FIXED SCOPEone-off build
Ship a GCP deployment, end to end.
Fixed scope, fixed price, senior-only team. Landing zone to production workloads in 8-14 weeks.
FIXED SCOPE
- Zero juniors on client work
- Fixed quote in week 1
- Code, infra, runbook — yours
Google Cloud Development Services.Cloud Done Clean.
Not every workload belongs on Google Cloud. Our Google Cloud development services exist for the ones that genuinely do — data analytics, ML inference, and Kubernetes-native platforms, where GCP outperforms everyone else per dollar. We build on BigQuery, Vertex AI, and GKE with the cost controls and operational maturity enterprise data teams actually need.
- Terraform
- GKE Autopilot
- BigQuery
- Vertex AI
Why Entalogics for Google Cloud
What most Google Cloud
development companies
miss.
GCP is the cleanest cloud for data and Kubernetes — and the most punishing for teams that don't understand its billing model. Every GCP estate we audit has BigQuery scanning full tables instead of partitioned ones, GKE running on default node pools nobody sized, and committed use discounts that don't match the actual workload.
Cost01
A single query just cost more than a month of compute, and nobody noticed until the invoice.
BigQuery bills by bytes scanned, not by time. A `SELECT *` on an unpartitioned table can burn through your monthly budget in one dashboard refresh. We partition, cluster, and enforce query governors from day one, before an analytics team accidentally spends five figures on a report.
Architecture02
Someone chose Standard mode out of habit, not because GKE needed it.
Autopilot handles node provisioning, scaling, and security patching automatically. Standard mode earns its place only when GPU workloads or custom machine types genuinely require it. Same Kubernetes API, far less operational overhead, and most teams never actually need the extra control.
State03
A team fought Cloud SQL to do a job Firestore was built for.
Cloud SQL for relational data. Firestore for documents. Pub/Sub for messaging. BigQuery for analytics. GCP's managed services are strongest in the data layer specifically — we lean into that and don't fight the platform where it's weaker.
Type safety04
A console change nobody logged became a production mystery.
Every resource lives in Terraform, stored in git, deployed through Cloud Build or GitHub Actions. No manual console changes surviving a PR. The infrastructure stays auditable because it's version-controlled, not because someone remembers what they clicked.
When GCP, when not
Google Cloud is a tool.
Not every workload belongs on it.
GCP wins decisively on data, AI, and Kubernetes. It has fewer managed services than AWS and less enterprise integration than Azure everywhere else. We'll tell you on the first call which side of that line your workload actually falls on.
PICK GCP WHEN
- Data analytics is the core workload — BigQuery's serverless pricing model has no real equivalent elsewhere
- AI and ML are first-class citizens in your product — Vertex AI, TPUs, and Gemini model access live on one platform
- Your architecture is genuinely Kubernetes-native — GKE is the most opinionated, best-operated managed Kubernetes available
- Open source alignment matters to your team — GCP leans into Kubernetes, Terraform, and Apache Beam more than either competitor
CONSIDER AWS WHEN
- You need the widest managed-service catalogue — AWS ships 200+ services against GCP's smaller set
- Custom silicon for general compute changes your math — Graviton beats GCP's options on standard workloads
- Your team already operates on AWS with no data-driven reason to switch
WE SAY NO WHEN
- "GCP because Google uses it internally" — that's not an architecture requirement, that's brand loyalty
- "Migrate everything to GCP in four weeks" — that ship has sailed
- "GCP for our Microsoft-heavy enterprise" — Azure integrates better, and we'll say so on the first call
What we build on Google Cloud
Google Cloud development
services and consulting we deliver.
Seven ways this work shows up for real clients. Each one deployed with cost controls and observability from day one, not added after the first surprising invoice.
- S01
Data analytics on BigQuery
Data analytics on BigQueryPartitioned, clustered tables. Scheduled queries. BI Engine for sub-second dashboards. Query governors that stop an accidental full-table scan before it reaches your bill.
BIGQUERYLOOKERDATAFORMDBT - S02
ML and AI development on Vertex AI
ML and AI development on Vertex AIModel training, fine-tuning, and deployment on Vertex AI. TPU access for large-scale training. Gemini and open-source model serving through MLOps pipelines that run in production, not just a notebook.
VERTEX AITPUGEMINICLOUD FUNCTIONS - S03
GKE application platforms
GKE application platformsAutopilot for most workloads, Standard mode for GPU and custom node pools, Istio for service mesh, ArgoCD for GitOps. GKE treated as a platform decision, never a default hosting choice.
GKEAUTOPILOTISTIOARGOCD - S04
Event-driven and serverless on GCP
Event-driven and serverless on GCPCloud Functions for lightweight event handling, Cloud Run for containerized serverless, Pub/Sub for messaging, Eventarc for routing. Pay for what actually executes.
CLOUD RUNCLOUD FUNCTIONSPUB/SUBEVENTARC - S05
Data engineering and ETL pipelines
Data engineering and ETL pipelinesDataflow for streaming and batch, Dataproc for Spark workloads, Cloud Composer for orchestration. Pipelines that process at real scale and fail loudly the moment something breaks.
DATAFLOWDATAPROCCOMPOSERPUB/SUB - S06
Cloud migrations to GCP
Cloud migrations to GCPOn-prem to GCP, AWS to GCP, or a legacy GCP estate modernized properly. Workload by workload with Migrate to Containers and Database Migration Service.
MIGRATE TO CONTAINERSDMSTRANSFER SERVICEVPC - S07
GCP managed services
GCP managed servicesOngoing monitoring, cost reviews, and incident response after go-live — so the environment that launched clean doesn't quietly drift back into the mess we were hired to fix in the first place.
CLOUD MONITORINGSECURITY COMMAND CENTERDATADOGSENTRY
The playbook
Google Cloud development
patterns we ship on repeat.
Patterns from real production GCP deployments — not a Qwiklab exercise dressed up as a case study.
P01
Terraform-first infrastructure
Every resource in Terraform modules, state in GCS with locking, no console changes surviving past a PR. Drift detection through Terraform Cloud or Atlantis.
P02
BigQuery cost guardrails
Partitioning and clustering on every table. Custom quotas per project. Maximum bytes billed enforced on every query, so no accidental full-table scan ever reaches an invoice.
P03
GKE Autopilot by default
Autopilot for every workload that doesn't specifically need GPU or custom node configuration. Pod-level billing, no idle node cost, Google managing the nodes instead of your team.
P04
Committed use discounts matched to the actual workload
CUDs applied to stable baseline compute. Preemptible VMs for batch jobs and CI. Sustained use discounts captured automatically, every discount mechanism matched to what it's actually built for.
P05
Security Command Center from the start
Threat detection and security posture live before an incident forces the question. Organization-level policies, findings prioritized by real severity and exposure.
P06
Cloud Build plus ArgoCD
Build in Cloud Build, deploy through ArgoCD to GKE, stage-gated with approval gates and rollback. No manual `kubectl apply` reaching production.
Signature case
A data platform,
consolidated from three tools into BigQuery + Vertex AI.
A B2B analytics company running Redshift, a self-managed Spark cluster, and a separate ML training environment on EC2 — $54k/mo, three ops teams, data duplicated across systems, and a 6-hour ETL pipeline that broke weekly. Consolidated into BigQuery for analytics, Dataflow for ETL, and Vertex AI for model training in 10 weeks. Monthly spend dropped 41%. ETL runs in 22 minutes.
Before
Redshift + Spark + EC2 ML · $54k/mo · 3 ops teams · 6hr ETL · weekly pipeline failures
After
BigQuery + Dataflow + Vertex AI · $31.8k/mo · 1 ops team · 22min ETL · zero failures in Q1
- Monthly infra cost−41%
- ETL runtime6hr → 22min
- Migration duration10wk
- Pipeline failuresweekly → 0
Industries we serve
Google Cloud development
across industries.
We've delivered Google Cloud development services for finance, media, retail, and data-driven SaaS — anywhere BigQuery's analytics engine or Vertex AI's model infrastructure is already part of the roadmap.
- Finance & Fintech
- Media & Streaming
- Retail & eCommerce
- Data-Driven SaaS
- Healthcare Analytics
- AI & Machine Learning
Engagement shape
Eight to ten weeks
on a Google Cloud engagement.
Workload by workload. The current infrastructure stays live the entire time we work.
- W01
Audit + RFC
BigQuery cost analysis, GKE cluster review, IAM audit, billing structure assessment. A ranked, dollarized plan, not a list of vague concerns.
- W02–03
Foundation + first workload
Terraform landing zone deployed, org policies enforced, first production workload live with monitoring and cost tagging. Real cost data in your dashboard by week three.
- W04–08
Workload by workload
Each workload deployed or migrated with right-sized compute, BigQuery guardrails, and Security Command Center enabled. Your product keeps running throughout.
- W09+
Handoff + FinOps
Cost dashboard live, security posture green. Runbook handed over, or we stay on for GCP managed services.
Stack
Tools we
Tools we reach
reach for for first.
Our default Google Cloud development stack — picked for production.
IaC
Terraform · Pulumi · Google Cloud Deployment Manager
Compute
GKE · Cloud Run · Cloud Functions · Compute Engine
Data
BigQuery · Cloud SQL · Firestore · Cloud Spanner · Pub/Sub
AI/ML
Vertex AI · TPU · Gemini API · Dataflow
CI/CD
Cloud Build · GitHub Actions · ArgoCD · Flux
Monitoring
Cloud Monitoring · Cloud Logging · Datadog · Sentry
Engagement
Three ways to hire
Google Cloud developers
at Entalogics.
No hourly billing for thinking time. Fixed quote or a transparent monthly rate.
DEDICATED TEAMmonthly
Hire dedicated Google Cloud engineers.
Senior cloud architects specializing in GCP data and Kubernetes, embedded in your Slack, your standups. Pause, resize, or exit with 30 days' notice.
PER ENGINEER
- Same senior bar as fixed-scope
- Embedded in your team
- Founder-direct escalation
ENGAGEMENTcustom
Strategic Google Cloud consulting partnership.
A standing partner for data-driven organizations — BigQuery optimization, Vertex AI pipelines, GKE platform engineering, hiring help.
PROCUREMENT-FRIENDLY
- Multi-quarter roadmap
- Architecture and hiring partner
- Procurement-friendly paper
FAQ
Questions we get on almost
every Google Cloud call.
GCP vs AWS vs Azure, BigQuery costs, GKE Autopilot, timelines, existing estates — in roughly the order people ask.GCP if data analytics, AI/ML, or Kubernetes-native architecture is your primary workload — BigQuery, Vertex AI, and GKE are genuinely best-in-class there. AWS for the broadest managed-service catalogue. Azure if you're a Microsoft-centric shop. We'll tell you which fits on the first call.
Partition and cluster every table, set maximum bytes billed per query, apply custom quotas per project. BigQuery bills by bytes scanned, not by time — the real cost control lives in schema design and query governance, not in the billing dashboard after the fact.
Autopilot for most workloads — Google manages the nodes, you pay per pod, no idle node cost. Standard only when you need GPU node pools or custom machine types. Most applications never actually need Standard mode.
Depends on scope and how much of your estate already exists. The week-one audit gives real, dollarized numbers — the quote is fixed from there, not a range that grows every sprint. Most engagements run 8-14 weeks end to end.
Yes. The engineers who write the RFC ship the infrastructure. No handoff mid-engagement, no account manager in between.
Yes. We audit what's there, tag what's untagged, right-size what's over-provisioned, and deploy new workloads with Terraform alongside what already exists. No rip-and-replace unless the estate genuinely needs it.
Founder-direct
Tell us whatyou're building.
Thirty minutes with the founder — a senior GCP architect, the relevant playbook, and a candid read on whether Google Cloud is the right platform, or AWS and Azure actually fit your workload better.