Google CloudDevelopment & Consulting
Build intelligent, data-driven applications on Google Cloud Platform. Our certified GCP architects help you leverage BigQuery, Vertex AI, and cloud-native services.
What is Google Cloud Development?
Google Cloud Platform offers a suite of cloud computing services running on the same infrastructure that Google uses for its own products. GCP excels in data analytics, machine learning, and Kubernetes-native workloads.
Key Capabilities
- Best-in-class data analytics with BigQuery
- Advanced AI/ML with Vertex AI
- Kubernetes-native with GKE
- Serverless computing with Cloud Run
- Global network infrastructure
- Open-source friendly ecosystem
Why Businesses Choose Google
Key benefits that drive business value and competitive advantage
Data Analytics Leader
BigQuery offers unmatched speed and scale for data analytics workloads.
AI/ML Excellence
Vertex AI provides the most advanced managed ML platform.
Kubernetes Native
GKE offers the most advanced managed Kubernetes from its creators.
Global Network
Google's private global network delivers low-latency performance.
Industry Use Cases
How leading companies leverage Google for competitive advantage
Customer Analytics & Personalization
Build real-time customer analytics and personalization engines using BigQuery and Vertex AI.
Key Benefits:
Technologies:
Media Processing & Delivery
Process, analyze, and deliver media content at global scale.
Key Benefits:
Technologies:
Game Backend Infrastructure
Scalable, low-latency game backends with global reach.
Key Benefits:
Technologies:
Healthcare Data Analytics
HIPAA-compliant healthcare analytics and AI solutions.
Key Benefits:
Technologies:
Our Google Cloud Expertise
Our GCP-certified team has delivered 55+ projects leveraging Google's data and AI capabilities.
Data Analytics
Build modern data platforms with BigQuery, Dataflow, and Looker.
AI & Machine Learning
Leverage Vertex AI for custom ML models and pre-built AI services.
Application Modernization
Modernize applications with GKE, Cloud Run, and serverless.
Data Engineering
Build scalable data pipelines and data lakes on GCP.
Technology Stack
Tools, frameworks, and integrations we work with
Core Tools
Integrations
Frameworks
Success Stories
Real results from our Google projects
Real-Time Analytics Platform
Challenge:
An e-commerce company needed to analyze billions of events daily for real-time personalization and business intelligence.
Solution:
We built a real-time analytics platform using Pub/Sub for ingestion, Dataflow for processing, and BigQuery for analytics. Looker provides self-service BI.
Results:
- 10B+ events processed daily
- Real-time personalization
- 60% cost reduction vs legacy
- 100x faster queries
Technologies Used:
ML-Powered Content Moderation
Challenge:
A social platform needed to automatically moderate user-generated content at scale while minimizing false positives.
Solution:
We developed a custom ML pipeline on Vertex AI combining Vision AI for images, Video Intelligence for videos, and custom NLP models for text, processing millions of items daily.
Results:
- 5M+ items moderated daily
- 95% accuracy achieved
- 80% reduction in manual review
- 10x faster moderation
Technologies Used:
Engagement Models
Flexible engagement options to match your project needs
GCP Assessment
Evaluate your workloads and create a GCP migration or optimization roadmap.
Includes:
- Workload assessment
- Cost analysis
- Architecture design
- Migration plan
Organizations evaluating GCP
GCP Implementation
End-to-end GCP solution implementation and migration.
Includes:
- Architecture
- Implementation
- Data migration
- Training
New GCP projects
Data Platform Build
Build modern data platforms on GCP with BigQuery and related services.
Includes:
- Data architecture
- Pipeline development
- BI implementation
- ML integration
Data-centric initiatives
Frequently Asked Questions
Why choose Google Cloud over AWS or Azure?
GCP excels in data analytics (BigQuery is unmatched), AI/ML (Vertex AI, TensorFlow), and Kubernetes (GKE from Kubernetes creators). It's often preferred for data-intensive workloads, ML projects, and organizations wanting a more open-source friendly cloud. We help you evaluate based on your specific needs.
How does BigQuery compare to other data warehouses?
BigQuery offers unique advantages: serverless architecture (no infrastructure management), separation of storage and compute, near-unlimited scale, and built-in ML. It's typically faster and more cost-effective for analytics workloads. The pay-per-query model is great for variable workloads.
Can you help migrate from AWS or Azure to GCP?
Yes, we have experience with multi-cloud and cloud-to-cloud migrations. We assess your current infrastructure, design the target architecture, and execute phased migrations. For some clients, we implement multi-cloud strategies using Anthos for consistent management.
What about GCP for AI/ML workloads?
GCP is excellent for AI/ML. Vertex AI provides a unified platform for the entire ML lifecycle. You can use AutoML for no-code solutions or custom training for advanced models. Integration with BigQuery ML allows ML directly in your data warehouse. Pre-built AI APIs (Vision, Language, etc.) accelerate development.
How do you optimize GCP costs?
We implement committed use discounts, right-size resources, use preemptible/spot VMs for suitable workloads, optimize BigQuery with partitioning and clustering, and set up budgets and alerts. Typical savings are 30-50% compared to on-demand pricing.
Ready to Build on Google Cloud?
Leverage the power of Google's infrastructure for your data and AI initiatives. Let's discuss your GCP project.