The friction-first comparison: AWS has more setup steps but more docs; GCP has fewer steps but fewer docs. Pick by what the team will spend hours on.
Table of contents
- Account setup
- First deployment
- The docs
- The pricing surprise
- The support
- The long-term migration cost
- The community and resources
- The migration path between them
- FAQ
Account setup
AWS: Create account -> verify email -> add payment method -> wait for activation (sometimes minutes, sometimes hours if fraud detection flags it).
GCP: Create account -> verify email -> add payment method -> immediate activation. $300 free credit for new accounts.
GCP wins on setup friction. The team that needs to be running in 10 minutes picks GCP. The team that can wait 30 minutes picks either.
First deployment
AWS: Install AWS CLI, configure credentials, learn IAM, choose a service (EC2, ECS, Lambda, Beanstalk), read the docs, deploy.
GCP: Install gcloud CLI, authenticate, choose a service (Compute Engine, Cloud Run, App Engine, Cloud Functions), deploy.
GCP wins on deployment friction. Cloud Run in particular is the simplest deploy experience in any cloud.
The docs
AWS has more docs but they’re longer, more detailed, and harder to find the right page. The team that needs a deep understanding of a service uses AWS docs.
GCP has fewer docs but they’re tighter, more focused, and easier to follow. The team that needs to ship in 30 minutes uses GCP docs.
Both have community resources (Stack Overflow, GitHub issues, blog posts). The team that gets stuck on AWS finds more help; the team that gets stuck on GCP finds less.
The pricing surprise
Both platforms have pricing surprises. The common ones:
AWS. Data transfer out (egress) is expensive. NAT gateway is expensive. Elastic IPs are free only when attached. The team that doesn’t track these gets a surprise bill.
GCP. Egress is cheaper than AWS. The team that uses BigQuery has a per-query cost that can spike. The team that doesn’t set query limits gets a surprise bill.
The team that uses the cost calculator before deploying has fewer surprises. Both AWS and GCP have pricing calculators.
The support
AWS support tiers: Basic (free), Developer, Business, Enterprise. The paid tiers are expensive ($100-$15K/month).
GCP support tiers: Standard (free), Silver, Gold, Platinum. The paid tiers are similarly expensive.
The team that runs production workloads needs paid support. The team that’s pre-production can rely on community resources.
The long-term migration cost
The team that starts on one cloud and wants to migrate later has a real cost. Migration tools exist but they take months.
The team that picks the right platform initially avoids this cost. The team that picks based on “I might migrate later” has the worst of both worlds: they spend engineering time evaluating migrations while paying for the current platform.
The team that picks based on the right criteria (stack, team, ecosystem) is fine on either platform for years.
The community and resources
Beyond the platforms themselves, the community and learning resources differ:
- AWS. Massive community. More Stack Overflow questions, more blog posts, more YouTube tutorials. The team that learns AWS finds an answer faster.
- GCP. Smaller but growing community. Fewer Stack Overflow questions, more official documentation quality. The team that learns GCP often goes straight to the official docs.
Conference ecosystem:
- AWS re:Invent. Massive annual conference, 50K+ attendees. The right place for AWS deep-dives.
- Google Cloud Next. Large annual conference, 30K+ attendees. The right place for GCP deep-dives.
The team that wants the largest pool of learning resources picks AWS. The team that wants higher-quality official docs picks GCP.
The migration path between them
The team that needs to migrate from AWS to GCP (or vice versa):
Compute. EC2 ↔ Compute Engine. Same instance types (with different names). Migrate via Lift-and-shift or re-platform.
Database. RDS ↔ Cloud SQL. Database Migration Service (DMS) handles the replication. The right tool for most migrations.
Object storage. S3 ↔ Cloud Storage. Use the Storage Transfer Service or gsutil rsync for bulk transfers.
Networking. VPC ↔ VPC. Both are similar; the migration is mostly about IP address planning and route table updates.
The team that migrates systematically (one service at a time, with testing) has the lowest risk. The team that migrates everything at once has the highest risk.
FAQ
Which is cheaper for a startup, AWS or GCP?
Roughly comparable. The team that uses sustained-use discounts (GCP automatic) or Savings Plans (AWS commit) sees comparable pricing. The team that doesn’t optimize sees high bills on both.
Which is easier for a startup, AWS or GCP?
GCP. The console is cleaner, the CLI is simpler, and the default configurations are more sensible. The team that values ease-of-use picks GCP.
Which has more features, AWS or GCP?
AWS. The service catalog is broader, the third-party integration ecosystem is larger, and more managed services exist. The team that needs a specific niche service usually finds it on AWS first.
Which should a startup pick in 2026?
Depends on the team’s stack and the people they hire. AWS for the ecosystem and hiring pool. GCP for AI/ML and developer experience. Neither is universally right.
Is GCP faster than AWS?
For some workloads. GCP’s networking is known for high throughput and low latency between regions. AWS has more global regions but each region’s internal networking is similar.
Which is better for AI/ML?
GCP. Vertex AI is more mature than SageMaker for many use cases. GCP has TPUs (custom ML accelerators). BigQuery ML is integrated with the data warehouse. The team that does AI/ML-heavy work picks GCP.
Which is better for legacy enterprise apps?
AWS. The team that has Oracle, SAP, or other enterprise apps finds more validated deployment patterns on AWS. The team that uses Windows-heavy workloads often finds better integration on Azure.
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: