AWS vs Google Cloud: Which Should You Choose?
AWS and Google Cloud Platform (GCP) both offer mature, capable infrastructure. GCP has particular strengths in data analytics and machine learning, while AWS offers a broader general-purpose service catalog and larger market presence.
AWS
Best for: General-purpose infrastructure needs, teams wanting the largest talent pool and community resources, or broad service breadth across many categories.
Google Cloud
Best for: Data-heavy applications, machine learning workloads, or teams that value GCP’s specific strengths in BigQuery and its ML/AI tooling.
Cost Considerations
Pricing is broadly comparable across core compute and storage services between AWS and GCP, though specific workloads can favor one or the other depending on usage patterns. GCP has historically had a reputation for sustained-use discounts that apply automatically, whereas AWS savings typically require more active management (Reserved Instances, Savings Plans) to capture. Neither is a reliably cheaper default without pricing your specific workload.
Performance & Security
Both platforms offer strong security postures and relevant compliance certifications. For data-intensive and ML workloads specifically, GCP’s tooling (BigQuery, Vertex AI) is often considered more mature and easier to use than AWS’s equivalents, though AWS has invested heavily in closing this gap in recent years.
Which Should You Choose?
Startups / early-stage
AWS is the more common default for general-purpose applications; GCP is worth serious consideration if your product is data or ML-centric.
Data/ML-focused products
GCP’s BigQuery and ML tooling are genuinely strong reasons to consider it over AWS for these specific use cases.
Agencies
AWS’s larger talent pool and ecosystem generally make it the safer default for varied client work, unless a specific client need points toward GCP.
Our Take
We specialize in AWS and that’s what we recommend for the general-purpose infrastructure work most of our clients need. If your product is heavily data or ML-focused, it’s worth evaluating GCP’s specific tooling in that area — we’ll say so rather than claim AWS is the right fit for every use case.
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