Best cloud for startups: AWS for default (most services, biggest ecosystem, deepest VC integration), GCP for data/ML startups (BigQuery, Vertex AI, GKE), Azure for Microsoft-stack startups. All three offer significant credits ($100k-$200k+ for VC-backed startups). The team that picks based on workload and team experience has the right answer; the team that picks based on credits alone often migrates within 2 years.
Table of contents
- The three hyperscalers for startups
- When AWS is the right pick
- When GCP is the right pick
- When Azure is the right pick
- Smaller providers for startups
- Total cost at startup scale
- FAQ
The three hyperscalers for startups
AWS:
- Pros: Most services (~200+), biggest ecosystem, most engineers know it, deepest VC integration.
- Cons: Most expensive without commitment, complex pricing.
GCP:
- Pros: Strong in data (BigQuery) and ML (Vertex AI), simpler pricing with auto-discounts, strong Kubernetes.
- Cons: Smaller ecosystem, fewer engineers know it.
Azure:
- Pros: Strong in Microsoft shops, hybrid (Azure Arc), free K8s control plane, deep enterprise integrations.
- Cons: Complex portal, services can be confusingly named.
When AWS is the right pick
Pick AWS when:
- Default choice for general SaaS/web apps.
- Need specific AWS services (Lambda, Step Functions, SageMaker).
- Want maximum VC/investor confidence (AWS has deepest startup programs).
- Need fastest hiring (most engineers know AWS).
- Need specific compliance certifications.
- Plan to scale to large enterprise (AWS has the deepest enterprise relationships).
When NOT to pick AWS:
- Primary workload is data analytics (GCP’s BigQuery is unmatched).
- Primary workload is ML training (GCP’s TPUs are cheapest at scale).
- Heavy Microsoft integration is required (Azure).
When GCP is the right pick
Pick GCP when:
- Building data pipeline or analytics product (BigQuery).
- Building ML product (Vertex AI, TPUs).
- Want the simplest K8s experience (GKE Autopilot).
- Want auto-discounts without commitment.
- Google Workspace is your team’s productivity suite.
When NOT to pick GCP:
- Need specific AWS services.
- Team has deep AWS expertise.
- Need very specific compliance certifications AWS has but GCP doesn’t.
When Azure is the right pick
Pick Azure when:
- Your team uses Microsoft stack (Office 365, Active Directory, .NET).
- Hybrid with on-prem is required (Azure Arc).
- Windows/.NET workloads dominate.
- Enterprise customer base uses Microsoft heavily.
- Free K8s control plane matters (Azure free vs $0.10/hr for EKS/GKE).
When NOT to pick Azure:
- Pure cloud-native, no Microsoft dependencies.
- Linux-only workloads with no Microsoft stack.
Smaller providers for startups
Beyond the hyperscalers:
- DigitalOcean: $200 in credit for new accounts. Simple, predictable pricing. Good for: dev environments, small SaaS, MVP launches.
- Linode/Akamai: $100 credit. Similar to DigitalOcean.
- Vercel: Free tier + paid plans for Next.js/frontend apps.
- Render: Free tier + paid for backend services.
- Fly.io: Free tier + paid for edge apps.
- Railway: Simple PaaS for backend.
- Hetzner: Cheapest, EU-focused, good for price-sensitive startups.
The team that runs an MVP and wants to defer cloud architecture decisions picks a PaaS (Vercel, Render, Railway). The team that needs flexibility picks a hyperscaler or smaller IaaS (DigitalOcean, Hetzner).
Total cost at startup scale
For a typical Series A startup (10 engineers, 5 production services, ~$3-10k/mo cloud spend):
- AWS: ~$5-12k/mo after credits expire.
- GCP: ~$4-9k/mo (sustained-use discounts).
- Azure: ~$5-11k/mo (free K8s helps).
- DigitalOcean: ~$2-5k/mo (predictable, no surprise charges).
The team that scales beyond Series A typically lands on a hyperscaler for service breadth. The team that stays small can keep using smaller providers indefinitely.
FAQ
Which cloud is best for early-stage startups?
Depends. For pure SaaS/web apps: AWS or GCP. For data-heavy startups: GCP. For Microsoft-stack: Azure. For MVP-first: PaaS (Vercel, Render) or DigitalOcean. The team that picks based on workload has the right answer.
How much in cloud credits can a startup get?
Up to $100k from AWS Activate (portfolio tier), up to $200k from GCP for Startups (VC partner tier), up to $150k from Microsoft for Startups. The team that applies to all three maximizes credits.
Should I use multiple clouds?
Not for early-stage. Multi-cloud adds complexity (IAM, networking, ops expertise in both). The team that goes single-cloud and standardizes has lower overhead. The team that has a specific reason (BigQuery + rest of stack on AWS) does multi-cloud intentionally.
What about Vercel, Render, Railway?
PaaS options that abstract cloud entirely. The team that picks these for MVP launch can defer cloud architecture decisions. Migration to a hyperscaler is straightforward when the team outgrows the PaaS.
Is it expensive to switch clouds later?
Yes. Plan 3-6 months for a non-trivial migration. The team that picks well at start saves significant cost. The team that picks based on credits alone often migrates within 2 years when the credits run out.
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: