Google Cloud Platform

A suite of cloud computing services by Google

5.0•189 reviews•

2.1K followers

A suite of cloud computing services by Google

5.0•189 reviews•

2.1K followers

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Cloud Computing Platforms

Build with generative AI, deploy apps fast, and analyze data in seconds with Google-grade security.

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Google Cloud Platform Launches

Google Cloud RunBringing serverless to containers

Launched on April 15th, 2019

Cloud Composer, by GoogleA fully managed workflow orchestration service

Launched on May 1st, 2018

Google Cloud Text-To-SpeechText to speech conversion powered by machine learning

Launched on March 28th, 2018

Google Cloud TPUsBuild and train machine learning models with Google

Launched on May 17th, 2017

Google Cloud PostgreSQLFully-managed database service

Launched on March 9th, 2017

View all Google Cloud Platform launches

5.0

Based on 189 reviews

Review Google Cloud Platform?

Reviewers see Google Cloud Platform as a strong fit for AI, data, and containerized workloads, with Cloud Run and BigQuery mentioned most often for easy deployment, analytics, and scaling. Users praise the broad managed-service stack and solid developer experience, while founders behind products like Greta and Needle say its services work well together and help teams ship faster. The main drawbacks are harder cost forecasting, complex IAM permissions, uneven docs, and a sense from one user that some offerings feel less mature than AWS.

+187

Summarized with AI

Pros

robust infrastructure (26)

scalability (20)

AI/ML capabilities (11)

startup program credits (10)

easy to use (5)

enterprise-grade security (5)

seamless integration with Google services (4)

Cloud Storage (3)

data analytics tools (3)

global reach (3)

Pros

Cons

Reviews

All Reviews

Most Informative

•11 reviews

What's great

robust infrastructure (26)

Google Cloud Platform has been my go-to cloud provider for deploying Python applications, APIs, data pipelines, and AI workloads. Cloud Run makes deployments simple, BigQuery is excellent for analytics, and Cloud Storage integrates seamlessly with the rest of the platform. The breadth of services means I rarely need third-party infrastructure, and everything scales well as projects grow.

What needs improvement

The platform is extremely capable, but pricing can become difficult to estimate across multiple services. IAM permissions are also quite complex for new users, and some products have documentation that could be more consistent. Better cost forecasting and simpler permission management would make the platform even easier to adopt.

vs Alternatives

I evaluated AWS, Azure, and DigitalOcean. I chose Google Cloud because Cloud Run, BigQuery, and the overall developer experience fit my workflow better. For AI projects, data engineering, and containerized applications, GCP provides a strong balance between ease of use, scalability, and managed services.

Ratings

Ease of use

Reliability

Value for money

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7 views8d ago

used Google Cloud Platform to build GyftPro

•3 reviews

We chose Google Cloud Platform (GCP) for GyftPro due to its unmatched reliability, scalability, and extensive range of tools that empower our app's development and operations. GCP’s powerful data processing and AI capabilities integrate seamlessly with our AI-driven gift recommendation engine, ensuring efficient and accurate performance. Compared to alternatives, GCP offers top-tier security, robust infrastructure, and innovative machine learning services that enhance our ability to provide personalized gift suggestions and support a seamless user experience. With GCP, GyftPro can scale effortlessly, meeting user demands during peak times like holidays, ensuring reliability and responsiveness.

What's great

scalability (20)enterprise-grade security (5)robust infrastructure (26)AI/ML capabilities (11)

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37 views1yr ago

used Google Cloud Platform to build PageTest.AI

•6 reviews

GCP offers fast, scalable infrastructure with easy-to-integrate APIs and strong support for AI and machine learning workloads. It gave us the flexibility to build PageTest.AI quickly, with global reliability and cost efficiency—ideal for a bootstrapped launch. Alternatives considered: AWS, Azure Why we chose GCP: GCP struck the right balance between performance, pricing, and ease of use—especially with strong AI/ML tooling and generous free tiers for getting started quickly.