Google Cloud
Platform Services
GCP environments stall when BigQuery costs spike, GKE clusters drift, and Vertex AI models go unserved.
Techgynt brings development-first GCP expertise to Indian businesses — we build your product and manage your Google Cloud infrastructure as one team. BigQuery pipelines, GKE deployments, Vertex AI production ML, Firebase apps, and continuous FinOps.
40%
Cloud cost saved
97%
ML model accuracy
90
Days to production
6+
Years experience
What Is GCP?
Google's enterprise cloud — built for data, AI, and scale
Google Cloud Platform (GCP) is Google's suite of cloud computing services covering over 150 products across compute, storage, data analytics, machine learning, and networking. GCP is the strongest choice for data-intensive and AI-first organisations — BigQuery for serverless data warehousing, GKE for Kubernetes orchestration, and Vertex AI for production ML workloads are unmatched in their class.
For Indian businesses building data platforms, AI products, or cloud-native applications, GCP offers competitive pricing through sustained use discounts and committed use discounts, along with Google's global fiber network for low-latency connectivity. Firebase — Google's app development platform — makes GCP particularly powerful for startups and SaaS products that need both a mobile/web backend and a scalable cloud infrastructure in one ecosystem.
Techgynt delivers end-to-end GCP consulting and development services for Indian businesses — from architecture design and migration to ongoing managed operations. Our team builds the application and manages the cloud, eliminating the gap between your development team and your infrastructure provider.
What We Do
Everything your business needs on Google Cloud
Turn raw data into decisions — fast
We design and operate BigQuery data warehouses built for analytics at scale. Slot optimisation, materialized views, partition pruning, and BI Engine caching keep your queries fast and your bill predictable. Connected to Dataflow, Pub/Sub, and Looker Studio for end-to-end data pipelines.
- BigQuery slot reservations & cost governance
- Dataflow + Pub/Sub ingestion pipelines
- Materialized views & partition pruning
- Looker Studio & BI Engine dashboards
- 30–40% cost reduction on typical workloads
BigQuery & Data Pipelines
Turn raw data into decisions — fast
Container infrastructure that runs itself
We configure GKE Autopilot for hands-off node management or Standard mode for GPU and specialized workloads. Workload identity, network policies, horizontal pod autoscaling, and GKE Gateway API for ingress — operated with SRE discipline. Cloud Run for fully serverless container workloads with zero infrastructure overhead.
- GKE Autopilot & Standard mode configuration
- Workload identity & network microsegmentation
- Horizontal pod autoscaling tuned to traffic
- Cloud Run serverless container deployments
- 24/7 cluster health monitoring
GKE & Cloud Run
Container infrastructure that runs itself
Ship ML models to production — not just notebooks
We deploy production machine learning with Vertex AI — training pipelines, model registry, online and batch prediction endpoints, and feature store management. Gemini API integration for LLM-powered features. Automated retraining, A/B testing, and drift monitoring so your models stay accurate after launch.
- Vertex AI training pipelines & model registry
- Online & batch prediction endpoints
- Gemini API integration for LLM features
- Model monitoring, drift detection & retraining
- GPU quota optimization for training jobs
Vertex AI & MLOps
Ship ML models to production — not just notebooks
Repeatable, automated deployments every time
We build GCP DevOps pipelines using Cloud Build for CI/CD, Artifact Registry for container and package management, Cloud Deploy for delivery pipelines, and Terraform modules for infrastructure-as-code. Every environment from development through production is consistent, version-controlled, and deployable in minutes.
- Cloud Build CI/CD pipelines
- Artifact Registry for containers & packages
- Cloud Deploy staged delivery pipelines
- Terraform IaC for all GCP resources
- Environment parity: dev → staging → prod
GCP DevOps & IaC
Repeatable, automated deployments every time
Full-stack GCP for web and mobile apps
Firebase sits at the intersection of GCP and your app — Authentication, Firestore, Realtime Database, Cloud Functions, and Hosting, all integrated with the wider GCP ecosystem. We build and maintain Firebase-powered backends for React, Next.js, Flutter, and React Native apps, scaling from zero to millions of users.
- Firebase Auth, Firestore & Realtime Database
- Cloud Functions for serverless backend logic
- Firebase Hosting with CDN & custom domains
- Integration with BigQuery & Cloud Storage
- Flutter & React Native app backend support
Firebase & App Infrastructure
Full-stack GCP for web and mobile apps
Secure by default. Optimised continuously.
We deploy Security Command Center for vulnerability and misconfiguration detection, Cloud Armor for WAF and DDoS protection, and VPC Service Controls for data exfiltration prevention. FinOps discipline covers Committed Use Discounts, sustained use discounts, spot VMs, and the GCP Recommender API — with monthly cost reports.
- Security Command Center setup & monitoring
- Cloud Armor WAF & DDoS protection
- VPC Service Controls & IAM hardening
- CUD, sustained use & spot VM optimisation
- Monthly FinOps report with savings breakdown
GCP Security & FinOps
Secure by default. Optimised continuously.
How We Compare
Techgynt vs. the alternatives
Our Process
From audit to production in 4 phases
01
GCP Audit
Review your current environment, workloads, and spend. Identify quick wins and target architecture. Deliverable: cost breakdown + recommendations.
1–2 weeks02
Architecture Design
Design the target GCP architecture — VPC layout, IAM hierarchy, BigQuery schema, GKE cluster sizing, and security controls.
1–2 weeks03
Build & Migrate
Execute migrations, build Terraform IaC, configure CI/CD pipelines, deploy GKE workloads, and wire up BigQuery pipelines.
4–8 weeks04
Operate & Optimise
Ongoing management: cluster ops, BigQuery slot tuning, model monitoring, security alerts, and monthly FinOps reporting.
OngoingIndustries We Serve on GCP
Why Techgynt
We build the app and manage the cloud — one team, no handoff
Most GCP managed services providers only run infrastructure. Your development team builds the features, then a separate provider manages the cloud — and the gap between them is where problems hide: misconfigured deployments, BigQuery schemas that nobody owns, Vertex AI models that never make it to production.
Techgynt closes that gap. We write the application code and manage the GCP environment it runs on. One team, one point of contact, one team accountable for both shipping features and keeping the infrastructure stable.
Faster debugging
We own the app code and the cloud config — no finger-pointing between teams when something breaks.
AI features that ship
We build the Vertex AI pipeline and the API endpoint that serves it — not two separate contractors.
BigQuery schemas we designed
We built the data models, so we know exactly how to optimise them — not reverse-engineering someone else's work.
Security across the stack
App-level and infrastructure-level security designed together, not bolted on after the fact.
FAQ
Frequently asked questions
What GCP services does Techgynt manage?
BigQuery, GKE, Cloud Run, Vertex AI, Firebase, Cloud Build, Artifact Registry, Cloud SQL, Cloud Storage, Security Command Center, Cloud Armor, and Terraform-based infrastructure-as-code. We cover the full platform, not just compute.
How is Techgynt different from a GCP managed services provider?
Most GCP managed services providers only run infrastructure — they don't write application code. Techgynt builds your product and manages your GCP environment as one team. No handoff between your dev team and an infrastructure provider.
Do you support Vertex AI and the Gemini API?
Yes. We deploy production ML workloads on Vertex AI including training pipelines, model registry, online and batch endpoints, and automated retraining. We also integrate Gemini API into products for LLM-powered features like document processing, chat, and search.
Can you migrate our workloads from AWS or Azure to GCP?
Yes. We use Migrate for Compute Engine for VM workloads, Database Migration Service for PostgreSQL and MySQL, and Transfer Service for storage. Every migration includes a pre-migration assessment, cutover rehearsal, and post-migration performance validation.
How do you reduce BigQuery costs?
We analyse query patterns, implement slot reservations vs on-demand pricing, add materialized views for repeated queries, enforce partition pruning and clustering, configure BI Engine caching for dashboards, and set per-project query quotas. Clients typically see 30–40% cost reduction within the first month.
Do you build Firebase apps alongside GCP backend services?
Yes. Firebase is part of the GCP ecosystem and we treat it as such — Authentication, Firestore, Cloud Functions, and Hosting integrated with BigQuery, Cloud Storage, and Vertex AI. We build Firebase-powered backends for React, Next.js, Flutter, and React Native apps.
Ready to build on Google Cloud?
Get a free GCP audit
for your business
We'll review your current environment, identify quick wins, and outline the architecture that moves you forward — no commitment required.