BigQuery
Cloud Run
Firestore
GKE
Pub/Sub
Google Cloud Development Services
Google Cloud engineering
Google Cloud engineering
your team can
actually own.
BigQuery, Cloud Run, Firestore, GKE, and Pub/Sub. Designed from the data layer up. Built for the engineers who will maintain it after we leave. Zero-downtime migrations. HA architecture. Google Cloud Platform-native from day one.
Infrastructure built to outlast the engagement
We document every architecture decision. Your team can extend the platform without calling us first.
GCP BILLING ALERT
$48,200 / month
Expected: $18,400 · 162% over budget
Cloud Cost Reality
Most GCP bills are 30% waste.
Ungoverned cloud resources, over-provisioned databases, development environments left running, and zero cost allocation. The bill grows. The return on investment does not. Below are the five most common categories we find in every GCP audit.
Idle and over-provisioned virtual machines0%
Average percentage of virtual machine spend on instances with less than 10% CPU utilization over 30 days
Unattached persistent disks0%
Storage attached to deleted virtual machines, still billed at full rate every month
Development and staging environments up 24 hours a
day0%
Non-production environments consuming the same resources as production, all weekend
Egress charges with no network configuration0%
Cross-region data transfer costs that disappear with proper Cloud CDN and network routing
Redefine architects review your GCP billing export as the first deliverable of every engagement. You see the waste before we write a line of infrastructure code.
GCP Service Engineering
Five GCP services. One engineering team.
BigQuery
Analytics & Data Warehouse
Cloud Run
Serverless Containers
Firestore
Real-Time NoSQL Database
GKE
Kubernetes Orchestration
Pub/Sub
Messaging & Event Streaming
BigQuery analytics and data warehouse engineering
Data model design, ETL/ELT pipeline architecture, BigQuery ML, partitioned and clustered tables, cost-optimized slot reservations, Looker Studio integration, and dbt transformation layers. From raw event data to executive dashboards.
SELECT
order_date, product_id,
SUM(revenue) AS total_revenue,
COUNT(DISTINCT customer_id) AS unique_buyers
FROM `redefine-prod.analytics.orders`
WHERE order_date >= DATE_SUB(CURRENT_DATE(), INTERVAL 90 DAY)
GROUP BY 1, 2
ORDER BY total_revenue DESC
order_date, product_id,
SUM(revenue) AS total_revenue,
COUNT(DISTINCT customer_id) AS unique_buyers
FROM `redefine-prod.analytics.orders`
WHERE order_date >= DATE_SUB(CURRENT_DATE(), INTERVAL 90 DAY)
GROUP BY 1, 2
ORDER BY total_revenue DESC
Rows
48,291
Revenue
$2.8M
Buyers
12,441
Cost
$0.004
Cloud Run: serverless containers that scale to zero
Container-based microservices with automatic scaling, zero cold-start optimization, traffic splitting for blue-green deployments, Cloud Run Jobs for batch workloads, and VPC peering for secure internal service communication.
api-gateway
us-central1 · 3 revisions
Serving
1,240 req/s
user-service-v2
us-central1 · Traffic: 80/20 split
Blue/Green
342 req/s
analytics-ingest
us-east1 · 0 instances (scaled to 0)
Idle
$0.00/hr
Firestore: real-time NoSQL designed for scale
Document schema design, subcollection architecture, composite index strategy, Firestore rules security model, real-time listener optimization, and migration from MongoDB or Datastore. Zero-downtime change data capture migration pipelines via Dataflow.
// users / user_8821fa
"id": "8821fa",
"email": "[email protected]",
"plan": "enterprise",
"mrr": 4800,
"created": Timestamp(2024-03-12)
"id": "8821fa",
"email": "[email protected]",
"plan": "enterprise",
"mrr": 4800,
"created": Timestamp(2024-03-12)
Documents: 2.4M·Reads: 48K/s·Listeners: 1,241 active
GKE: Kubernetes clusters built for production from day one
Cluster design (Autopilot versus Standard), Helm chart management, Horizontal Pod Autoscaler configuration, network policy, Workload Identity for secure service-account binding, node pool cost optimization, and continuous integration and delivery via Cloud Deploy.
prod-cluster-us-central1
Autopilot · GKE 1.29 · 3 regions
Healthy
24
Pods
99.98%
Uptime
3
Node pools
CPU: 42% · Memory: 61% · Network: 1.2 Gbps
Pub/Sub: event-driven architecture and streaming pipelines
Topic and subscription design, exactly-once delivery guarantees, Dataflow pipeline integration for streaming analytics, dead-letter queue configuration, schema registry with Avro/Protobuf, and Eventarc for Cloud Run trigger integration.
order-events
4,820 msg/s3 subs
user-activity
12,100 msg/s5 subs
dead-letter-queue
0 msg/sHealthy
Total throughput: 16,920 msg/s · Exactly-once delivery
enabled
What We Build on GCP
Six capabilities. One Google Cloud Platform-native team.
Architecture first
Every GCP engagement starts with an architecture document. Not a Jira backlog. Not a kickoff call. A written document with diagrams, data flows, and a cost model that you approve before we touch your infrastructure.
01
GCP Architecture Design
Data model, service selection, VPC topology, IAM design, and
cost model. Documented before Sprint 1 ends.
02
Zero-Downtime Migrations
Change data capture pipelines, parallel-run validation, traffic
cut strategies. Live production stays live throughout.
03
HA and Disaster Recovery
Multi-region design, automated failover, recovery time objective
and recovery point objective targets defined and tested. 99.9%+
uptime architecture.
04
Cost Governance
Committed use discounts, sustained use monitoring, budget
alerts, and idle resource cleanup. Billing under control from
day one.
05
Security and IAM
Workload Identity, VPC service controls, CMEK encryption, Cloud
Armor WAF, and compliance-ready audit logging.
06
Infrastructure as Code and Continuous Delivery
Terraform modules for every resource, Cloud Deploy pipelines,
automated rollback, and environment parity between development,
staging, and production.
GCP Proof
Zero downtime. Live production. Firestore migrated.
Service interruption
0
minutes of downtime during the migration
HA uptime achieved
99.0%
sustained after GCP multi-region HA setup
Data systems migrated
0
NoSQL sources migrated to Firestore via change data capture
Zero downtime confirmed
Client
OTT Media Streaming Platform
Enterprise Streaming Infrastructure
Google Cloud PlatformFirestoreDataflow
A live OTT streaming platform requiring migration of complex NoSQL data across multiple systems, where any downtime would directly interrupt active users and revenue.
The Problem
Data was distributed across MongoDB and Amazon DocumentDB with complex schemas and high write volumes. Traditional migration approaches required maintenance windows. Zero downtime was a hard requirement from the business, not a preference.
Live streaming infrastructure. High write volumes. Complex multi-system NoSQL schemas. Any downtime directly impacts paying subscribers.
The Solution and Result
0 min
downtime. Real-time change data capture pipelines replicated data continuously from MongoDB and DocumentDB into Firestore via Google Dataflow and Python ETL. Traffic cutover happened at the application layer, not the data layer.
Firestore-native architecture with Google Cloud Platform-native Dataflow pipelines
Scalable cloud-native data foundation for ongoing platform growth
Why Redefine
What separates a GCP specialist from a GCP agency.
01 / Ownership
Infrastructure as code from day one
Every resource created by Terraform. No click-ops. No tribal
knowledge. Your team inherits a Git-versioned infrastructure
repository they can review, extend, and rollback. Not a support
contract.
02 / Architecture
Service selection with written rationale
We document why we chose Cloud Run over GKE, Firestore over
Cloud Spanner, Pub/Sub over Kafka. The rationale is in writing.
You can challenge it. Most agencies cannot explain their own
choices.
03 / Cost
Cost model before we touch your GCP console
The architecture sprint outputs a projected monthly GCP cost by
service. You approve it. Billing surprises in month 3 mean the
architecture changed without your sign-off. We do not let that
happen.
04 / Migration
Zero-downtime or we do not propose the timeline
If your production system cannot tolerate a maintenance window,
we design change data capture pipelines and parallel-run
validation before we propose a cutover date. Zero downtime is an
architecture requirement, not a stretch goal.
05 / Security
IAM and VPC designed before the first resource
Workload Identity, service account scoping, VPC service
controls, and audit logging are Sprint 1 outputs. Security is
not a hardening phase at the end. It is the foundation
everything runs on.
06 / Handoff
Runbooks your team can execute without us
Terraform apply instructions, disaster recovery playbooks, cost
optimization runbooks, and incident response guides. Your
on-call engineer can respond to a production issue at 2am
without calling us first.
Questions
What engineering teams ask before a GCP engagement.
Service selection, migration risk, and cost governance are the real blockers. These are direct answers.
Pricing approach
Architecture sprint first. Scoped before any infrastructure code runs.
The sprint delivers an architecture document, GCP cost model, service selection rationale, and migration plan. You approve before Sprint 2 begins.
Cloud Run when: stateless HTTP services, unpredictable or spiky traffic, no need for persistent storage on the container, or when the team should not be managing Kubernetes. GKE when: stateful workloads (databases, queues), services that need sidecar containers, teams comfortable with Kubernetes, or when you need fine-grained control over networking and scheduling. We document the choice in the architecture sprint with the rationale. If your team disagrees, we revise before writing code.
Change data capture pipelines built with Google Dataflow continuously replicate writes from MongoDB to Firestore in real time. The application reads from MongoDB during migration. When Firestore is within an acceptable lag threshold (usually under 1 second), we switch the application's read and write target to Firestore at the load-balancer layer. MongoDB stays live as a rollback target for 72 hours. No maintenance window. No user-visible interruption. This is the same approach we used for the OTT streaming case study above.
We set up budget alerts per project and per service with escalation to Slack and email before the project ends. Committed use discounts are reviewed and applied for any workload running continuously. Development and staging environments use scheduled shutdown jobs to scale to zero outside business hours. The Terraform codebase includes tagging for cost allocation per team or feature. A cost runbook documents the manual review process your team runs monthly after we hand off.
Terraform exclusively, using the official Google Cloud provider. Deployment Manager is Google Cloud Platform-proprietary and creates lock-in your team should not accept. Terraform is multi-cloud compatible, has a wider community, and your team can use the same tooling if you add AWS or Azure services later. We structure modules so Google Cloud Platform-specific resources are isolated from cross-cloud shared patterns. State is stored in Google Cloud Storage with locking via Cloud Firestore or a Terraform backend.
GCP's sustained use discounts apply automatically (up to 30% off on-demand for Compute Engine) without requiring upfront commitment. BigQuery's per-query pricing is lower than Redshift for sporadic analytics workloads. Cloud Run's scale-to-zero pricing eliminates idle compute costs entirely. Egress pricing on GCP is generally competitive. We do not advise choosing GCP on cost alone for steady-state compute-heavy workloads where AWS Reserved Instances with longer commitments win. The architecture sprint includes a cost comparison for your specific workload profile.
Right Match?
Select what describes your situation.
We are direct about fit. Teams that need basic GCP setup often benefit more from Google's own documentation than from a custom engagement.
Match score0 of 6 selected
Not sure? Describe your GCP situation and we will be direct about the right approach for your team.
Your GCP bill is growing faster than your traffic or features
Ungoverned resource provisioning, idle virtual machines, and lack of cost allocation are the usual causes.
You need to migrate a live production system to GCP without a maintenance window
Change data capture pipelines and parallel-run architecture require dedicated GCP engineering experience.
Your GCP infrastructure was built with click-ops and has no Terraform or infrastructure as code
Reconstructing state into Terraform without disrupting production is a specialist task.
You need high availability with defined recovery targets for a regulated environment
Multi-region GCP high availability with automated failover and tested recovery processes.
Probably not the right match if:
You need a basic GCP project setup and a single Cloud Run service deployed
Google Cloud documentation and a junior GCP engineer handle this in an afternoon.
Total project budget under $10,000
A proper architecture sprint with Terraform and documentation requires real engineering time.
Start Here
Describe your GCP challenge. Get a scoped architecture proposal.
No commitment. No pitch. A written architecture approach and cost model in 3 business days.
01
Submit your brief
Describe the GCP services involved, current state, and the goal. Include a billing screenshot if you have one.
02
Architecture call within 48 hours
With a GCP-certified engineer. We ask about your current infrastructure, IAM setup, and migration constraints.
03
Architecture proposal in 3 days
Service selection, architecture diagram, cost model by service, migration approach, and sprint plan.
04
Sprint 1 within 1 week of sign-off
Architecture sprint: Terraform structure, IAM design, VPC topology, and cost model. All approved before resources are provisioned.
Form
48 hours
Architecture call
3 days
Cost model
48+
GCP projects
0 min
Migration downtime