AWS has been the default answer for so long that people sometimes choose it on autopilot. GCP gets dismissed as Google trying to catch up. Both of those reactions miss the point. These are mature, capable platforms. The real question is which one fits the work you are doing, the team you have, and the budget you are working within.
We run client workloads on both. Here is how we actually think about the choice.
Where AWS wins and why it earned its market share
AWS is the broadest platform. If you need a specific managed service — a particular database engine, a media transcoding pipeline, a niche compliance certification — AWS probably has it. The ecosystem of tooling, documentation, community knowledge, and third-party integrations is unmatched. If your team already knows AWS, that familiarity has real value and you should weigh it.
- Your team has deep AWS experience and you want to hire into that pool
- You need a specific managed service that only AWS offers (or offers best)
- Your software vendors, partners, or compliance auditors are already in the AWS ecosystem
- You are building on top of services like Lambda, Step Functions, or SageMaker
- Your workload is unpredictable and you need granular scaling at the edge
The honest downside: AWS pricing is complicated. The console is complex. IAM alone has a reputation for confusing people who should know better. This does not matter much if you have experienced AWS engineers, but it is a real burden for smaller teams trying to manage infrastructure alongside product work.
Where GCP has a genuine edge
GCP tends to win on three things: compute pricing, data and analytics, and Kubernetes. Google Compute Engine's sustained use discounts kick in automatically — no upfront commitment required. If you are running workloads that run for most of the month, you often get a better effective rate than the equivalent AWS EC2 instance even before negotiating reserved capacity.
- You are doing significant data engineering, ML, or analytics work (BigQuery, Dataflow, Vertex AI)
- Kubernetes is central to your architecture — GKE is where the technology was developed
- You want simpler, more predictable billing on compute-heavy workloads
- Your team is building with Terraform and wants clean, consistent APIs
- You are already in the Google Workspace ecosystem
Cloud Run deserves a specific mention. Google Cloud Run is one of the cleanest serverless compute offerings available — you deploy a container, it scales to zero, you pay per request. For web applications and APIs that do not need constant uptime, the economics are hard to beat.
The best cloud platform is the one your team can operate at 2 AM when something breaks.
The factors that matter more than the platform comparison
Here is the truth: for most small to mid-sized web applications and SaaS products, the performance and capability difference between AWS and GCP is not the deciding factor. What matters more is team familiarity, the specific services your workload depends on, and total cost of ownership including operations time.
Migrating between cloud providers is painful and expensive. Do not pick the platform you think you might want someday — pick the one that fits your workload today. If your needs grow to the point where a platform switch makes sense, you will have the engineering capacity and the business justification to do it.
How we approach the decision for clients
We start with the workload requirements: what services are non-negotiable, what the team already knows, and what the monthly spend ceiling looks like. From there, we run a quick pricing model on the expected compute and storage profile. Nine times out of ten the right answer is obvious before we even get to a feature comparison.
If the answer is not obvious, we build the first version on whichever platform the core team is most comfortable with, containerise everything from day one, and revisit at scale. Containerisation is not just good practice — it is your exit door if you ever need it.