Google Cloud Platform reviews

A great cloud provider!

We run Popcraft on Google Cloud because it lets us autoscale generation traffic and sit close to Google's own models (Veo, Nano Banana, Gemini), with one account for compute, storage and the AI APIs we depend on.

We're using GCP for quick and seamless signup/login and for our clients' Postmaster Tools data integrated into our B2B email deliverability SaaS application.

GCP was the right infrastructure choice for Manta. The startup support programme made it accessible early, and deep familiarity with the platform meant we could move fast without second-guessing our infrastructure decisions.

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. 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. 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. Costs are predictable when budgets, alerts, labels, and quotas are configured. Compute Engine and GKE are easier to forecast, while BigQuery needs closer monitoring because query volume and data scanned can change quickly. GCP migration from AWS or Azure is manageable if the architecture is containerized or API-driven. Moving apps to Cloud Run, GKE, Cloud Storage, and BigQuery is straightforward, but IAM, networking, and service mapping require careful planning. IAM and Cloud Armor are strong for access control and web protection. For compliance-heavy workloads, they should be combined with audit logs, VPC controls, encryption, organization policies, and proper role design.

Runs all our backend and the agent infra that powers Blop's discovery, healing, and test orchestration.

It’s an exceptionally capable AI agent.

The engine behind our agents, emulators, devices, and pipelines. Scales as fast as our customers ship.

Preflight runs on GCP. Cloud Run and the startup credits made spinning up AI workloads painless.

Google Cloud’s developer-friendly infrastructure gives Traycer a rock-solid foundation. Cloud Run keeps our stateless server stack scalable, fast, and easy to manage.

Google Cloud has been rock-solid for us — uptime is excellent and performance is consistent, even under heavy workloads.

Gave us $2,000 USD in AI Startup Credits. Was a Godsend 🙏🏼