AWS vs GCP for startups: AWS wins on ecosystem depth (most services, most third-party tools, most engineers), startup credits ($100k Activate), and broadest VC integration. GCP wins on data (BigQuery), ML (Vertex AI, TPUs), simpler pricing (auto-discounts), and Kubernetes (GKE Autopilot). The team that picks based on workload fit and team experience has the right answer; the team that picks based on credits alone often migrates within 2 years.
Table of contents
Service breadth
AWS: ~200+ services. The widest catalog. If a service exists, AWS probably has it.
GCP: ~100+ services. Smaller catalog but covers all common needs.
For common workloads (web apps, APIs, databases, K8s), both have you covered. For specialized workloads (specific ML services, niche data tools), AWS has more options.
The team that needs a specific niche service (e.g., Braket for quantum, Ground Station for satellite) finds AWS more likely to have it. The team with common needs finds both sufficient.
Pricing model
AWS:
- On-demand baseline.
- Savings Plans: 1-3 year commitments, up to 72% off.
- Reserved Instances: older model, similar savings.
- Spot Instances: up to 90% off.
GCP:
- Sustained-use discounts: automatic, up to 30% off for VMs running >25% of month.
- Committed-use discounts: 1-3 year commitments, additional savings.
- Spot VMs: up to 80% off.
- Custom machine types: pay for exact vCPU/RAM.
For startups with unpredictable workloads, GCP’s auto-discounts are easier. For startups with predictable workloads, AWS with Savings Plans is competitive.
Data and ML
AWS:
- Redshift (data warehouse, older than BigQuery).
- SageMaker (ML platform, comprehensive).
- EMR (Hadoop/Spark).
- Athena (SQL on S3).
- Glue (ETL).
GCP:
- BigQuery (data warehouse, serverless, unmatched SQL).
- Vertex AI (unified ML).
- TPUs (custom ML chips, cheapest for large training).
- Dataflow (Apache Beam).
- Dataproc (managed Spark/Hadoop).
For data-heavy startups, GCP is the clear winner. For ML startups, GCP is usually better (TPUs, simpler ML pipeline). For general SaaS, both work.
Kubernetes
EKS (AWS):
- ~$0.10/hr per cluster.
- Auto-upgrade opt-in.
- IRSA (IAM Roles for Service Accounts) for pod-level IAM.
- Fargate for serverless nodes.
GKE (Google):
- Standard: ~$0.10/hr per cluster.
- Autopilot: provider-managed nodes, per-pod pricing.
- Auto-upgrade on by default.
- VPC-native networking built in.
GKE is more managed, especially Autopilot. The team that picks GKE for Kubernetes is choosing the most-managed K8s. The team that picks EKS is choosing deeper AWS integration.
Startup programs
AWS Activate:
- Self-serve: $1,000 credits.
- Portfolio (VC-backed): $3,000-$100,000.
- Valid for 1-2 years.
- Deepest VC integration.
GCP for Startups:
- Self-serve: $2,000 credits.
- VC-backed: up to $200,000 over 2 years.
- Stronger credits but smaller VC network.
The team that has VC partners in both programs can stack credits. The team that picks based on credits alone usually finds the underlying platform cost dominates after credits run out.
Ecosystem and community
AWS:
- Largest community (Stack Overflow, GitHub, blogs, conferences).
- Most third-party tools and integrations.
- Most certifications and training.
- Easiest to hire for.
GCP:
- Smaller but growing community.
- Strong enterprise customers (Twitter, Spotify, PayPal).
- Better integration with Google’s products (Workspace, etc.).
- Harder to hire for (fewer GCP-experienced engineers).
The team that hires a lot of cloud engineers picks AWS (more candidates). The team that has specific GCP expertise picks GCP.
FAQ
Is AWS or GCP better for a startup?
AWS for default (services, hiring). GCP for data/ML startups (BigQuery, TPUs). Both have strong credit programs. The team that picks based on workload fit and team experience is correct.
Which has better startup credits?
GCP for Startups offers up to $200k. AWS Activate offers up to $100k. GCP has higher amounts; AWS has broader eligibility.
Can I use both AWS and GCP?
Yes but adds complexity. The team that runs multi-cloud has higher ops overhead. Most startups should start single-cloud and add the second only if there’s a specific reason.
What about Azure for startups?
Microsoft for Startups offers up to $150k in Azure credits. The team that uses Microsoft stack picks Azure. The team that doesn’t, picks AWS or GCP.
Should I switch clouds if my startup grows?
Some startups migrate to a different cloud at scale (e.g., from GCP to AWS for enterprise relationships). The team that plans for this from the start has less migration cost.
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: