The community submitted 220 reviews to tell
us what they like about Google Cloud Platform, what Google Cloud Platform can do better, and
more.
5.0
Based on 220 reviews
Review Google Cloud Platform?
Reviewers describe Google Cloud Platform as a strong, developer-friendly choice for deploying apps, APIs, data pipelines, and AI workloads, with Cloud Run and BigQuery mentioned most often for simple deployment, scaling, and analytics. Founders of Needle, Fina, and Greta likewise praise reliable infrastructure, clean service integration, and faster shipping. The main caveats are less mature product coverage than AWS in some areas, harder pricing estimates across services, complex IAM permissions, and uneven documentation for new users.
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.
Amazing for the most bit, I do feel like they are not as advance as AWS. Even their product offerings arent as mature as AWS. For example AWS has their own SMTP offering which you can use to build an email notification system. With GCP you are forced to go through a third party like SendGrid.
We chose Google Cloud Platform specifically for Google Cloud Run and Cloud Firestore.
Webhook traffic is notoriously unpredictable and bursty: you need an endpoint that can instantly scale up to handle hundreds of concurrent HTTP payloads, but also automatically scale down to zero when idle so you aren't paying for unused servers.
Deploying our Next.js service as a single container on Cloud Run gave us instant HTTPS endpoints, effortless autoscaling, and seamless native integration with Cloud Firestore for persistent event storage across container restarts. All without needing to manage clusters, Redis queues, or complex infrastructure.
Orchestrating complex workflows between the browser extension, local AI context, and external food delivery APIs results in highly variable traffic patterns. Google Cloud Run provides an exceptional container-based deployment environment that automatically scales the lightweight Node.js API server from zero to instantly handle sudden bursts in active cart sessions. This serverless execution model ensures compute resources are only utilized during active agent events, telemetry collection, and checkout verification, completely removing the operational overhead of managing underlying infrastructure.
We chose Google Cloud Platform for its managed infrastructure and seamless integration between services. LegacyFixer uses Cloud Run to execute its services, Cloud SQL for persistent data, and Secret Manager for secure credential management. These services allow us to deploy, scale, and maintain our GitHub App without managing dedicated servers, while keeping the architecture modular and operational costs under control.