Practical uses
- Deploy an enterprise application at scale.
- Build a big data and analytics pipeline.
- Use ML (vision, NLP) in an application.
- Host a managed database (Cloud SQL, Firestore).
- Deploy Docker containersCloud Run.
- Integrate Google Analytics and Google Ads.
Pros and cons
What Google Cloud does especially well — and the limits to anticipate.
Pros
- Vast and powerful cloud services.
- Generous permanent free tier (Always Free).
- Free trial credit to get started.
- Excellent integration with Google ecosystem.
- Steep discounts with committed-use contracts.
- World-class machine learning and big data.
- Auto-scaling built-in.
- Very reliable and performant.
Cons
- Steep learning curve (many services).
- Complex cost estimation (often high).
- Interface/dashboard can be overwhelming.
- Proprietary lock-in.
- Premium support expensive.
- Less simple than alternatives (Heroku, Fly.io for simple apps).
Google Cloud: when it makes sense.
GCP suits teams building complex cloud applications with data/ML; for simple MVPs or lightweight apps, Firebase or Fly.io may be more appropriate.
Keep if
You're building a scalable enterprise application.. You need ML or advanced analytics.
Challenge if
You're building a very small MVP on tight budget (use Firebase).. You have very simple needs (a basic VM is enough).