Answer Details

1. Amazon Web Services (AWS) Why many SaaS startups choose it Offers a huge range of services — compute, storage, databases, analytics, machine-learning, serverless, etc. saasbold.com +3 CloudZero +3 OpenMetal IaaS +3 Strong startup support via programs / credits (helps early-stage cost) DevCom +1 Proven at large scale with global infrastructure, built-in resilience & ecosystem. OpenMetal IaaS Things to watch / limitations Costs can escalate quickly if usage isn’t tightly managed (data transfer, idle resources, etc). OpenMetal IaaS +1 Complexity: The breadth of services means steeper learning-curve for teams not experienced in cloud operations. Potential lock-in risk: If you build deeply into AWS-specific services, moving later may be nontrivial. Best fit If your SaaS startup anticipates rapid growth, needs advanced capabilities (machine-learning, global scale, microservices, etc), and you have or will build a team comfortable with cloud operations. 2. Microsoft Azure Why many SaaS startups choose it Broad service set similar to AWS (compute, data, AI, DevOps, etc). Zeet +2 Cloudvisor +2 Strong if your stack already uses Microsoft tools (Windows Server, .NET, Azure AD) or you anticipate enterprise-customers who rely on Microsoft ecosystems. Good startup programs/credits (free tier, startup-support). DevCom Things to watch / limitations Pricing and support complexity similar to AWS — you still need to monitor cost structure. Some startup teams report that although service breadth is strong, navigation and setup take time (compared to lighter providers). Ecosystem may skew toward Microsoft-centric stacks; if your stack is fully open-source you’ll want to evaluate fit. Best fit If your SaaS product or your target enterprise customers lean heavily on Microsoft tech, or you want tight integration with identity/security in Microsoft ecosystem. 3. Google Cloud Platform (GCP) Why many SaaS startups choose it Strong capabilities in analytics, data-warehousing (BigQuery), machine-learning (TensorFlow, Vertex AI) which can be attractive for data-driven SaaS. Zeet +1 Transparent UI and good global network/infrastructure backing. Cloudvisor Startup programs and credits available through partnerships with VCs/accelerators. DevCom Things to watch / limitations Market share is somewhat smaller compared to AWS/Azure (so ecosystem, third-party tooling might be slightly less mature in some cases). CloudZero +1 If you need very broad global infrastructure or very niche services, you’ll want to ensure GCP meets your region/feature needs. As with all big providers: cost governance remains important. Best fit If your SaaS is data-intensive, you’ll lean heavily into analytics/ML, or you prefer Google’s developer-oriented tooling and infrastructure. 4. DigitalOcean Why many SaaS startups choose it Simpler, more predictable pricing and easier setup compared to the big three. DigitalOcean Built with startups in mind: less overhead, more transparent cost model. DigitalOcean +1 Good choice if you’re in early stages and want to build quickly without heavy infrastructure complexity. Things to watch / limitations While it scales, it may not offer quite as many specialized services (e.g., advanced ML/AI, global presence, enterprise-level integrations) as AWS/Azure/GCP. If you anticipate very large scale, or very high complexity (multi-region, regulatory compliance, hybrid than public cloud) you’ll want to check whether it supports all your needs. Feature set may lag big providers in some niche cases. Best fit If your SaaS startup is early-stage, you want to minimize overhead, build MVP quickly, keep costs lean, and don’t yet need the full enterprise cloud ecosystem. How to choose among them / key criteria Here are criteria you should evaluate (and they helped me compare the above options): Scalability & infrastructure capacity: Can the provider support your growth (users, regions, data volume)? Pricing transparency & cost control: Can you forecast costs, and do you have tools to monitor/manage them? Service/features match: Do you need specialized services (ML, analytics, multi-cloud, containers, serverless, hybrid)? Startup support/credits: Especially for early stage, free credits & startup programs reduce burn. Ecosystem & talent: Can your team effectively use the provider? Is there strong community/support? Vendor lock-in & portability: How easy is it to migrate later or to use multi-cloud/multi-region? Compliance/regulation: If you serve regulated industries (healthcare, fintech), does the provider have required certifications/locations? My recommendation for your SaaS startup situation Given that you are building/operating a SaaS startup (assuming you might be early to growth phase), I’d suggest the following: Start with a provider that offers good startup credits + simple cost structure (DigitalOcean is attractive). Ensure the architecture you build is cloud-agnostic or loosely coupled so moving or scaling to a larger provider (AWS/Azure/GCP) is not highly constrained (minimize lock-in). As you gain traction and complexity (global users, advanced features, regulation), plan to reassess whether you should migrate or extend to one of the large providers. Pick a provider whose ecosystem best fits your stack (for example, if your SaaS uses heavy data/analytics: GCP; if you use .NET/Microsoft stack: Azure; if you plan large scale or highest flexibility: AWS). If you like, I can rank 8-10 cloud providers (including niche or startup‐friendly ones) with feature tables (cost, ease of use, startup credits, scaling ability, best use-case) so you have a broad screen. Would you like that?