A SaaS company’s primary cloud is the one that hosts its production database and its customers’ data. Everything else can be moved. That single fact should drive the choice, and it is why the question deserves more than a feature comparison. This framework walks through the five factors that matter most for a production SaaS product and applies them to the three hyperscalers whose accounts are listed on the marketplace.
Factor 1: which managed services you will depend on
SaaS teams rarely run everything on raw virtual machines. They lean on managed databases, queues, object storage, secrets management and, increasingly, hosted AI models. List the services you expect to depend on in year one and check how each provider delivers them.
- AWS has the broadest catalog: EC2, managed databases, Lambda, and Amazon Bedrock for foundation models through a single API.
- Azure pairs its compute and database services with Microsoft Entra ID for identity and Azure AI services, including Azure OpenAI Service where available.
- Google Cloud is strong in data analytics, Kubernetes and Vertex AI for model training and serving.
The provider that covers your top five dependencies natively usually wins, because every gap is a service you operate yourself.
Factor 2: who your customers are
Enterprise SaaS sold to Microsoft-centric organizations benefits from living in the ecosystem those buyers already trust, and Azure’s tenant model integrates with the Microsoft 365 identities customers use daily. Developer-facing products and data-heavy platforms often align with Google Cloud. Products sold to a broad market with no single ecosystem tend to default to AWS because of its reach. The AWS vs Azure vs Google Cloud comparison goes deeper on ecosystem fit.
Factor 3: data gravity and exit cost
Compute is portable. Data is not. Once customer records, backups and analytics tables live in one provider’s storage, moving them means transfer charges, downtime windows and re-testing every integration. Choose the primary provider as if the decision is permanent, and keep the rest of the stack portable by using open formats, containers and infrastructure as code.
A practical test
Ask: if we had to leave this provider in two years, which components would be hardest to move? If the answer is “the proprietary database and the identity layer,” make sure you are comfortable with that lock-in before signing up for it.
Factor 4: credits, quotas and the first year of spend
Each hyperscaler runs startup credit programs with eligibility rules, and each starts new accounts with conservative quotas for compute and GPU capacity. The marketplace lists credit-based options for all three: the Buy AWS Account page carries balances from the AWS $1K Credit Account up to the AWS $100K Credit Account, the Buy Azure Account page runs from the Azure $1k Credit Account to the Azure $100k Credit Account, and the Buy Google Cloud Account page lists the Google Cloud $1K Credit Account through the Google Cloud $25K Credit Account.
Credits extend runway but do not change unit economics. Model your steady-state monthly cost at list prices and treat any credit as a temporary offset. Also confirm which services a credit covers, since marketplace purchases, support plans and some AI meters are often excluded.
Factor 5: operational fit for your team
The best provider is the one your team can operate at 3 a.m. A team with AWS certifications will ship faster on AWS regardless of feature tables. A .NET shop will feel at home on Azure. A team that already runs Kubernetes and BigQuery-style analytics will be productive on Google Cloud. Hiring pools matter too; check which skills are common in your region.
| Factor | Leans AWS | Leans Azure | Leans Google Cloud |
|---|---|---|---|
| Managed service breadth | Widest catalog | Strong with Microsoft stack | Data and Kubernetes |
| Customer ecosystem | Broad market | Microsoft-centric buyers | Developer and data products |
| Team background | General cloud skills | .NET, Windows, Entra ID | Containers, analytics, ML |
Making the call
Score each factor from one to five for each provider, weight data gravity and operational fit highest, and pick the leader. Then commit: put production on one provider, keep secondary workloads portable, and revisit the decision only when the product or the team changes materially. Spreading production across two clouds before you have a reason to is a cost, not a hedge; the article on when multi-cloud makes sense explains why.
