Google Cloud Development
Achieve seamless connectivity, rapid computation, secure storage, and effortless scalability with Google Cloud Platform. Experience reliable performance, high availability, and enhanced security with Google Cloud Development.
Google Cloud Delivery Snapshot
BigQuery, Dataflow, Pub/Sub — Google's native advantage
Vertex AI, Gemini, AutoML — production-ready ML
Terraform + Cloud Build — every resource version-controlled
Cloud Run — zero cold starts, scale to zero
"Ahex migrated our analytics stack to BigQuery and built our entire ML pipeline on Vertex AI in 6 weeks. Our data team went from hour-long Redshift queries to sub-second BigQuery results. The Vertex AI pipeline now retrains our recommendation model every night without a single engineer touching it."
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Customized Google Cloud Development Services
Ahex Technologies is your go-to partner for Google Cloud Platform engineering. With deep expertise across GKE, Cloud Run, Cloud Functions, Cloud SQL, Spanner, Bigtable, Firestore, BigQuery, Dataflow, Pub/Sub, Vertex AI, Gemini API, Firebase, and the full GCP networking and security stack, we design and deploy Google Cloud architectures that are data-first, AI-native, and globally scalable from day one.
Our GCP services span the full cloud stack — from single Cloud Run deployments for containerised APIs to multi-region GKE clusters with BigQuery data warehouses, Vertex AI ML pipelines, Dataflow streaming ETL, Pub/Sub event-driven microservices, Firebase real-time back-ends, and Looker BI dashboards. Whether you are migrating from AWS or on-premise, building a data-intensive SaaS product on Google’s data platform, or deploying production AI/ML workloads on Vertex AI, our certified GCP engineers deliver architectures that pass the Google Cloud Well-Architected Framework review.
Engineering
GCE · Functions
Deployment Manager
Auth · Hosting
Vertex AI · Dataflow
Spanner · Firestore
Dataflow · Bigtable
BigQuery, Vertex AI, and Global Infrastructure: The Foundation Behind Every Data-Driven Cloud Architecture
Google Cloud is the platform that data-first and AI-first engineering teams converge on — BigQuery is the world’s most powerful serverless data warehouse, Vertex AI is the most complete managed ML platform, Cloud Run offers the industry’s best serverless container experience with true scale-to-zero, and Google’s private global fibre network delivers lower latency than any public internet routing path.
BigQuery Is the World's Most Powerful Serverless Data Warehouse
BigQuery queries petabytes in seconds with no infrastructure to manage — no cluster sizing, no vacuuming, no index tuning. Storage and compute are separated, so you pay only for the queries you run. Every eCommerce, SaaS, and analytics team that moves from Redshift or Snowflake reports the same result: queries that took 45 minutes now take 4 seconds.
Vertex AI Is the Most Complete Managed ML Platform
Vertex AI pipelines, Feature Store, Model Registry, Model Monitoring, and Prediction endpoints — the full MLOps lifecycle on managed infrastructure. Teams running Jupyter notebooks on EC2 move to production ML inference in days, not months, with Vertex AI's managed training and serving.
Cloud Run Offers True Scale-to-Zero Serverless Containers
Cloud Run deploys any containerised application with zero cold-start penalty, scales from zero to thousands of instances in seconds, and charges only for actual request processing time — the only serverless container platform that genuinely scales to zero with no standby cost.
Google's Private Global Fibre Network
Traffic between Google Cloud regions travels over Google's private undersea fibre network, not the public internet — inter-region latency is consistently 30–50% lower than equivalent AWS or Azure cross-region routing, with no cross-region data transfer surprises for traffic that stays on the Google backbone.
Pub/Sub and Dataflow for Real-Time Data Pipelines
Google Cloud Pub/Sub is the most reliable managed message queue on any cloud — guaranteed at-least-once delivery, exactly-once semantics, and native integration with Dataflow for serverless Apache Beam streaming and batch ETL at petabyte scale.
Chosen by Spotify, Twitter/X, HSBC, The New York Times, PayPal, and Snap — and the foundation of Google's own internal infrastructure, making GCP the only cloud built by and continuously validated by the world's largest web-scale engineering organisation.
Our Google Cloud Development Services
From GKE and Cloud Run deployments to BigQuery data warehouses, Vertex AI ML pipelines, Cloud SQL and Firestore databases, Pub/Sub event streaming, Firebase back-ends, and full AWS or on-premise migrations to GCP.
GKE & Cloud Run Deployments
Production-grade Google Cloud compute — GKE Autopilot and Standard mode Kubernetes clusters with Helm and ArgoCD, and Cloud Run for stateless containerised services with true scale-to-zero and zero infrastructure management overhead.
GKE Autopilot — no node management, per-pod billing
GKE Standard — node pools, Workload Identity, KEDA autoscaling
Cloud Run — scale-to-zero, HTTP/2, no cold-start penalty
Artifact Registry — private container image hosting
Multi-region with Cloud Load Balancing and Traffic Director
BigQuery & Data Platform
End-to-end BigQuery data platforms — dataset design, table partitioning and clustering, Data Transfer Service ingestion, Dataform SQL pipelines, Looker Studio BI dashboards, and real-time streaming inserts via Pub/Sub and Dataflow.
BigQuery dataset design — partitioning, clustering, access controls
Dataform — SQL-based transformation pipelines, dbt equivalent
BigQuery ML — ML models trained and served in SQL
Looker Studio — live BigQuery dashboards with scheduled refresh
Vertex AI & ML Pipelines
End-to-end ML engineering on Vertex AI — Kubeflow Pipelines for orchestration, Vertex AI Feature Store, Model Registry with versioning, batch and online prediction endpoints, Model Monitoring for drift detection, and Gemini API integration for generative AI features.
Vertex AI Pipelines — Kubeflow / TFX component orchestration
Vertex AI Feature Store — centralised, low-latency feature serving
Gemini API — generative AI features with VPC Service Controls
Model Monitoring — drift detection, skew alerts, retraining triggers
Cloud SQL & Firestore
GCP managed relational and NoSQL databases — Cloud SQL PostgreSQL and MySQL with high-availability failover replicas, Cloud Spanner for globally distributed ACID transactions at any scale, and Firestore for real-time document sync in mobile and web applications.
Cloud SQL PostgreSQL / MySQL — HA failover, read replicas, PITR
Cloud Spanner — globally distributed ACID, 99.999% SLA
Firestore — real-time sync, offline support, sub-10ms reads
Memorystore for Redis — session cache, leaderboards, Pub/Sub
Pub/Sub & Dataflow Streaming
Real-time event streaming and ETL on Google Cloud — Pub/Sub topics and subscriptions for decoupled microservices, Dataflow Apache Beam pipelines for streaming and batch data processing, and BigQuery streaming inserts for real-time analytics on live data.
BigQuery streaming inserts — real-time data ingestion at scale
Dataflow — Apache Beam streaming and batch ETL, auto-scaling
Eventarc — Cloud Run triggers from Pub/Sub, Cloud Storage events
React Hook Form + Zod resolver integration
Firebase & Mobile Back-End
Firebase back-end services for mobile and web applications — Firestore real-time database, Firebase Authentication with social and custom providers, Firebase Hosting for React/Angular SPAs, Firebase Cloud Messaging for push notifications, and App Check for abuse prevention.
Firestore — real-time sync, offline support, security rules
Firebase Auth — Google, Apple, email/password, custom tokens
App Check + Firebase Security Rules — abuse prevention
Husky pre-commit type-check hook
AWS / Azure Migration to GCP
On-premise, AWS, or Azure workloads migrated to Google Cloud — application migration to GKE or Cloud Run, database migration to Cloud SQL or Spanner via Database Migration Service, data warehouse migration to BigQuery, and zero-downtime DNS cutover.
AWS EC2 + RDS → GKE / Cloud Run + Cloud SQL
AWS Redshift / S3 → BigQuery (data warehouse migration)
Zero-downtime DNS cutover with Cloud DNS weighted routing
Turnaround: full audit report in 5–7 days
Terraform & Cloud Build IaC
Every GCP resource managed as reviewed code — Terraform modules using the Google provider for reusable infrastructure patterns, Cloud Build pipelines for plan-then-apply workflows, and Workload Identity Federation for keyless CI authentication.
Terraform Google provider — GKE, Cloud SQL, BigQuery, IAM modules
Cloud Build — trigger on push, terraform plan + apply pipeline
GCS remote state backend with state locking
Code review sessions on real team PRs
Hire Google Cloud Engineers You Can Rely On
At Ahex Technologies, we don’t just write code — we own outcomes. From type architecture to post-launch monitoring, our GCP team is your end-to-end cloud engineering partner — responsive, transparent, and accountable.
3–5 days to onboard your dedicated GCP engineer
Senior GCP — GKE, Cloud Run, BigQuery, Vertex AI, Cloud SQL, Firestore, Terraform, and Cloud Buildpes
Direct Slack access to your actual engineer — no account managers
Named, consistent developer — no bait-and-switch
Full code ownership from day one — no lock-in
Timezone-aligned — UK, UAE, and US hours coverage
2-week replacement guarantee if it's not the right fit
Ahex Google Cloud Infrastructure Standard
3–5 days
100%
Day 1
2 weeks
3 zones
// Signed
Secure by Default. Well-Architected by Design.
The following are the GCP security, compliance, data governance, and infrastructure quality standards we engineer into every deployment — built from the Google Cloud Well-Architected Framework and BeyondCorp Zero Trust principles, applied from day one.
Workload Identity Always — No Service Account Keys in Code
Every GCP workload Ahex deploys uses Workload Identity Federation — GKE pods via Workload Identity, Cloud Run via service account impersonation, Cloud Build via Workload Identity Federation — zero service account key files stored anywhere. (prev: no implicit any, no unsafe assignments, no unchecked indexed access.
Branded / Opaque Types
Cloud SQL, Memorystore, and internal APIs are placed inside a VPC with no public IP — reachable only from authorised service accounts and VPC-native GKE pods. Cloud SQL Auth Proxy enforces IAM authentication without exposing the database port.
Zod Input Validation
Google Cloud KMS customer-managed encryption keys for BigQuery datasets, Cloud Storage buckets, Cloud SQL instances, and Secret Manager secrets. Data at rest is always encrypted; keys are rotated on a defined schedule.
Discriminated Union Errors
Cloud SQL automated daily backups with 7-day PITR, GCS versioned object storage for application data, Firestore daily export to Cloud Storage — tested restore procedures documented and validated against RTO/RPO targets before every production go-live.
Dependency Auditing
Google Cloud Security Command Center (SCC) continuously assesses misconfigurations and vulnerabilities across the GCP project — findings reviewed weekly, critical findings trigger PagerDuty alerts within minutes.
CI Type-Check Gate
Cloud Armor WAF rules protect Cloud Load Balancing and Cloud Run endpoints — OWASP Top 10 managed rule groups, rate limiting, geo-blocking, and custom rules for application-specific threat patterns.
Built to Compliance & Industry Standards
Our GCP engineering practices align with regulatory requirements across healthcare, finance, and data privacy — a typed codebase is also an auditable one.
HIPAA — GCP HIPAA-Eligible Services
PHI workloads are deployed on HIPAA-eligible GCP services with BAA in place — Cloud SQL and Cloud Storage encrypted with KMS, VPC Service Controls perimeter, Cloud Audit Logs, and Security Command Center alerts for PHI access. prev: PHI from non-sensitive data at the type level — misuse flagged at compile time, not discovered in an audit.
Healthcare platforms, patient portals, HL7/FHIR integrations
PCI DSS Type Patterns
Opaque CardNumber and CVV types prevent raw payment strings being passed through un-validated code paths — enforced by the compiler, not just policy.
Payment platforms, financial dashboards, trading portals
GDPR — Data Residency & Region Locking
PII is stored only in private-subnet RDS or DynamoDB — no PII in S3 public buckets, no PII in CloudWatch logs, and IAM policies prevent cross-account access to PII data stores models — accidental exposure of personal data caught before runtime in production.
EU, UK, and India data-privacy regulated products
SOC 2 — CloudTrail & Config Audit
Typed event schemas ensure every audit log entry has a known, validated shape — no untyped JSON blobs in the compliance trail.
SaaS products targeting enterprise buyers with security reviews
OWASP — Cloud Armor WAF Rules
Google Cloud Armor WAF configured with OWASP ModSecurity Core Rule Set — SQL injection, XSS, RCE, and scanner detection rules active on all Cloud Load Balancing and Cloud Run endpoints with adaptive protection enabled for DDoS mitigation.
All GCP workloads with Cloud Load Balancing or Cloud Run fronting internet traffic
PCI DSS — Cardholder Data Environment
GCP is PCI DSS Level 1 certified. Ahex scopes cardholder data environments using dedicated VPC subnets, VPC Service Controls perimeters, Cloud Armor, Cloud KMS, and Cloud Audit Logs — isolating payment workloads from other GCP services in the same project.
All GCP deployments processing or storing payment card data
ISO 27001 & GCP Well-Architected
Google Cloud Well-Architected Framework review across all pillars before go-live, Security Command Center continuous posture scoring, and Organisation Policy constraints preventing non-compliant resource creation — ISO 27001 control mapping documented per deployment.
Enterprise clients requiring certified development processes
Secret Manager & Workload Identity
All secrets stored in Google Cloud Secret Manager with IAM-controlled access — Cloud Run, GKE pods, and Cloud Functions retrieve secrets at runtime via Workload Identity, never via key files or environment variable injection. Secret versions rotated without application restarts.
All GCP workloads with database credentials, API keys, or secrets
GCP Cost Optimisation & Committed Use
Resource labelling policy for project, team, and environment attribution. Cloud Billing budget alerts at 80% and 100%, Committed Use Discounts for predictable GKE and Compute Engine workloads, BigQuery slot reservations for steady query workloads, and monthly cost review against GCP Recommender rightsizing suggestions.
All production GCP projects with monthly spend above $1,000
The Full GCP Stack, Operated at Depth
From GKE, Cloud Run, and Cloud Functions to BigQuery, Vertex AI, Pub/Sub, Dataflow, Cloud SQL, Firestore, Secret Manager, Cloud Armor, Terraform, and Cloud Build — every GCP service and tool our team operates daily in production.
Compute
GCP compute layer
Data & Analytics
GCP data platform
AI & ML
GCP AI/ML platform
Databases & Storage
GCP data layer
Security & Identity
GCP security layer
CI/CD & DevOps
GCP delivery pipeline
Observability
GCP monitoring and tracing
IaC & Networking
GCP resource management
Google Cloud vs AWS vs Azure vs DigitalOcean — An Honest Cloud Comparison
We deploy on all four. We give honest advice — including recommending AWS for the broadest compliance catalogue, Azure for Microsoft-first organisations, and DigitalOcean for cost-sensitive developer teams that need simplicity above all else.
| Criteria | Google Cloud (GCP) | AWS / Azure | DigitalOcean / Render |
|---|---|---|---|
| Data warehouse | BigQuery — serverless, petabyte-scale, sub-second queries, no cluster management | AWS Redshift (cluster-based); Azure Synapse Analytics | No native data warehouse |
| AI/ML platform | Vertex AI — most complete MLOps, Gemini API, AutoML, Feature Store | AWS SageMaker (widest reach); Azure OpenAI (best GPT integration) | No native ML services |
| Serverless containers | Cloud Run — true scale-to-zero, no cold start, per-request billing | AWS App Runner / Fargate (no true scale-to-zero); Azure Container Apps | Render web services — similar DX to Cloud Run |
| Kubernetes (managed) | GKE — most automated, Autopilot mode, best GKE upgrade UX | EKS: most powerful, complex IAM; AKS: best Kubernetes DX on Azure | DOKS: simplest; no Kubernetes on Render |
| Network performance | Google private fibre backbone — lowest inter-region latency of any cloud | AWS global backbone (excellent); Azure global network (competitive) | Standard internet routing — higher latency |
| Firebase / mobile back-end | Firebase — unique, first-class real-time mobile back-end with Auth, Firestore, FCM | AWS Amplify (similar concept, less mature); Azure Static Web Apps | No native mobile back-end |
| Enterprise compliance | Strong — ISO 27001, SOC 2, PCI DSS, HIPAA, FedRAMP High, GDPR | AWS: broadest (140+ certifications); Azure: strongest UK/EU government | SOC 2 — sufficient for most SMB |
| Ahex recommendation | Best for: data-intensive SaaS, AI/ML workloads, BigQuery analytics, Firebase mobile apps | AWS: enterprise compliance/scale; Azure: Microsoft-first orgs | Best for: cost-conscious dev teams, simple infrastructure |
Type Contract First. Shared Architecture. Well-Architected, BeyondCorp Zero Trust & IaC-Managed Throughout.
A GCP-specific process — Well-Architected design, IAM strategy, and disaster recovery plan agreed before a single resource is provisioned. Zero Trust and data governance enforced at every phase, not compiler config defined before a single component is built. Safety enforced from sprint zero, not patched in retrospect.
Architecture & Well-Architected Design
AWS architecture design against the five Well-Architected pillars — compute sizing, multi-AZ strategy, database engine selection, IAM design, VPC CIDR and subnet planning, and DR RTO/RPO targets agreed before any resource is provisioned.
Terraform Foundation & Security Baseline
AWS account structure (Organisations + SCPs), Terraform workspace with S3 remote state and DynamoDB locking, VPC with public/private/isolated subnets across 3 AZs, IAM roles, GuardDuty, CloudTrail, Config, and Security Hub enabled before any application resource is provisioned.
Compute, Database & CI/CD
ECS Fargate or EKS cluster, RDS/Aurora in private subnets with Multi-AZ, ElastiCache Redis, ECR private registry, GitHub Actions pipeline with Trivy scan → ECR push → ECS deploy. Secrets in Secrets Manager, config in SSM Parameter Store.
CDN, WAF, Monitoring & Alerting
CloudFront distribution with OAC, WAF with AWS Managed Rules, ACM certificates, CloudWatch dashboards and alarms, X-Ray distributed tracing, Budget alerts at 80%/100% monthly threshold, and AWS Backup automated snapshots.
Well-Architected Review & Load Testing
AWS Well-Architected Tool review against all five pillars, Security Hub findings remediated, k6 load test at 2× expected peak traffic, RDS failover drill, Lambda cold-start benchmarks, and Cost Explorer review before go-live sign-off.
Monitoring, Patching & Cost Optimisation
Monthly Cost Explorer review and rightsizing recommendations, Savings Plans evaluation, Trusted Advisor findings, Security Hub continuous compliance, ECS task definition and AMI patch cycles, and Terraform state audit to confirm all live resources are tracked in code.
Three Ways to Work With Ahex Google Cloud Engineers
All models include IaC Terraform workspaces, documented GCP runbooks, named engineers, and full code ownership from day one.
FIXED BUDGET
Cost is locked in a fixed-scope model. Ideal when the roadmap is well-defined and you want budget certainty.
- Pay for specific milestones
- Pricing based on requirements
- Fixed delivery date
- Low management overhead
Billing
Fixed / Milestone
Specific, well-defined scope
Best For
A clear brief → build → ship
Dedicated Team
A dedicated pod you optimise, scale, and augment your in-house team with. Best for ongoing product development.
Best suited for teams that need predictable sprint velocity.
- Personalised team
- No lock-in
- Real-time updates
- Contributions & standups
Billing
Monthly retainer
Best For
Long-term, cost-optimised delivery
Model Fit
Team that owns the roadmap
Most Popular
TIME & MATERIAL
In this model, there is no fixed time or budget. You will pay for the actual hours worked or materials completed and used.
- Volume of your time
- Discuss and understand
- Pay per task
- Full flexibility on scope
Billing
Hourly rate card
Speed, scope, resource coordination
Best For
Evolving, flexible scope
Why Choose Google Cloud Development
Your teams will ship faster, safer code — and your production systems will have faster data pipelines and smarter products — when GCP is architected correctly from day one.
BigQuery Changes Data Analysis From Hours to Seconds
Queries that took 45 minutes on Redshift or Snowflake take 3–8 seconds on BigQuery. No vacuuming, no cluster sizing, no index maintenance — just SQL against petabyte datasets with automatic query optimisation and built-in column-based storage.
Vertex AI Takes ML From Notebook to Production
Vertex AI Pipelines, Feature Store, Model Registry, and online prediction endpoints give data science teams a structured path from Jupyter notebook experiments to production-grade ML inference — without managing Kubernetes or MLflow infrastructure themselves.
Cloud Run Eliminates Server Management for Stateless APIs
Cloud Run deploys any containerised API with zero server management, true scale-to-zero, automatic TLS, and per-request billing — teams that moved from EC2 or GCE to Cloud Run consistently cut compute costs by 40–70% for variable-traffic APIs.
Firebase Gives Mobile Teams a Fully Managed Back-End
Firestore real-time sync, Firebase Authentication, Cloud Messaging, and App Check — mobile and web teams launch production-quality back-ends in days rather than weeks, without provisioning a single database server or writing authentication middleware.
Gemini API Brings Google's Best AI to Your Application
Vertex AI Gemini API provides access to Gemini 1.5 Pro and Flash — Google's most capable foundation models — with VPC Service Controls for data residency, enterprise IAM access control, and competitive per-token pricing. Build AI features without managing model infrastructure.
Google's Private Global Fibre Network Delivers Lower Latency
Inter-region traffic on Google Cloud travels over Google's private undersea fibre backbone — not the public internet. London-to-Singapore latency on GCP is consistently 30–50% lower than equivalent AWS or Azure routing, with no cross-region transfer billing surprises for backbone traffic.
GKE Autopilot Eliminates Node Management Entirely
GKE Autopilot manages node provisioning, patching, and scaling — you deploy workloads, GKE handles the nodes. Per-pod billing means you pay only for what your containers actually use, with no idle node capacity cost between traffic peaks.
Google Cloud Well-Architected Framework Reviews Every Deployment
Google's five-pillar Well-Architected framework — Operational Excellence, Security, Reliability, Performance Efficiency, and Cost Optimisation — gives engineering teams a structured review process. Ahex engineers every deployment to pass the Well-Architected assessment before go-live.
We Type Faster with AI — So You Ship Sooner
Our GCP engineers use AI-powered tools across every phase — from type migration to test generation — without sacrificing type safety or code quality. The result: more output, fewer delays, the same rigorous strictness.
55%
Faster Type Migration
AI generates Zod schemas from JSON samples, infers types from existing JS, and suggests typed replacements for any casts — saving 2–3 days per migration sprint.
40%
Reduction in Review Cycles
AI-assisted code review flags unsafe type patterns, missing return types, and any-cast misuse before human review — fewer back-and-forth cycles and faster PR merges.
70%
Test Coverage Generated Automatically
k6 load test generation, Terraform plan analysiss auto-generated from Zod schemas and function signatures — QA phase starts with strong coverage.
30 %
Shorter Overall Delivery Time
Combined AI acceleration across all phases consistently cuts total delivery timelines by 25–35% without scope compromise.
GitHub Copilot + Claude Code
Inline Terraform Google provider completion, Cloud Build YAML generation, IAM policy JSON suggestions, and BigQuery SQL query optimisation. Every engineer's daily driver for GCP infrastructure and data work.
Terraform GCP Resource Generation
AI generates Terraform resource blocks, Cloud Build pipeline YAML, Dataform SQL transformations, and Vertex AI pipeline component definitions — 50% of infrastructure and data pipeline scaffolding done before the first terraform apply, reviewed by a certified GCP engineer.
AI-Generated Architecture Docs
GCP architecture decision records, runbooks, BigQuery data dictionaries, and Vertex AI model cards auto-generated from Terraform state and GCP resource metadata — always in sync with the deployed infrastructure.
AI Security Findings Triage
AI-assisted Trivy and Security Command Center findings triage surfaces container CVEs and GCP misconfigurations with remediation suggestions — engineers review every finding before merging. Shift-left security for every GCP deployment.
All AI-generated Terraform, Cloud Build YAML, and Dataform SQL is reviewed, tested, and owned by a named Ahex engineer before it ships. We use AI to move faster — not to skip the Well-Architected review or compromise data governance standards.
Six Google Cloud Problems Every Engineering Team Encountersg Team Encounters
Every team building on GCP hits these sooner or later. These are the problems our engineers diagnose repeatedly and know how to prevent from sprint zero.
BigQuery Query Costs Exploding — $800 Bill From a Single Accidental Full Table Scan
Problem
A data analyst runs a SELECT * with no WHERE clause against a 2TB BigQuery table. The query processes all 2TB and costs $10. Then it happens again, 80 times across the team in one month. The BigQuery bill is $800 for a dataset that should cost $30.
Solution
Ahex configures BigQuery table partitioning on the event_date column, clustering on user_id, adds a maximum bytes billed setting of 10GB per query in the project IAM policy, and sets up per-user byte quota alerts. The next month's BigQuery bill is $28.
Cloud Run Service Returning 502 After Deployment — No Error in Logs
Problem
A Cloud Run service starts returning 502 errors immediately after a new image is deployed. Cloud Run logs show the container starts successfully. No application-level error appears. The team has rolled back twice and the 502s persist — even the previous revision is now broken.
Solution
Ahex identifies the root cause — the Cloud Run service account had its Secret Manager access permission revoked during an IAM cleanup. The container starts but crashes silently on the first Secret Manager API call. Ahex re-grants secretmanager.secretAccessor to the service account and adds a Cloud Run startup probe to surface secret access failures as container startup errors in future.
Vertex AI Training Pipeline Failing Intermittently With No Useful Error
Problem
The nightly Vertex AI training pipeline fails 3 times out of 5 runs. The pipeline log shows "Worker pool 0 failed" with no further context. The team has been re-running the pipeline manually for two weeks and cannot identify whether the failure is the data ingestion component, the training component, or the evaluation component.
Solution
Ahex adds component-level logging to the Kubeflow pipeline, identifies the training component is running out of memory on 30% of nightly datasets, increases the training machine type from n1-standard-4 to n1-highmem-8 for the training component only, and adds a pipeline run retry policy — the pipeline has run successfully every night for the following 6 weeks.
GKE Cluster Out of Capacity During Traffic Spike — Pods Pending
Problem
A product launch drives 15× normal traffic. GKE pods scale up via HPA but node pool capacity runs out — new pods are stuck Pending for 8 minutes waiting for Cluster Autoscaler to provision new nodes. Some users are seeing timeouts during that window.
Solution
Ahex reconfigures the node pool with a minimum size of 3 (up from 0), enables GKE Autopilot for the stateless API workloads to eliminate node pool management entirely, and sets a PodDisruptionBudget ensuring at least 50% of pods remain available during autoscaling events — subsequent traffic spikes scale in under 90 seconds with zero timeouts.
Cloud SQL Connection Exhausted — Too Many Connections Error From GKE
Problem
Under a load test of 300 concurrent users, the application starts returning database connection errors. GKE has 20 pods, each opening 15 direct connections via Cloud SQL Auth Proxy — 300 total, exceeding the Cloud SQL PostgreSQL instance max_connections limit of 200. Queries fail for all users during peak traffic.
Solution
Ahex deploys PgBouncer as a sidecar container in the GKE pods in transaction pooling mode — all 300 application connections multiplex through 25 PgBouncer connections to Cloud SQL. The Cloud SQL instance now supports 500+ GKE pod connections through 25 database connections, and the load test at 3× previous peak traffic completes with zero connection errors.
GCP Infrastructure Not in Terraform — Console Drift and No Audit Trail
Problem
The GCP project was built by clicking through the console over 12 months. There is no Terraform code — 40+ resources across GKE, Cloud SQL, Cloud Run, IAM service accounts, and Cloud Storage buckets all created manually. The team cannot reproduce the environment and does not know what a colleague changed last Tuesday that briefly broke Cloud Run.
Solution
Ahex uses the GCP API and gcloud export to reverse-engineer all existing resources into Terraform code, runs terraform import for each resource, validates with terraform plan showing zero drift, and connects the workspace to a Cloud Build trigger on the infrastructure repository — all future changes are reviewed pull requests, not console clicks, with a full Cloud Audit Log audit trail.
What We Build Best — Real Expertise, Not Just Slides
Six solution types where our GCP engineers have deep, repeated delivery experience — every stack listed is what we shipped in production in the last 18 months.
GKE & Cloud Run Deployments
Containerised Node.js, Python, Go, and Java on GKE Autopilot and Cloud Run — multi-region, Cloud Load Balancing, and Cloud Build GitOps. SPAs with strict tsconfig, generics-first component design, typed state management (NgRx / Zustand), and Zod-validated API layers across the UI.
NestJS / Express Back-End APIs
Fully typed REST and GraphQL APIs with NestJS dependency injection, Prisma typed models, Zod request validation middleware, and tRPC for end-to-end type safety.
Nx / Turborepo Monorepos
Multi-package monorepos with shared @company/types, shared tsconfig bases, ESLint boundary rules, and Nx affected builds that cut CI time by ~60%.
BigQuery Data Platform Builds
End-to-end BigQuery data warehouse migrations using allowJs incremental strategy, type-coverage audits, any-elimination phases, and strict mode graduation — production stays deployable throughout.
Vertex AI ML Pipelines
Production ML pipelines from Jupyter notebook to Vertex AI — AWS and Vercel SageMaker and Redshift workload migration to Vertex AI and BigQuery, model training and prediction endpoint deployment — Zod-validated payloads, and cold-start optimised bundles under 1MB.
Firebase Mobile Back-Ends
Complete Firebase back-ends for iOS and Android — Firestore, Auth, Cloud Messaging, App Check with shared types in a monorepo, single CI/CD pipeline, tRPC or OpenAPI contracts, and one team owning the entire stack from DB to UI.
Industry Standards for Enterprise-Grade Compliance & Standards
The following are the industry standards and compliance that we align Google Cloud with. Our team ensures that these are built into the markup from sprint one only.
Web Content Accessibility Guidelines
WCAG 2.1
AI accessibility scanning flags WCAG violations in real time during development — not post-launch in an audit.
- Required for: Government, Education, Healthcare, Finance
US Federal Accessibility
Section 508
Section 508 for the USA. An U.S. federal accessibility standard that requires government agencies and their digital services to be accessible to people with disabilities.
- Required for: US Government contractors
Americans with Disabilities Act
ADA
A U.S. civil rights law. It promotes the idea that people with disabilities should also have equal access. Its web accessibility requirements encourage businesses to provide inclusive online experiences.
- Required for: All US-facing businesses
Cookie Consent & Data Privacy
GDPR
Standards that help websites collect user data transparently. Supports GDPR and CCPA. Gives users control over their data.
- Required for: EU-facing businesses
W3C GCD
W3C
W3C GCD Validation ensures that the GCD development follows official web standards. It must improve compatibility with browsers, reliability, and overall user experience.
- Best practice for: All websites
Structured Data Markup
Schema.org
Standardized format that helps search engines understand content on the webpages. Improves SEO and crawlability.
- Required for: SEO, Google rich results
Industries We Serve with Google Cloud
We deploy and manage Google Cloud infrastructure for product companies across all major verticals — from healthcare typed APIs to fintech platforms, logistics systems to SaaS products. Click an industry to explore what we've delivered.
Healthcare and Fitness
Our solutions for healthcare and fitness focus on developing user-friendly interfaces for fitness apps, appointment scheduling systems, and health tracking platforms, ensuring secure and efficient data management.
- HIPAA-compliant patient portals
- Cross-device fitness UI experiences
- AI-powered health dashboards
- Real-time telehealth interfaces
Real Estate
We help real estate companies build immersive property listings, interactive maps, and responsive websites that streamline property searches and improve customer engagement.
- GIS-enabled property mapping
- AR/VR property walkthroughs
- CRM-integrated listing portals
- Real-time property analytics
Automotive and Manufacturing
Our front end services help automotive and manufacturing companies build robust applications for managing inventory, tracking production, and enhancing customer engagement through intuitive interfaces.
- IoT-powered monitoring dashboards
- MES-integrated production systems
- Predictive maintenance interfaces
- Supply chain visibility platforms
Banking & Finance
We deliver secure and compliant front-end solutions for financial institutions, enhancing user experience through intuitive dashboards, transaction management systems, and mobile banking apps.
- PCI-DSS compliant interfaces
- AI-driven financial dashboards
- Secure payment interfaces
- Live transaction monitoring UIs
Travel & Hospitality
Our frontend development services for tourism and hospitality focus on creating interactive maps, virtual tours, and streamlined booking interfaces that enhance the customer journey from discovery to booking.
- API-driven booking engines
- Dynamic pricing dashboards
- AI-powered recommendation interfaces
- Multi-channel reservation systems
Entertainment and Media
We help media and entertainment companies build intuitive systems for content delivery and consumption, including real-time single-page applications and personalized content recommendations that keep audiences engaged.
- OTT streaming interfaces
- Real-time content delivery platforms
- Dynamic recommendation experiences
- Live media analytics dashboards
Technology and Software
Our expertise extends to creating modern, scalable front-ends for software applications, ensuring fast performance, intuitive navigation, and seamless integration with backend systems.
- SaaS product dashboards
- Microservices-based frontends
- API-first web interfaces
- Mobile app UIs
Retail & E-Commerce
We empower e-commerce platforms with seamless checkout processes, intuitive product navigation, and responsive designs that boost sales and customer satisfaction.
- Dynamic product recommendation widgets
- Headless commerce frontends
- Omnichannel shopping interfaces
- Real-time inventory dashboards
Education
Our front-end services for education include developing interactive learning platforms, online course management systems, and student portals that enhance engagement and accessibility.
- LMS-integrated learning portals
- AI-based learning dashboards
- Interactive virtual classrooms
- Gamified learning interfaces
Award-Winning Innovation Solutions
Known for building innovative technology solutions across diverse industries, we’ve received multiple awards and recognitions from top B2B platforms.
Clutch 1000 Company – 2025
Recognized by Clutch among the top 1000 global companies for excellence in service and delivery in 2025
Clutch Global Award Winner – Fall 2024
Awarded by Clutch as a Global Leader for outstanding performance and client satisfaction in Fall 2024
Clutch Global Award Winner – Spring 2024
Recognized by Clutch as a Global Leader for delivering high-quality solutions and consistent client success in Spring 2024
Clutch Champion – Fall 2024
Honored by Clutch as a Champion for sustained excellence, industry leadership, and exceptional client feedback in Fall 2024
Clutch Champion – Spring 2024
Honored by Clutch as a Champion for sustained excellence, industry leadership, and exceptional client feedback in Fall 2024
Case Study
Tokenize : Cryptocurrency Trading & Exchange Platform
Tokenize Exchange
Ahex Technologies Pvt. Ltd. developed a client-facing mobile app with geolocation features as well as an intranet portal for employees. They provide ongoing support for any updates that need to be made.
Woohoo : Home AI & Enterprise AI Assistant
Woohoo
An enterprise-grade AI Voice Assistant platform designed to automate, govern, and optimize every guest interaction across single and multi-property hotel environments.
- IoT Hub — 1500+ Devices
- Voice-as-a-Service (VaaS)
- 360° Camera + Face ID
- Healthcare & Senior Care
Loan application tracking system
Southwest Funding
Southwest Funding is a Dallas-based mortgage lender founded in 1993 — serving 50,000+ home loans across 29 states with Conventional, FHA, VA, and USDA programs through 1,200+ licensed loan officers.
- Lead-to-Loan Pipeline
- Role-Based Dashboards
- Credit Report Integration
- SMS/Email Notifications
Ready to Build on Google Cloud?
Book a free scoping call with a senior GCP engineer. We'll review your architecture, data requirements, and current cloud costs — and give you an honest assessment of what Google Cloud would deliver for your specific workloads.
Testimonials
BLOGS
- ajay hooda
Benefits of Frontend Development
The frontend is the first thing users see. They interact with it on mobile apps, software, and websites. Because of
- Sanjay
How Start-ups Can Save Costs with Custom App Development
Every start-up begins with an idea, but running a business needs constant efforts, time, and money. Initially, start-ups have to
- ajay hooda
AI in Frontend Development for Smarter UI and UX Design
Frontend development is undergoing a transformation and it’s not just about new frameworks or fancier animations. It’s about AI in
AWS Rarely Lives Alone — Pair It With These Services
DigitalOcean Cloud Services
Build, deploy, and scale modern applications on DigitalOcean with flexible cloud infrastructure, managed databases, scalable compute, storage, and efficient deployment solutions designed for performance and cost efficiency.
AWS Cloud Services
Design, develop, migrate, and optimize scalable applications on AWS using secure cloud infrastructure, managed services, DevOps automation, and cloud-native technologies.
Microsoft Azure Services
Build, migrate, and modernize applications on Microsoft Azure with scalable infrastructure, cloud-native services, data solutions, AI capabilities, and secure enterprise cloud environments.
Vercel Development Services
Deploy and scale modern web applications with Vercel's frontend cloud platform, leveraging edge delivery, serverless functions, automated deployments, and performance optimization.
DevOps & CI/CD Pipelines
Accelerate software delivery with DevOps consulting, CI/CD automation, infrastructure as code, containerization, cloud infrastructure, monitoring, and deployment solutions. Build reliable, scalable delivery pipelines while improving development efficiency and reducing operational complexity.
Frequently Asked Question
Yes — it’s the explicit choice of enterprise engineering teams at Spotify, Twitter/X, HSBC, PayPal, and Snap. GCP’s BigQuery, Vertex AI, and global fibre network make large data-intensive multi-team codebases safe to refactor and extend. For smaller utility scripts plain JavaScript may be fine, but anything long-lived and applications benefit enormously from Google Cloud.
Any project with more than one developer, more than a few weeks of lifetime, or data-intensive SaaS products with BigQuery analytics needs, AI/ML workloads on Vertex AI, mobile apps with Firebase, and teams that want the best serverless container experience with Cloud Run. GCP is the default cloud for data-first and ML-first engineering teams. Prisma, tRPC, and Next.js — it’s the natural choice for the modern JavaScript ecosystem rather than an add-on.
We configure a CI type-check gate (tsc –noEmit) that blocks any PR introducing type errors, activate @typescript-eslint/no-explicit-any and @typescript-eslint/ban-ts-comment to prevent suppressions, and run a type-coverage threshold check on every build. Strictness is enforced by the CI pipeline, not by convention or code review alone.
By default, yes — strict:true enables strictNullChecks, noImplicitAny, strictFunctionTypes, and several other critical checks simultaneously. If you have a legacy codebase where strict mode can’t be enabled immediately, we use an incremental approach — enabling individual flags one at a time and graduating to full strict over sprints.
Typically 3–12 weeks depending on codebase size, existing test coverage, and strictness targets. We use an incremental allowJs strategy — your project stays deployable throughout, never blocked on a big-bang branch. Most production codebases see zero runtime regressions after our migration.
We start with a discovery call to understand your application stack, data requirements, ML needs, and current cloud costs. We then propose an engagement model — fixed budget, dedicated team, or time & material — and move into type architecture design, iterative build or migration sprints, and a documented handover with type coverage report.
Absolutely. We regularly audit inherited GCP projects — Security Command Center findings, IAM over-permissioned service accounts, BigQuery cost and partitioning gaps, missing Terraform coverageing Zod boundaries, and ESLint rule gaps — produce a prioritised remediation roadmap, and execute it incrementally without pausing delivery.
Looking for a Solution? Let's Talk
- sales@ahex.co.in
- (India) +91 - 8885564224
- (USA) +1 - 6503197394
DEVELOPERS
YEARS IN OPERATION
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