AI vs. SageMaker is another clear win for GCP. SageMaker has more features. Vertex AI has better integration with the rest of GCP’s ecosystem. If you’re doing ML on GCP, your data already lives in BigQuery, your training data is in Cloud Storage, and your model goes directly into Vertex AI Prediction. No data shuffling.

Where AWS Destroys GCP

Breadth and Maturity

AWS has been public since 2006. GCP since 2008. That two-year head start isn’t the real advantage. The real advantage is that AWS spent those years listening to enterprise customers and building features they asked for.

EC2 instance types are a good example. AWS has over 400 instance types. GCP has about 80. If you need a specific GPU or a specific amount of memory or a specific networking performance, AWS probably has it. GCP might not.

RDS vs. Cloud SQL is another case. AWS RDS supports 6 database engines. GCP Cloud SQL supports 3. PostgreSQL, MySQL, and SQL Server. That’s it. If you want Oracle or MariaDB, you’re managing the VM yourself.

Managed Kafka is a painful example. AWS MSK works. GCP’s equivalent (Confluent Cloud on GCP) is a third-party add-on. In 2023, we tried to migrate a Kafka cluster to GCP. We couldn’t because GCP doesn’t offer native Kafka as a service. We stayed on AWS.

Pricing Models

AWS pricing is complicated but predictable. GCP pricing is simple but has hidden costs.

Here’s what I mean. AWS charges per hour. GCP charges per minute with a 1-minute minimum. Sounds better, right? It is, for bursty workloads. But GCP’s sustained-use discounts kick in automatically, while AWS requires you to commit to reservations to get the best prices.

The real trap with GCP: egress costs. AWS charges ~$0.09/GB for internet egress. GCP charges the same. But GCP’s network costs for inter-region traffic can be 2-3x higher than AWS. I learned this the hard way when a client’s multi-region deployment cost 40% more than expected.

Networking: The Elephant in the Room

GCP’s network is objectively faster than AWS. Period. Google owns its own fiber. Amazon rents from third-party providers.

I benchmarked this in 2023. A single VM on GCP (n2-standard-4) could push 32 Gbps of network throughput. The equivalent instance on AWS (m5.xlarge) could push 10 Gbps. That’s 3.2x difference.

For data-heavy workloads — training ML models, moving large datasets, real-time streaming — this matters. A lot.

But there’s a catch. GCP’s VPC (Virtual Private Cloud) model is more restrictive. AWS lets you create multiple VPCs per region with overlapping CIDR ranges. GCP doesn’t. You get one VPC per project, and you can’t have overlapping subnets. If you’re doing complex network topologies, this is a real pain.

Here’s how to set up a basic VPC on both:

hcl
# AWS Terraform: flexible but verbose
resource "aws_vpc" "main" {
  cidr_block       = "10.0.0.0/16"
  enable_dns_hostnames = true
  tags = {
    Name = "main"
  }
}

resource "aws_subnet" "public" {
  vpc_id     = aws_vpc.main.id
  cidr_block = "10.0.1.0/24"
  map_public_ip_on_launch = true
}

hcl
# GCP Terraform: simpler but less flexible
resource "google_compute_network" "main" {
  name                    = "main"
  auto_create_subnetworks = false
}

resource "google_compute_subnetwork" "public" {
  name          = "public"
  network       = google_compute_network.main.id
  region        = "us-central1"
  ip_cidr_range = "10.0.1.0/24"
}

Notice GCP requires you to specify a region at the subnet level. AWS doesn’t. That’s a design choice with consequences.

Pricing: The Real Math

Most people think GCP is cheaper than AWS. Sometimes it is. Often it isn’t.

I ran a comparison for a client in 2023. They had a typical setup: 10 web servers (2 vCPU, 8GB RAM), 2 database servers (8 vCPU, 32GB RAM), and 500GB of object storage.

Monthly cost on AWS (us-east-1, on-demand):

10 x t3.large: $705.60
2 x r6g.xlarge: $243.20
500GB S3: $11.50
Total: ~$960

Monthly cost on GCP (us-central1, on-demand):

10 x e2-standard-2: $680.40
2 x n2-standard-8: $259.20
500GB Cloud Storage: $10.25
Total: ~$950

Almost identical. But the GCP setup used a different approach to compute — Google’s e2 series is designed for cost optimization, with burstable CPU. If you need consistent performance, you’d use n2 instances, which cost more.

The real savings with GCP come from committed use discounts. If you commit to 1 year, you get 20-30% off. AWS has reserved instances, but they’re less flexible.

My conclusion: AWS is cheaper for variable workloads. GCP is cheaper for stable, predictable workloads.

Managed Kubernetes: GCP’s Secret Weapon

GKE (Google Kubernetes Engine) is the best managed Kubernetes service on any cloud. I’ll die on this hill.

I’ve run production clusters on EKS (AWS), AKS (Azure), and GKE. GKE is faster to set up, has better autoscaling, and handles upgrades without downtime.

Here’s a real example from 2022. We needed to scale a Kubernetes cluster to handle a 10x traffic spike. On EKS, this took 12 minutes because the node group had to launch new EC2 instances. On GKE with cluster autoscaler, it took 4 minutes. The difference? GKE uses preemptible VMs by default and has better horizontal pod autoscaling configurations.

yaml
# GKE autoscaling: simple and effective
apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
  name: web-app
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: web-app
  minReplicas: 3
  maxReplicas: 100
  metrics:
  - type: Resource
    resource:
      name: cpu
      target:
        type: Utilization
        averageUtilization: 70

AWS EKS requires you to configure a Cluster Autoscaler or Karpenter separately. GKE includes this functionality out of the box.

But here’s the trade-off I don’t see people talking about: GKE’s default configurations are aggressive. They optimize for cost first, reliability second. If you don’t tweak the settings, you’ll get preempted nodes crashing your workload at the worst possible moment.

I learned this when a client’s production batch job failed because all nodes were preempted simultaneously. We spent a day debugging. The fix: use node pools with a mix of on-demand and preemptible nodes, with pod disruption budgets.

Production AI: Where Theory Meets Reality

We build production AI systems at SIVARO. I’ve seen both clouds handle ML workloads. Here’s the honest truth:

GCP is better for data engineering and training. BigQuery + Vertex AI + Cloud Storage creates a pipeline that’s hard to beat. Your training data stays in BigQuery, you export it to Cloud Storage as Parquet, and Vertex AI trains the model. No ETL. No data movement.

AWS is better for inference serving. SageMaker endpoints are more mature than Vertex AI Prediction. They handle spikes better, scale down faster, and integrate with more monitoring tools.

I tested this in 2024. A BERT model running on SageMaker handled 5000 requests/second with p99 latency of 150ms. The same model on Vertex AI Prediction handled 4000 requests/second with p99 latency of 180ms. That’s a 25% difference.

Why? AWS has invested more in inference optimization. They have Inferentia chips, better load balancers, and more experience with high-throughput serving.

When to Pick GCP

You should pick GCP if:

Your workload is data-heavy. BigQuery, Dataflow, and Bigtable are best-in-class.
You’re running Kubernetes. GKE is the gold standard.
You need high network throughput. GCP’s fiber network delivers.
You’re building on Google’s ecosystem. If you use Google Analytics, YouTube, or any Google consumer product, GCP integrates naturally.

When to Pick AWS

You should pick AWS if:

You need breadth of services. AWS has everything, even if some services are mediocre.
You’re doing inference serving at scale. SageMaker is better than Vertex AI for this.
You have complex networking requirements. AWS VPC is more flexible.
You’re running Oracle, MariaDB, or any non-mainstream database. GCP doesn’t support them natively.

The Contrarian Take

Most people think GCP is better for startups and AWS is better for enterprises. I think the opposite.

GCP’s simplicity benefits large organizations with complex compliance requirements. You have fewer services to audit, fewer configurations to secure, fewer things that can break. AWS’s breadth benefits startups because you have more options to experiment with.

I’ve seen enterprises spend months on AWS trying to figure out which of 15 different storage services to use. On GCP, you have 3. That’s a feature, not a bug.

FAQ: Is GCP Better Than AWS?

Is GCP better than AWS for small businesses?

Depends. If you’re building a data-driven product, yes. GCP’s managed services reduce operational overhead. If you need something like a simple web app, AWS’s smaller instance sizes and wider ecosystem might be cheaper.

Is GCP better than AWS for machine learning?

For training? Yes. GCP’s data pipeline is smoother. For inference? No. AWS SageMaker handles production serving better.

Which has better uptime?

Both claim 99.99% for compute. In practice, both have outages. In 2023, AWS had a major outage in us-east-1 that lasted 4 hours. GCP had a similar outage in europe-west1. Neither is perfect.

Is GCP cheaper than AWS?

For stable, predictable workloads with committed use discounts, yes. For variable workloads paying on-demand, no. Run your own benchmarks. Don’t trust vendor calculators.

Which has better support?

AWS’s support is more responsive for critical issues. GCP’s support is more knowledgeable for complex problems. I’ve had better experiences with AWS for “my production system is down” calls and better experiences with GCP for “how do I design this architecture” consultations.

Can I use both?

Yes. Most large companies do. My advice: pick one for your primary infrastructure and use the other for specialized workloads. Don’t try to be multi-cloud for everything. That’s a management nightmare.

The Bottom Line

Is GCP better than AWS? No. Is AWS better than GCP? No.

They’re different tools for different jobs. GCP is better for data infrastructure, managed Kubernetes, and high-throughput networking. AWS is better for breadth of services, mature inference pipelines, and complex networking.

Pick the one that fits your workload. Don’t pick based on brand loyalty or pricing calculators. Pick based on what you’re actually building.

At SIVARO, we default to GCP for data-heavy projects and AWS for inference-heavy projects. We don’t fight the tools. We use them where they’re best.

That’s the honest answer to “is GCP better than AWS?” — it’s not about which cloud is better. It’s about which cloud is better for you.

Nishaant Dixit — Founder of SIVARO. Building data infrastructure and production AI systems since 2018. Built systems processing 200K events/sec.