Cloud & Infrastructure

Google Cloud Development Services

We build on Google Cloud for teams whose product lives or dies on data, analytics or fast container deployment.

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How we work

Google Cloud earns its place when a product's center of gravity is data. BigQuery handles analytical workloads that would take real engineering effort to replicate on another cloud's warehouse tooling, and Cloud Run gives teams one of the simplest paths from a container image to a running, autoscaling service.

Our google cloud development services cover Cloud Run and GKE for compute, Cloud SQL and Firestore for data, and Vertex AI where a project needs managed machine learning infrastructure rather than calling a model API directly. We also work with Firebase for teams building mobile or web products that want authentication, hosting and a realtime database in one connected set of tools.

As with any cloud, the platform is not the differentiator by itself. We pick Google Cloud when its specific strengths, analytics, container simplicity, or Firebase's mobile tooling, map onto what the product actually needs, and say so plainly when they do not.

Fit

When Google Cloud is the right choice, and when it is not

Good fit

Your product depends on analytics or reporting over large datasets, where BigQuery's separation of storage and compute keeps query costs and speed manageable.
You want the simplest possible path from a Docker image to a running, autoscaling web service, which is what Cloud Run is built for.
You are building a mobile or web app and want Firebase's authentication, hosting and realtime database working together out of the box.
Your machine learning work benefits from Vertex AI's managed training and endpoint infrastructure rather than assembling your own.
Your team already has strong GCP experience from a previous product and reuses that expertise rather than starting from zero on another cloud.

Consider something else

Enterprises needing the broadest set of compliance certifications and regional coverage across many jurisdictions often find AWS or Azure ahead of Google Cloud on availability.
Teams built around the Microsoft stack, especially .NET and Entra ID, generally get more value from Azure's tighter identity integration.
Hiring for Google Cloud-specific roles can be slower in some markets than for AWS, which affects long-term staffing more than the technology itself.
If a workload needs a very large third-party marketplace of prebuilt integrations, AWS's ecosystem is still the deepest of the three major clouds.

What we build

What we build with Google Cloud

Compute
Cloud Run for containerized services that scale to zero and back without cluster management, GKE for teams that need full Kubernetes control, and Compute Engine for workloads that need a specific VM configuration.
Data and analytics
BigQuery for petabyte-scale analytical queries with storage and compute billed separately, Cloud SQL for managed Postgres and MySQL, and Firestore for document-shaped data with realtime sync to clients.
Messaging
Pub/Sub for decoupled, at-least-once event delivery between services, used as the backbone for pipelines feeding BigQuery or triggering Cloud Functions.
Machine learning
Vertex AI for managed model training, tuning and endpoint hosting, used when a project trains or fine-tunes its own models rather than only calling a hosted API.
Mobile and web tooling
Firebase Authentication, Hosting and Firestore together for products that want a connected mobile and web backend without assembling separate services.
Content delivery
Cloud CDN and Cloud Storage for serving static assets and media at low latency close to users.

Ecosystem

Libraries and tools we pair it with

Terraform
gcloud CLI
Cloud Build
Firebase
Looker Studio
dbt
Pub/Sub
Artifact Registry

Cloud Run Versus GKE for Container Workloads

Cloud Run is our default for a containerized service on Google Cloud. It takes a Docker image, runs it, scales it to zero when idle and back up under load, and needs no cluster to patch or upgrade. Most APIs, background workers and internal tools fit this model without friction.

GKE earns its complexity when a system needs Kubernetes-specific features Cloud Run does not expose: custom schedulers, a service mesh across many services, or workloads that need to share a cluster's networking model with services already running elsewhere on Kubernetes. We recommend GKE when a team already runs it, not as a starting point for a new project.

BigQuery for Product and Marketing Analytics

BigQuery separates storage from compute, so a team pays for the data it queries rather than for a warehouse sized to peak load. That makes it a strong fit for product analytics, marketing attribution and reporting pipelines that run irregular, exploratory queries rather than a constant stream of small transactions.

We typically land event data in BigQuery through Pub/Sub or a batch export, model it with dbt, and expose dashboards through Looker Studio or a BI tool the client already uses. For transactional workloads with frequent small writes and strict consistency needs, Cloud SQL or a dedicated operational database remains the better fit; BigQuery is not a replacement for an application's primary database.

Services

Services that use Google Cloud

Industries

Where we use it

FAQ

Google Cloud FAQs

Is Google Cloud a good fit for a typical SaaS backend, or is it mainly for analytics?

It works well for a typical SaaS backend too, particularly with Cloud Run for the API layer and Cloud SQL for the primary database. Its advantage sharpens further once the product also needs serious analytics, since BigQuery sits in the same account without a separate data platform to integrate.

What is the real difference between Cloud Run and GKE for a small engineering team?

Cloud Run removes cluster management entirely: you deploy a container and it scales automatically, with no nodes to patch. GKE gives full Kubernetes control but requires ongoing cluster operations. A small team without dedicated infrastructure staff is almost always better served by Cloud Run unless a specific Kubernetes feature is a hard requirement.

How does BigQuery pricing actually work compared with a traditional data warehouse?

BigQuery charges separately for storage and for the data scanned by each query, rather than for a fixed-size warehouse running continuously. This rewards well-partitioned tables and selective queries, and can cost more than expected if queries routinely scan entire large tables, so query design matters as much as the platform choice.

Should we use Firebase or build our own authentication and database layer on Google Cloud?

Firebase is a strong choice for mobile and web products that want authentication, realtime sync and hosting working together with minimal setup, especially for early-stage products. Once an application needs complex relational queries or fine-grained access control beyond Firestore's security rules, we often introduce Cloud SQL alongside or instead of Firestore.

Can we use Vertex AI instead of calling a model provider's API directly?

Vertex AI makes sense when you are training or fine-tuning your own models, or need managed infrastructure for custom machine learning work. For most teams calling a general-purpose language model for chat, summarization or extraction, calling the provider's API directly is simpler and keeps you on their latest features sooner.

How long does migrating an existing workload to Google Cloud take?

A single service with a managed database typically migrates in four to eight weeks, including data migration and a validation period running both environments in parallel. Timelines grow when the workload includes custom infrastructure automation tied to another cloud's specific services, which has to be rebuilt rather than lifted as-is.

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