- Startup credit programs from any of the three can be a genuinely deciding factor early on.
- Existing team familiarity with a specific cloud usually outweighs marginal feature differences.
- Data and analytics-heavy products often lean toward GCP's specific tooling strengths.
- Enterprise sales into companies already on Azure sometimes make Azure the pragmatic choice.
The core services are more similar than different
Compute, storage, and managed databases across AWS, GCP, and Azure solve the same core problems in broadly similar ways. For most early-stage products, the differences between the big three matter far less than founders assume when comparing feature lists in the abstract.
Credits can be the deciding factor
Startup credit programs — sometimes tens of thousands of dollars in free cloud usage — from any of the three providers can meaningfully extend runway in the first year or two. For a cash-constrained early-stage company, this is often a legitimate, practical tiebreaker over marginal technical preferences.
Existing skill beats marginal feature differences
A team that already knows AWS well will ship faster and make fewer mistakes on AWS than the same team learning GCP from scratch to chase a slightly better-priced specific service. Familiarity is a real, underrated factor in the decision.
GCP's edge for data-heavy products
Products built heavily around data analytics, machine learning pipelines, or BigQuery-style workloads sometimes get a genuine advantage from GCP's specific tooling in that space, which is more mature and better integrated there than the equivalent offerings elsewhere.
Azure's edge for enterprise-adjacent sales
A startup selling into large enterprises that already run their infrastructure on Azure sometimes finds procurement and integration conversations easier when their own product also lives on Azure — a business consideration that can outweigh a purely technical comparison.