[ Strong where data and containers meet ]
GCP is our recommendation when the work involves data or containers — BigQuery and Cloud Run are both best in class, and Cloud Run in particular is the simplest way to put a container into production and pay nothing when idle.
GCP is our recommendation when the work involves data or containers — BigQuery and Cloud Run are both best in class, and Cloud Run in particular is the simplest way to put a container into production and pay nothing when idle.
Tell us whether there is analytics in thisA container, a URL, scaling to zero. For applications with uneven traffic it is both simpler and cheaper than keeping a server warm.
Serverless analytics that handles large data without a cluster to manage. It is the clearest reason to choose GCP over AWS.
Auth, push, remote config and Firestore for mobile products, integrated with the rest of the project rather than bolted on.
Budget alerts, request limits and log retention set deliberately. Scale-to-zero is only cheap if nothing is left scaling up unattended.
Containerised applications on Cloud Run with Cloud SQL, a CDN and CI/CD — scaling to zero between traffic, which suits uneven Indian usage patterns.
Analytics warehouses and pipelines — ingesting from your application, transforming on a schedule, and serving dashboards without a cluster to run.
Auth, Firestore, push, remote config and crash reporting for mobile products, with the same project and billing as the rest of the stack.
Data and container workloads — a strong yes. A broad enterprise estate with existing AWS agreements — probably not, and we will say so.
Cloud Run for almost everything; GKE only where you genuinely need Kubernetes. Running a cluster you do not need is a full-time cost.
Cloud SQL for transactions, BigQuery for analytics, with a deliberate path between them rather than reporting queries on the production database.
Terraform for projects, networking, services and IAM, with separate environments and reviewed changes.
Cloud Build or GitHub Actions to build, test and deploy containers, with staged rollout and an easy rollback.
Cloud Monitoring alerts, log retention set to something sensible, budget alerts, and max instance limits so a traffic spike cannot become an invoice.
We pick a cloud per workload rather than by allegiance, and Google Cloud wins two arguments clearly. Cloud Run is the simplest good way to run a container in production — deploy, get a URL, scale to zero when nobody is using it — and BigQuery handles analytical data at a scale that would otherwise need a cluster and someone to look after it.
For a broad enterprise estate with existing AWS commitments, we would not move you for the sake of it. The reason to be on GCP is a specific fit, and for Indian businesses with spiky traffic, scale-to-zero is often that reason on its own.
GKE is excellent and a cluster is a job. Most applications need a container and a URL, which is exactly what Cloud Run is.
Analytical queries on the transactional database is the commonest cause of mysterious slowness. BigQuery exists for this.
Scale-to-zero saves money; unbounded scale-up spends it. Max instances and budget alerts are set at deployment.
Direct VPC access, volume mounts and GPU support removed the last reasons to run a cluster for a straightforward service.
Editions and per-query controls made costs predictable, which had been the main objection from smaller teams.
One project, one bill and shared IAM removed the awkward split that used to make mobile-plus-backend projects harder to manage.
Google Cloud if your workload is containers or analytics: Cloud Run and BigQuery are both genuinely better than their AWS equivalents for those jobs. AWS if you need the broadest service catalogue, have existing commitments, or your team already knows it. Neither is a bad choice; the workload should decide.
Let’s talk about your google cloud project. No obligation, just a conversation.
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