AWS vs Google Cloud for startups comes down to: AWS has more services and ecosystem, GCP has better pricing and AI/ML. The right pick depends on the team’s stack and the people they hire.
Table of contents
- The startup credit programs
- Compute pricing
- The service catalog
- The developer experience
- The hiring market
- The lock-in question
- The decision
- The detailed cost comparison
- The ecosystem comparison
- FAQ
The startup credit programs
Both offer startup credits:
- AWS Activate. Up to $100K in credits for qualifying startups (through accelerators like YC, Techstars).
- Google Cloud for Startups. Up to $200K in credits for qualifying startups.
The team that picks the platform with the larger credit package (if they’re eligible) saves money up front. Both programs require application through an accelerator or VC.
The credit is nice but should not be the deciding factor. The team that picks a platform based on credits alone is stuck on that platform when the credits run out.
Compute pricing
The pricing is roughly comparable for similar instances. The differences:
- AWS. Per-second billing, more instance types, more regions.
- GCP. Sustained-use discounts (automatic, no commitment), per-second billing, fewer instance types.
The team that runs steady-state workloads benefits from GCP’s automatic sustained-use discounts. The team that needs a specific instance type (GPU, memory-optimized) usually finds it on AWS first.
For a 2-vCPU, 8 GB instance running 24/7:
- AWS t3.large. ~$60/month.
- GCP e2-standard-2. ~$50/month with sustained-use discount.
The difference is small at this scale but grows with the fleet.
The service catalog
AWS has more services (~200 vs GCP’s ~100). The ones that matter for most startups:
- Compute. Both have similar offerings (EC2 vs Compute Engine, Lambda vs Cloud Functions).
- Database. Both have managed Postgres (RDS vs Cloud SQL), managed Redis (ElastiCache vs Memorystore), managed MongoDB (DocumentDB vs MongoDB Atlas).
- AI/ML. GCP wins here. Vertex AI is more mature than SageMaker for many use cases. GCP has TPUs for ML training.
- Serverless. Both have similar offerings.
- Networking. Comparable.
The team that picks based on services usually picks AWS for breadth and GCP for AI/ML.
The developer experience
GCP generally has a cleaner console and CLI. AWS has more features but more complexity.
AWS CLI. Comprehensive, well-documented, more options than you can use.
gcloud CLI. Cleaner, fewer options, easier to learn.
The team that values developer experience picks GCP. The team that values ecosystem and third-party integrations picks AWS.
The team that uses Terraform or Pulumi has the same experience on both platforms (infrastructure as code abstracts the cloud-specific bits).
The hiring market
AWS skills are more common in the job market. Most cloud engineers have AWS experience; fewer have GCP experience.
The team that hires cloud engineers has an easier time hiring for AWS. The team that uses GCP has to train or hire more selectively.
The team that picks AWS for hiring reasons is making a reasonable choice. The team that picks GCP for AI/ML reasons is making a reasonable choice. Neither is universally right.
The lock-in question
Both platforms have lock-in, but at different layers:
- AWS lock-in. Lambda, DynamoDB, SQS, SNS, Kinesis. The team that uses these is deeply locked in.
- GCP lock-in. BigQuery, Cloud Spanner, Vertex AI. The team that uses BigQuery is locked in to GCP for analytics.
The team that wants to minimize lock-in uses compute (EC2/Compute Engine), managed databases (RDS/Cloud SQL), and standard object storage (S3/Cloud Storage). The team that uses proprietary services has chosen lock-in by design.
The decision
The right pick:
- Pick AWS if: the team is building a typical SaaS app, the team hires AWS-experienced engineers, the team needs the broadest service catalog.
- Pick GCP if: the team is building AI/ML-heavy products, the team values developer experience, the team has predictable steady-state workloads.
- Pick both if: the team has a specific reason (BigQuery + AWS compute, GCP ML + AWS infrastructure). Be ready for the operational overhead.
The detailed cost comparison
A more detailed cost comparison for common workloads:
Web app (2 vCPU, 4 GB RAM, 100 GB SSD, 1 TB egress):
- AWS: $50/month compute + $90/month egress = $140/month
- GCP: $35/month compute (with sustained-use) + $50/month egress = $85/month
Database (4 vCPU, 16 GB RAM, 500 GB SSD, no egress):
- AWS RDS: $250/month
- GCP Cloud SQL: $200/month
Object storage (1 TB, 10M requests):
- AWS S3: $23/month + $0.40 per million requests
- GCP Cloud Storage: $20/month + $0.05 per 10,000 requests (cheaper at scale)
Container orchestration (10 nodes, mixed sizes):
- AWS EKS: $73/month for the control plane + node costs
- GCP GKE: Free for the control plane (standard tier) + node costs
GCP is generally 20-30% cheaper for comparable workloads, primarily due to sustained-use discounts and lower egress prices.
The ecosystem comparison
Beyond raw services, the ecosystems differ:
- Third-party marketplace. AWS Marketplace has more third-party software than GCP. The team that needs a specific commercial software on the cloud usually finds it on AWS first.
- Open source integrations. Both have first-class integrations with major open source projects (Kubernetes, Terraform, Prometheus). AWS has more first-party integrations; GCP has cleaner integrations with Google open source (TensorFlow, Kubernetes).
- Partner ecosystem. AWS has more consulting partners, managed service partners, and technology partners. The team that needs outside help has more options on AWS.
The team that picks AWS has the broader ecosystem. The team that picks GCP has the cleaner integration with Google’s own tooling.
FAQ
Is AWS cheaper than GCP?
Roughly comparable. AWS has more instance types and pricing options; GCP has automatic sustained-use discounts. The team that runs steady-state workloads may find GCP slightly cheaper; the team that uses Spot or Reserved Instances may find AWS cheaper.
Is GCP easier to use than AWS?
Generally yes, for new users. The console is cleaner, the CLI is simpler, the documentation is more consistent. The team that values developer experience picks GCP. The team that needs the breadth of AWS picks AWS anyway.
Should I start with AWS or GCP?
Pick based on the team’s stack and the people you hire. Most startups pick AWS for the ecosystem and the talent pool. The team that’s heavy on AI/ML picks GCP for Vertex AI and BigQuery.
Can I use both AWS and GCP?
Yes, but the operational overhead is real. The team that starts on one platform and adds the other for a specific service (GCP BigQuery + AWS compute) has a reasonable hybrid. The team that runs the same workload on both for no reason is doing double the work.
Can I use both AWS and GCP?
Yes, but expect operational overhead. The team that uses GCP BigQuery + AWS compute has a specific reason for the hybrid. The team that uses both for the same workload has unnecessary complexity.
How do I estimate my cloud bill before deploying?
Both AWS and GCP have pricing calculators. The team that estimates before deploying has fewer surprises. Common oversights: data egress, idle resources, snapshot storage, support plans.
Should I start with a free tier?
Yes, for prototyping. AWS has 12 months free + always-free tier. GCP has $300 credit + always-free tier. Azure has 12 months free + always-free tier. The team that prototypes on the free tier and migrates to paid before launch has the lowest cost during development.
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.
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