AWS vs Google Cloud is a decision that every team building custom software has to make - and the right answer is not the same for every team. Both are production-grade cloud platforms used by some of the world's largest applications. Both provide the core infrastructure services any custom software needs: compute, storage, databases, networking, and managed services. The meaningful differences are in depth of service catalog, AI and ML infrastructure, developer experience, pricing model, and the breadth of the available talent pool.

The choice is not about which cloud is objectively better. It is about which cloud fits your team's skills, your product's likely infrastructure requirements, and the services that will be most load-bearing for your specific application.

For context on the database layer decision that often accompanies cloud selection, PostgreSQL vs MongoDB for SaaS - making the right database choice covers the data tier selection that pairs with your cloud infrastructure choice.

AWS: breadth, ecosystem, and market share

Amazon Web Services holds the largest cloud market share - approximately 31-33% as of 2025 - and has been in production the longest. This translates to practical advantages: the widest service catalog (over 200 distinct services), the largest community of engineers with AWS experience, the most extensive documentation and training resources, and the broadest partner ecosystem. For any standard infrastructure requirement - container orchestration, managed databases, CDN, serverless compute, message queuing - AWS has a mature, well-documented service.

AWS is particularly strong for: applications with complex networking requirements, workloads that need the broadest possible geographic distribution (AWS has more regions than any competitor), teams hiring from a broad talent pool where AWS certification is most prevalent, and products that depend on AWS Marketplace for software procurement and licensing.

Google Cloud: AI infrastructure and data analytics

Google Cloud Platform's differentiated strength is its AI and machine learning infrastructure. Google's Tensor Processing Units (TPUs), Vertex AI platform, and the direct integration of Google's first-party AI models (Gemini, Imagen, etc.) give GCP a meaningful advantage for applications with significant AI workloads. For custom software that includes training or fine-tuning machine learning models, or that uses AI-powered services at scale, GCP's AI tooling is frequently more capable and more cost-effective than AWS's equivalent offerings.

Google Cloud is also strong in data analytics: BigQuery, Dataflow, and the broader data ecosystem are best-in-class for large-scale data processing. For SaaS products with heavy analytics requirements or for companies that need to process large datasets cost-effectively, GCP's data infrastructure frequently outperforms AWS on both capability and pricing.

The honest verdict: AWS if, GCP if

Choose AWS if: your team has AWS experience or you are hiring from a talent pool where AWS is most prevalent, your application has standard infrastructure requirements without heavy AI or analytics workloads, you need the broadest possible geographic distribution, your product interacts with the AWS Marketplace or integrates with other AWS services your customers already use, or you are starting fresh and want the most mature ecosystem with the largest community.

Choose Google Cloud if: your application has significant AI or ML workloads and you want access to Google's AI infrastructure (TPUs, Vertex AI, Gemini APIs), your product requires large-scale data analytics where BigQuery's performance and pricing model are advantageous, your team has existing Google Cloud expertise, or you are building AI-first applications that benefit from tight integration with Google's AI services. For teams that are also making decisions about in-house engineering versus agency partners on their cloud infrastructure, in-house developer vs development agency - what makes sense covers that resourcing decision.

What the decision often comes down to

In practice, two factors dominate: the team's existing platform knowledge and the AI workload profile. Teams with strong AWS experience rarely benefit enough from GCP's AI advantages to justify the operational learning curve unless AI is central to the product - and for AI-central products, GCP's advantages are meaningful enough to justify the investment. Teams building AI-native SaaS products should evaluate GCP seriously; teams building standard SaaS without heavy AI or analytics workloads should generally default to AWS for the ecosystem advantages.

How TTGC makes cloud infrastructure recommendations

Ravve at Through The Glass Creatives has deployed production systems on both AWS and GCP. TTGC's standard recommendation for new SaaS builds without specific AI workloads is GCP Cloud Run for containerized applications - it provides excellent developer experience, competitive pricing on the compute tier, and strong integration with the Google Cloud AI services that TTGC commonly builds around. For clients with specific AWS requirements, TTGC delivers on AWS without hesitation. Platform preference does not override client requirements or team skill sets.

The cloud platform your team operates confidently beats the cloud platform with the theoretically superior service catalog every time. Operational competence compounds; platform features don't matter if they're not deployed correctly.