AWS vs GCP

The default choice vs the data and AI specialist, compared on how they actually bill you.

At a glance

Pricing

Cloud pricing depends on region, instance family, and usage shape, so exact totals only come from each vendor's calculator. But the entry economics and discount mechanics are concrete, verified as of August 2026.

One change worth knowing: AWS retired its old 12-month free tier in July 2025 and moved new accounts to the credit model above. Older comparisons that mention a year of free t2.micro hours are describing a program that no longer exists for new signups.

When to pick AWS

Pick AWS when you are optimizing for optionality and people. Whatever odd requirement shows up in year two, AWS almost certainly has a managed service for it, and there is a blog post, a Terraform module, and a contractor who has done it before. That ecosystem depth is not a soft factor. For a small team, being able to hire engineers who already know the platform is worth more than a modest compute discount.

It is also the safer choice when your customers or partners dictate architecture. Enterprise procurement, compliance checklists, and third-party tools tend to assume AWS first. If you sell into companies that will review your infrastructure, running on the platform their own teams use removes friction you would otherwise pay for in sales cycles.

The tradeoff is billing complexity. Getting good prices on AWS requires active work: choosing Savings Plans, right-sizing instances, and watching egress. AWS rewards teams that manage it and quietly overcharges teams that do not.

When to pick GCP

Pick GCP when data and AI are the center of the workload. BigQuery is the strongest argument: a serverless warehouse where you pay for storage and queries rather than managing clusters, and it routinely wins on analyst productivity. Vertex AI, first-party access to Gemini models, and TPUs make it the natural home for teams training or heavily using models.

GCP also suits small teams that do not want to become billing experts. Sustained-use discounts apply automatically when a workload runs most of the month, with no commitment or forecasting required. The always-free tier, including a small VM and a monthly BigQuery quota, has no expiry date, which makes GCP friendlier for side projects and long-running experiments than the new time-boxed AWS free plan.

The honest downsides: a smaller catalog, a smaller hiring pool, and a long-standing reputation for deprecating products that makes some teams nervous about betting on less central services. Stay near the core services and that risk is small.

What most comparisons miss

Most comparisons rank feature checklists, but feature parity is close enough that it rarely decides anything. The bigger real-world cost differences come from billing mechanics. AWS prices assume you will actively manage commitments; GCP discounts passively. Egress pricing punishes chatty architectures on both clouds, and it is also the exit tax: moving petabytes out is expensive enough that your first choice is stickier than any contract. Both vendors now waive egress fees for customers who are fully migrating away, after regulatory pressure, but that only helps if you leave entirely.

The second missed point is that team composition should outweigh benchmarks. If your first three hires know AWS, picking GCP for a 10 percent theoretical savings means paying a learning tax that dwarfs the discount. The cheapest cloud is almost always the one your team already knows, and the comparison only gets interesting when the workload is data-heavy enough for BigQuery and TPUs to overcome that.