"Should we build on AWS or GCP?" is one of the first questions we hear from clients starting a cloud project. Both platforms are mature, secure, and capable of running mission-critical workloads at scale—so the right answer usually comes down to your team's existing skills, your data and AI needs, and your budget model, not which provider is "better."
Here's how we help clients think through the decision.
Where AWS Tends to Win
AWS has the largest service catalog and the deepest bench of third-party tooling and talent. If you need a very specific managed service, there's a good chance AWS has shipped it first. Teams that already have AWS-certified engineers, or that rely heavily on the broader AWS partner ecosystem, often get to production faster by staying on AWS.
AWS is also a strong default for organizations with complex, highly customized infrastructure requirements—multi-account governance, granular IAM policies, and a wide range of compute and storage options for specialized workloads.
Where GCP Tends to Win
GCP has a real edge in data analytics and AI. BigQuery is one of the fastest and most cost-predictable data warehouses available, and it integrates tightly with Vertex AI for teams building machine learning and generative AI features. If your roadmap is data-and-AI-first, GCP often requires less integration work to get from raw data to a working model.
GCP's networking is also frequently cited as simpler and faster to reason about—Google's global network backbone means fewer surprises when you scale across regions. Teams that value a smaller, more curated set of services (rather than dozens of overlapping options) often find GCP's console and APIs more approachable.
Cost Considerations
Both providers offer discounts for committed usage (AWS Savings Plans and Reserved Instances vs. GCP Committed Use Discounts), and both offer on-demand pricing for unpredictable workloads. In our experience, GCP's per-second billing and sustained-use discounts can be slightly more forgiving for spiky or bursty workloads, while AWS's broader instance catalog can make it easier to right-size costs for very specific, steady-state workloads. The real cost driver is almost always architecture decisions, not the sticker price of the provider.
A Practical Framework
When we run a platform assessment with a client, we typically weigh:
Existing skills and tooling — What does your team already know? Retraining has a real cost.
Data and AI roadmap — Is analytics and machine learning central to your product? GCP often reduces integration overhead here.
Compliance and industry requirements — Some regulated industries have established patterns and audited reference architectures on one platform.
Vendor and partner ecosystem — Are there existing tools, SaaS integrations, or partners your business depends on that are tied to one provider?
You Don't Always Have to Choose Just One
Many of our clients run a multi-cloud strategy by design—for example, using AWS for core application infrastructure while running analytics and AI workloads on GCP's BigQuery and Vertex AI. This isn't the right fit for every team (it adds operational complexity), but for organizations with strong platform engineering capacity, it lets you use the best tool for each job.
Conclusion
There's no universally "correct" choice between AWS and GCP—only the right choice for your team, your workloads, and your growth plans. At Vizio Consulting, we help you evaluate both platforms against your specific requirements, build a proof of concept where it makes sense, and implement a production-ready foundation on whichever platform—or combination of platforms—fits best.