AWS vs Azure vs Google Cloud: choosing for your stage, not the hype

Ask three engineers which cloud to use and you'll get three confident, contradictory answers — usually a defence of whichever one they happen to know best. That's the trap. AWS, Azure, and Google Cloud all run real businesses at planetary scale; none of them will be the reason your product fails. The reason it stumbles is almost always operational: a team running a platform it doesn't understand, a bill nobody forecast, or a migration sold on features the company will never touch.

So instead of comparing feature lists, choose the way you'd hire — for fit with where you are right now. Below is the decision the way we actually walk clients through it, organized by the things that change the answer: your stage, your team, your existing stack, and your cost profile.

First, ignore the feature-count contest

Every provider publishes a number of "200+ services." It's noise. A typical product uses a dozen primitives — compute, a managed database, object storage, a queue, a load balancer, secrets, logging, and a CI/CD path — and all three clouds do those competently. What differs is the texture of the day-to-day: the console, the IAM model, the docs, the defaults, and how quickly your specific team can debug something at 2 a.m. Pick on that, and you'll be right more often than the spec sheet would have made you.

The exception is when you genuinely need one provider's standout capability — and you should be able to name it in a sentence. If you can't, it isn't a deciding factor.

Match the cloud to your team, not the other way around

The single biggest predictor of whether a cloud will go well is what your people already know. Re-skilling a team onto an unfamiliar platform costs months you usually can't spare, and you pay it in slower delivery and avoidable incidents. A quick read on each:

• AWS — the broadest hiring pool and the deepest pile of tutorials, Stack Overflow answers, and third-party tooling. If you want to recruit cloud-experienced engineers quickly, this is the safe default.
• Azure — the path of least resistance for organizations already living in Microsoft: Active Directory, Office 365, .NET, SQL Server. Identity and licensing integration is the real selling point, not the compute.
• Google Cloud — strongest where the team leans data, analytics, and ML, or is committed to Kubernetes. BigQuery and a cleaner Kubernetes story win it serious fans among data-heavy teams.

If your team has no incumbent skill set, default to AWS for hiring depth — and revisit only if a specific workload below pulls you elsewhere.

Let your stage set the bar

What's "right" at five people is wrong at fifty, and vice versa. Be honest about where you are:

• Early / pre-product-market-fit — optimize for speed and low cognitive load. Managed, opinionated services and a serverless or platform-as-a-service path let two engineers ship without becoming part-time infrastructure admins. Don't build for scale you don't have yet.
• Growing / scaling — now reliability, observability, and cost control start to matter. This is the stage to invest in infrastructure-as-code, CI/CD, and clear environments — on whichever cloud your team runs best — so growth doesn't turn into firefighting.
• Established / compliance-driven — regional data residency, audit posture, enterprise support tiers, and procurement relationships dominate. Here the "boring" criteria — contracts, certifications, support SLAs — legitimately outweigh developer ergonomics.

Read the bill before you sign the contract

Cloud spend rarely blows up on compute. It blows up on the things teams forget to model — and those traps look similar across all three providers:

• Egress fees — moving data out of the cloud (or between regions) is where surprise charges live. High-bandwidth products should price this before committing.
• Managed-service premiums — managed databases, search, and analytics buy you convenience but cost meaningfully more than self-hosting. Often worth it for a small team; model it anyway.
• Idle and orphaned resources — non-production environments left running, unattached storage, and over-provisioned instances quietly compound month over month.
• Discount commitments — reserved instances and committed-use discounts cut bills 30–60%, but lock you in. Only commit to the steady-state baseline you're confident about.

Sticker prices across the three are close enough that they rarely decide it. Your architecture and your discipline about cleanup decide it.

A note on lock-in and multi-cloud

You'll hear that you must stay "cloud-agnostic." For most teams that's premium advice you can't afford to follow. Building to a lowest-common-denominator abstraction means giving up the managed services that make a single cloud productive — you pay the cost of portability without ever using it. Use your chosen cloud's strengths, keep genuinely portable pieces (containers, standard SQL, open formats) portable, and only go multi-cloud when a concrete requirement — regulatory, redundancy, or an acquisition — actually demands it.

The short version

Choose for fit, not features. Default to what your team already runs well; let your stage decide how much infrastructure to build; model egress and commitments before you sign; and resist multi-cloud until something real forces it. Any of the three can carry you to serious scale — the deciding factor is whether it's run with discipline, not which logo is on it. If you'd like a second opinion on the right cloud for your stage, or help migrating without the surprises, that's exactly what our Cloud & DevOps team does every week.