Google Cloud for Data and ML First Teams
We design and run Google Cloud workloads for teams whose centre of gravity is data and ML — BigQuery, Vertex AI, Dataflow, Looker, and the rest of the GCP data stack. Right-sized, governed, and built so the analytics team can move fast without the bill becoming the next board topic. Senior GCP engineers who use the boring services first and the fancy ones only when they earn it.
Google Cloud isn't expensive.
Untuned BigQuery is.
Most GCP pain we see is concentrated in two places: a BigQuery bill that nobody can explain, and IAM that grew organically until everyone is roughly an Owner of everything. The rest of the stack is usually fine. We fix those two, set up sensible guardrails, and leave the project in a state your data team can actually move fast in without lighting money on fire.
BigQuery bill spikes nobody can explain
On-demand pricing, SELECT * across petabyte tables, partitioning ignored, materialised views never created, scheduled queries running on data that hasn't changed in a week. One bad notebook can cost more than the analyst that wrote it.
IAM grew organically and now everyone is Owner
Project-level Owner handed out three years ago, service accounts with primitive roles, keys downloaded to laptops, no Workload Identity Federation. One leaked key from a forgotten Cloud Run service away from a very bad week.
GKE that was the wrong call
A two-service app running on a multi-node GKE cluster because someone wanted Kubernetes on the CV. Should have been Cloud Run or App Engine. Costs 5x what it needs to and nobody wants to be the one to migrate it.
What You Actually Get
No vague deliverables. Here's exactly what lands in your hands.
Terraform in your repo
Projects, IAM, networking, BigQuery datasets, Cloud Run services — all as code, with state in GCS, plan/apply via CI. The whole org reproducible from git.
A BigQuery cost & performance audit
Top 50 expensive queries identified, partitioning and clustering recommendations, materialised view candidates, slot reservation modelling vs on-demand. Line-item dollar savings.
A locked-down org structure
Folders by environment, organisation policies, VPC Service Controls where they matter, Workload Identity Federation instead of service account keys, Cloud Identity / Workspace integration done properly.
Real observability
Cloud Monitoring dashboards, log-based metrics, alerting wired to a real on-call channel, SLOs defined where they matter, log retention policies that don't bankrupt you.
A Real GCP & Data Engineering Team
GCP rewards teams who actually understand the data stack. Six roles you get on every engagement.
GCP Solutions Architect
Designs the org, the network, the data flow. Knows when Cloud Run beats GKE, when BigQuery beats Spanner, and when Firestore is the right answer.
BigQuery / Data Engineer
Owns BigQuery cost and performance. Partitioning, clustering, materialised views, slot reservations, BI Engine, Dataform, dbt. Can read a query plan and tell you what's wrong.
Vertex AI / ML Engineer
Vertex AI training, pipelines, model registry, endpoints, Gemini and Model Garden. Knows when to call an API and when to actually train something.
Platform / DevOps Engineer
Terraform, Cloud Build, GitHub Actions, Artifact Registry, Workload Identity Federation. Owns the pipelines and the policy-as-code.
Cloud Security Engineer
IAM, org policies, VPC Service Controls, Security Command Center, KMS, Secret Manager. Has read the GCP security best practices and applies them without religion.
FinOps Lead
Owns the bill. Labels everything, builds the chargeback view, models CUDs (Committed Use Discounts) and slot reservations against real usage.
You See Everything. In Real Time.
Every Pillai Infotech project comes with a dedicated client dashboard. Kanban boards, live logs, test results, meeting notes — it's all visible the moment it happens. No status-report theatre, no "we'll get back to you", no surprises at the demo. You work with us like you work with your own team.
Kanban Board, Live
Every epic, every story, every task — visible on your dashboard. Drag, comment, reprioritize. It's the same board our team works from.
Documented Everything
Every decision, spec, API contract, and architecture diagram lives in the dashboard. Searchable, versioned, linked to the tasks they shaped.
Live Logs & Test Results
Build logs, deployment logs, test suite results — streamed to your dashboard the moment they run. You never have to ask "did the build pass?"
Meetings → Tasks, Automatically
Every meeting is recorded, transcribed, and every action point is auto-converted into a tracked task assigned to the right person. Nothing gets lost between calls.
Sprint Burndown & Velocity
See exactly how much work is done, how much remains, and our velocity over time. If a sprint is slipping, you see it the same moment we do.
Comment, Approve, Decide — In-Place
Comment on any task, approve designs, sign off on specs, and raise blockers directly in the dashboard. Everything tied to the work, not buried in email threads.
GCP Workloads We Run Without Drama
We pick the GCP service that fits the workload, not the one with the newest blog post.
📊 BigQuery data warehouses
BigQuery + Dataform or dbt + Looker / Looker Studio. Partitioned, clustered, materialised, with cost controls and a slot reservation model that fits your actual workload.
🌊 Streaming & batch pipelines
Pub/Sub, Dataflow, Cloud Composer, BigQuery streaming inserts. ELT-first, schemas validated, dead-letter queues, replay built in.
🤖 Vertex AI & GenAI workloads
Vertex AI pipelines, model registry, endpoints, Gemini via API, RAG on BigQuery + Vertex AI Search, evaluation harnesses. Right-sized so a single rogue endpoint can't drain the budget.
🌐 Cloud Run web & API platforms
Cloud Run, API Gateway, Cloud SQL or Spanner, Cloud CDN. Boring, scales to zero, costs almost nothing at idle. Our default for most web workloads.
📦 GKE when it's actually justified
GKE Autopilot for teams that genuinely need Kubernetes — multi-tenant platforms, complex networking, existing K8s investment. Otherwise we use Cloud Run.
🏢 Lift-and-shift & migrations
Migrate to Virtual Machines for IaaS workloads, then progressively re-platform onto Cloud Run, BigQuery, and managed services. Honest about what should and shouldn't move.
The GCP Stack We Use
Boring services first. The exotic ones only when they earn their keep.
Compute & Containers
Data & Analytics
AI & ML
Security & Delivery
A Six-Stage GCP Delivery Process
Designed to leave your org in a state your own team can take over on day 91.
Org & Bill Audit
Read-only access for one week. We map every project, IAM principal, BigQuery dataset, and cost driver, and produce a written audit with prioritised findings.
Org Structure & Guardrails
Folder layout, organisation policies, IAM baseline, VPC design, Workload Identity Federation. The foundation that stops the next mistake from being a $10k one.
Architecture Design
A target architecture in writing, with diagrams, trade-offs, and a monthly cost model — including a BigQuery slot vs on-demand decision.
Build in Terraform
Terraform modules pull-requested into your repo. Plan output reviewed before every apply. No console clicks.
Cutover & Validation
Migration windows, smoke tests, rollback plan rehearsed. SLOs, log-based metrics and alerting wired up before traffic hits.
Handover or Run
Either we hand the keys back with documentation and training, or we keep operating it on a managed-services retainer. Your call.
Three Ways to Engage
Pick the engagement that matches the state of your GCP org today.
GCP Audit Sprint
Two-week deep dive: cost (especially BigQuery), security, IAM, reliability, IaC readiness. Written report and prioritised action list — no obligation to use us for the fix.
- BigQuery cost deep dive
- IAM and org policy audit
- Written report you own
Build or Re-Platform
Fixed-scope engagement to design and ship a new workload, or re-platform an existing one onto a clean org structure with full Terraform.
- Fixed scope, fixed price
- Typical: 6–14 weeks
- Full Terraform handover
Managed GCP Retainer
Ongoing operation: on-call, BigQuery cost reviews, security posture, capacity planning, platform upgrades.
- 24/7 on-call available
- Monthly cost & posture review
- Quarterly architecture review
Honest Answers to GCP Reality Questions
The questions every smart buyer asks before signing. Here's what we tell them.
Can you really cut our GCP bill 30%+?
On a typical un-audited project, yes — 25–45% in the first 90 days, with most savings coming from BigQuery (partitioning, clustering, materialised views, slot reservations vs on-demand), idle Compute Engine cleanup, CUDs on baseline workloads, and switching mis-sized GKE clusters to Cloud Run. We won't promise it without seeing the bill.
BigQuery on-demand or slot reservations?
Depends on your usage curve. Spiky, exploratory, low-volume — on-demand is fine and you should focus on query hygiene. Predictable, high-volume, multi-team — Editions with autoscaling reservations almost always wins. We model both against 90 days of your real query history before recommending.
Cloud Run or GKE?
Cloud Run unless you have a real reason for Kubernetes — multi-tenant platforms, complex networking, existing K8s investment your team operates well. Cloud Run scales to zero, costs almost nothing at idle, and removes a huge operational surface. Most teams that pick GKE first regret it.
Vertex AI or just the Gemini API?
Most teams should start with the Gemini API and Vertex AI Search for RAG. You only need the heavier Vertex AI training, pipelines and model registry when you're actually fine-tuning or training custom models. We'll tell you honestly which side of that line you're on.
How do you handle service account keys?
We don't use them. Workload Identity Federation for CI/CD, Workload Identity for GKE, attached service accounts for Cloud Run / Functions / Compute Engine. Downloaded JSON keys are the #1 cause of GCP credential leaks and we treat them as a last resort.
Can you migrate us from another cloud?
Yes. Most often we move data warehouse workloads (Snowflake, Redshift) to BigQuery, or web workloads to Cloud Run. Honest assessment first — sometimes the answer is "stay where you are for that workload, move only this one piece". We don't do religion-driven migrations.
What about multi-region and DR?
BigQuery and GCS handle multi-region natively. For Cloud Run and Cloud SQL, most workloads do not need active-active — we design for the recovery objective you actually need and tell you the cost difference. No DR theatre.
Compliance — SOC 2, HIPAA, ISO 27001?
Yes. GCP has the certifications and the BAA; we configure the org so you can pass the audit on top: org policies, VPC Service Controls where they matter, Security Command Center Premium, audit logs to a locked logging project, KMS, encryption everywhere, access reviews.
Who owns the GCP organisation?
You do. Always. Org tied to your Cloud Identity / Workspace, billing account in your company name, super admins in your safe, Terraform in your GitHub org. We work inside it as delegated principals.
Can you sign an NDA before we share details?
Always. NDA before the first call. The audit can run on read-only access only until trust is earned.