Cloud platform
Google Cloud
Google Cloud is often the right home for data. Its analytics tools handle very large data sets, and it offers direct access to Google's AI models. We build applications and data platforms on it, and we compare it with other clouds where the case is less clear.
- 1,200+ clients helped
- Building since 2010
- 15 services, one team
- 50+ technologies
At a glance
What it is
Google Cloud is Google's cloud platform, known for data and analytics tools, strong container support, and direct access to Google's AI models.
Problems it solves
Organizations with large data sets or AI plans want a platform for analysis and models, without stitching together many separate tools.
Who it suits
Teams with data-heavy or AI-heavy workloads, organizations using Google Workspace, and companies building on containers.
How to choose
Consider Google Cloud when data analysis, AI, or containers are central to the workload, or when you already use Google Workspace.
What it means
A cloud with data at its center
Google Cloud is Google's cloud platform. Like other clouds it rents servers, storage, and databases by use. It is best known for BigQuery, a data warehouse for fast analysis of large data sets, for container tooling, and for Google's AI models.
For an organization whose main need is understanding data, this matters. Loading data into a warehouse and asking questions of it can be much quicker than building the same capability on servers.
For ordinary application hosting, it competes closely with the other large clouds, so we choose between them on price, skills, and what you already use.
Where it fits
Google Cloud services we use most
| Service | What it does | When we use it |
|---|---|---|
| BigQuery | A data warehouse for fast analysis of large data sets. | Reporting, analytics, and data that outgrew a normal database. |
| Cloud Run | Runs containers without managing servers. | Web applications and APIs (the connections other programs use) with variable traffic. |
| Vertex AI | Access to Google's models and tools for building AI features. | AI features inside products or workflows. |
| Cloud Storage | Durable storage for files and data lakes. | Uploads, backups, and raw data. |
| Kubernetes engine | A managed cluster for many containers. | Larger platforms with several teams. |
Choosing the right approach
Query cost deserves attention
In a warehouse, cost often follows how much data each query reads. A careless query over a huge table can cost real money, and dashboards that refresh often can quietly add up.
We design tables and queries to read less, set limits and alerts, and review the bill monthly, so analysis stays affordable.
Options
What are you trying to do on Google Cloud?
Projects tend to fall into three kinds.
Which is closest to your project?
Choose this when
Reports take too long, data lives in many places, and questions go unanswered.
- What you get
- A data platform with a warehouse, regular loads, and dashboards.
- Keep in mind
- Agree what the measures mean first, or the platform will faithfully compute the wrong numbers.
Choose this when
A web application or API with variable traffic.
- What you get
- A container-based service that scales up and down with demand.
- Keep in mind
- Compare cost and effort with the alternatives before committing.
Choose this when
You want models that use your own information.
- What you get
- Models and data in the same environment, with access controls you already use.
- Keep in mind
- Test quality on your own examples, and plan what the model may see.
What DECHY can do
Working with Google Cloud
Design
- Architecture and cost estimates.
- Comparisons with AWS and Azure.
Build
- Data platforms with warehouses and pipelines.
- Applications on managed container services.
Secure
- Project structure, access, and encryption.
- Budgets and alerts.
Run
- Monitoring and incident response.
- Regular cost and design reviews.
Common questions
Questions about Google Cloud
Do we need Google Workspace to use Google Cloud?
No. They are separate products, though they share sign-in and work well together.
Is BigQuery right for our data?
It suits analysis of large data sets. For small data or heavy transactional use, a normal database such as PostgreSQL is often better. We choose by the workload.
Can you add AI to our data on Google Cloud?
Yes, using Google's models or others. We test quality on your examples and set limits on what the models can see.
How do we keep costs predictable?
Budgets, alerts, query limits, and a monthly review, with estimates before building.
Have a different question?
Tell us what you are weighing up and we will answer it plainly.
Ask a questionAlready a client? Email support@dechytech.com
Technical depth
How we build with Google Cloud
For your developers and IT lead: our Google Cloud choices on structure, data, and running workloads, and the reasoning behind each.
We use an organization with folders and separate projects for production and testing, with infrastructure described in code using Terraform, and identity through groups, not individual grants.
- Partitioned and clustered tables to limit what each query scans.
- Scheduled loads and transformations, with tests on data quality.
- Access to sensitive columns restricted by policy.
- Container services with Docker images, autoscaling, and private connections to databases.
- Logging and monitoring wired to alerts.
Tell us about the data and the workloads
Describe what you want to analyze or run. We will propose a Google Cloud design, a cost range, and an honest comparison with the alternatives.