Pick AWS. No wait, pick Azure. Actually, GCP is winning on data. The cloud-vendor debate has been the most exhausting recurring meeting in tech for fifteen years — and 80% of the answers come down to which one matches your team, not which one is “best.”
Here’s an honest comparison from someone who’s shipped on all three in the past twelve months. Strengths, real weaknesses, and the cases where each is the obvious pick.
AWS — the default answer
AWS is what you pick when nobody on the team has a strong preference. It’s the widest service catalog, the deepest documentation, the most third-party SDK integrations, and the easiest hiring market — any senior engineer has touched EC2, S3, RDS, IAM and CloudWatch at least once.
Pick AWS when:
- You want maximum optionality five years from now
- Your team is heterogeneous — AWS has “the version of X you know”
- You sell to other startups (your customers are already there)
- You need the most mature ecosystem of 3rd-party tools (Datadog, Sentry, Snyk all started on AWS)
Avoid AWS when:
- You want clean abstractions over messy ones. AWS’s networking primitives (VPC, subnets, route tables, NAT gateways, transit gateways…) are powerful but punishingly intricate.
- The bill is going to scare your CFO. AWS is rarely the cheapest option at steady state, especially on egress.
Azure — the enterprise answer
Azure is the right answer when half your future customers are Microsoft shops. The integration with Entra ID (Azure AD), the .NET-first feature pace, and the enterprise sales motion are real advantages that show up in actual deal cycles.
Pick Azure when:
- Your customers run Active Directory and want SSO “just work”
- Your stack is .NET-heavy
- You’re selling to large enterprises with existing Microsoft EAs
- You value tight integration with Office 365 / Teams
Avoid Azure when:
- You want services that ship and stay stable. Azure churns docs and APIs more than the others.
- You’re building data-intensive workloads — BigQuery + Cloud Spanner is in a different league
- You’re a Linux-first team and the “Windows tax” matters
GCP — the data & networking answer
GCP is the underdog of the big three by market share, but punches above its weight in three areas where it’s genuinely the best option: data warehousing (BigQuery), global networking (lower egress, Anycast IPs out of the box), and ML infrastructure (Vertex AI, TPUs).
Pick GCP when:
- Your product is fundamentally data-heavy — analytics, ML, search
- You serve global users and egress costs would crush AWS economics
- You want the cleanest IAM model of the three
- You’re Kubernetes-native (GKE is still the best managed K8s)
Avoid GCP when:
- Your enterprise buyers think “cloud” means “Azure or AWS”
- You need a service that exists on AWS/Azure but hasn’t shipped on GCP yet (sometimes happens for niche compliance services)
- The hiring market matters — you’ll find 5x more AWS engineers than GCP ones
The choice nobody makes (but probably should): multi-cloud
For most products, “multi-cloud” is a expensive mistake — you get the lowest common denominator of three platforms with three sets of pipelines. But there’s one real case for it: BigQuery on GCP + everything else on AWS. The egress cost penalty is real, but BigQuery is so much better than Redshift that for data-heavy products the math still works.
Just don’t do it on day one. Start single-cloud, add the second cloud only when a specific service forces your hand.
How we approach this
Our default is AWS — we have the most production hours, the team is fluent, and the customer demand follows. We move to Azure when the customer is a Microsoft shop and to GCP when the workload is data-first. We don’t multi-cloud unless there’s a sharp reason. See our Cloud & DevOps service page for what a typical landing-zone setup looks like across each.
Takeaways
- AWS = breadth + ecosystem (default for most B2B SaaS)
- Azure = enterprise + Microsoft-shop integration
- GCP = data + networking + ML
- Multi-cloud only when there’s a specific service forcing it
- The hiring market is real. Pick the cloud you can staff.
