AWS vs Google Cloud for Custom Software — A Practical Guide

Every team that builds custom software must pick a cloud. The aws vs google cloud for software deployment choice has no single right answer. Both are production-grade platforms. Both run some of the world's largest apps. Both give you the core building blocks: compute, storage, databases, networking, and managed services. So where do they differ? They differ in how deep the service catalog goes. They differ in AI and machine learning tools. They differ in developer experience, pricing, and how easy it is to hire skilled people.

This is not about which cloud is better overall. It is about fit. Pick the cloud that fits your team's skills. Pick the one that fits what your product will need. Pick the one whose services matter most to your app.

Your database choice often goes hand in hand with your cloud choice. PostgreSQL vs MongoDB for SaaS - making the right database choice covers the data tier that pairs with your cloud.

AWS: breadth, ecosystem, and market share

Amazon Web Services has the largest market share. It sat near 31 to 33 percent in 2025. It has also run the longest. That brings real advantages. It has the widest catalog, with over 200 services. It has the most engineers who know it well. It has the most documentation and training. It has the broadest partner network. Need a standard service like container orchestration, a managed database, a CDN, serverless compute, or message queuing? AWS has a mature, well-documented option for each.

AWS shines in a few cases. It is strong for apps with complex networking needs. It is strong when you need the widest geographic reach, since AWS has more regions than any rival. It is strong when you hire from a broad talent pool, since AWS skills are the most common. It also helps when your product relies on AWS Marketplace to buy and license software.

Google Cloud: AI infrastructure and data analytics

Google Cloud Platform stands out for AI and machine learning. Google offers Tensor Processing Units, or TPUs. It offers the Vertex AI platform. It plugs in Google's own AI models, like Gemini and Imagen. This gives GCP a real edge for apps with heavy AI work. Does your software train or fine-tune machine learning models? Does it use AI services at scale? If so, GCP's AI tools are often more capable. They are often cheaper than the AWS equivalents.

Google Cloud is also strong in data analytics. BigQuery, Dataflow, and the wider data tools are best in class for large-scale work. Some SaaS products run heavy analytics. Some firms must process huge datasets without spending a fortune. For both, GCP's data tools often beat AWS on power and price.

The honest verdict: AWS if, GCP if

Choose AWS if any of these fit. Your team already knows AWS, or you hire where AWS skills are most common. Your app has standard needs, with no heavy AI or analytics work. You need the widest geographic reach. Your product uses the AWS Marketplace, or it ties into other AWS services your customers run. Or you are starting fresh and want the most mature ecosystem with the largest community.

Choose Google Cloud if any of these fit. Your app has heavy AI or ML work, and you want Google's AI tools like TPUs, Vertex AI, and the Gemini APIs. Your product needs large-scale analytics, where BigQuery's speed and pricing help. Your team already knows Google Cloud. Or you are building AI-first apps that gain from tight links to Google's AI services. Some teams also weigh in-house engineers against an agency partner for their cloud work. in-house developer vs development agency - what makes sense covers that staffing call.

What the decision often comes down to

In practice, two things matter most. The first is what platform your team already knows. The second is how much AI work your app needs. Teams with strong AWS skills rarely gain enough from GCP's AI edge to justify the learning curve. The exception is when AI sits at the core of the product. For those products, GCP's edge is large enough to be worth the move. Building AI-native SaaS? Look hard at GCP. Building standard SaaS with little AI or analytics? Default to AWS for the ecosystem.

How TTGC makes cloud infrastructure recommendations

Through The Glass Creatives has run production systems on both AWS and GCP. For new SaaS builds with no special AI needs, the usual pick is GCP Cloud Run for containerized apps. It offers a strong developer experience. It offers competitive pricing on the compute tier. It links well with the Google Cloud AI services TTGC often builds around. For clients who need AWS, TTGC delivers on AWS without hesitation. A platform preference never overrides client needs or team skills.

The cloud your team runs with confidence beats the cloud with the better catalog on paper. Skill compounds over time. Features mean little if you do not deploy them well.