credit Firebase’s tight integration and free tier for rapid launches. Users appreciate developer tooling and support, though some note fewer native services than AWS (e.g., no built-in SMTP) and occasional learning curve. Overall: dependable, secure, and cost-aware.

+158

Summarized with AI

Pros

robust infrastructure (25)

scalability (22)

AI/ML capabilities (11)

startup program credits (10)

easy to use (7)

enterprise-grade security (7)

seamless integration with Google services (4)

Cloud Storage (3)

data analytics tools (3)

global reach (3)

Pros

Cons

Reviews

All Reviews

Most Informative

used

Google Cloud Platform

to build Depth AI

•4 reviews

GKE takes the Kubernetes headaches off your plate

What's great

Kubernetes Engine (2)

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1yr ago

•76 reviews

Reddit Answers utilises AI models from both OpenAI and Google Cloud. Thanks to their data licensing agreement, the platform is able to create a robust tool that delivers accurate responses to user queries.

What's great

robust infrastructure (25)AI/ML capabilities (11)

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4 views1yr ago

used

Google Cloud Platform

to build Block Convey Drive

•8 reviews

GCP provides a full suite of AI/ML services, from data preparation and model training to deployment and monitoring, making it easier to build and deploy AI solutions.

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1 view1yr ago

used

Google Cloud Platform

to build Tana

•3 reviews

As ex Googlers, GCP provides us with familiar and trusted scalability, robustness and the security+privacy we need.

What's great

scalability (22)enterprise-grade security (7)robust infrastructure (25)

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1 view1yr ago

used

Google Cloud Platform

to build Proxy 1.0

•3 reviews

Google Cloud Platform has been essential to our infrastructure. Their robust cloud services helped us build and scale efficiently, while their powerful compute options enabled rapid development and deployment. The comprehensive documentation and enterprise-grade security made GCP a natural choice for building reliable systems at scale.