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Entalogics

Data, AI, andKubernetes.Built onGoogle Cloud.

Google Cloud development services for organisations building on BigQuery, Vertex AI, and GKE — where GCP's genuine strengths live. Not every workload belongs on Google Cloud. But data analytics, ML inference, and Kubernetes-native platforms are where GCP outperforms everyone else per dollar. We build on GCP with the cost controls and operational maturity that enterprise demands.

Plan a GCP build
See the playbook
  • Terraform
  • GKE Autopilot
  • BigQuery
  • Vertex AI

Why Entalogics for Google Cloud

Four things every
Google Cloud deployment
actually needs.

GCP is the cleanest cloud for data and Kubernetes — and the messiest for organisations that don't understand its billing model. The GCP estates we audit always have BigQuery scanning full tables instead of partitioned ones, GKE clusters running on default node pools nobody sized, and committed use discounts that don't match actual workloads.

Cost01

BigQuery bills by bytes scanned, not by time. Act accordingly.

A single `SELECT *` on an unpartitioned table can cost more than a month of compute. We partition, cluster, and enforce query governors from day one — so your analytics team doesn't accidentally spend $5,000 on a dashboard refresh.

Architecture02

GKE Autopilot unless you have a real reason to manage nodes.

Autopilot handles node provisioning, scaling, and security patching. Standard mode only when GPU workloads, custom machine types, or specific node configurations genuinely require it. Less operational overhead, same Kubernetes API.

State03

Managed services over self-managed where GCP is genuinely best.

Cloud SQL for relational data. Firestore for document storage. Cloud Pub/Sub for messaging. BigQuery for analytics. GCP's managed services are strongest in the data layer — we use them there and avoid fighting the platform where it's weaker.

Type safety04

Infrastructure as code. Terraform. No console clicking.

Every resource in Terraform, stored in git, deployed via Cloud Build or GitHub Actions. No manual console changes. The infrastructure is auditable because it's version-controlled.

When GCP, when not

Google Cloud is a tool.
Not every workload belongs on it.

GCP wins on data, AI, and Kubernetes. It has fewer managed services than AWS and less enterprise integration than Azure. We'll tell you on the first call which workloads belong on GCP — and which don't.

PICK GCP WHEN

  • Data analytics is the core workload — BigQuery's serverless model and pricing are unmatched
  • AI and ML are first-class — Vertex AI, TPUs, and Gemini model access from the same platform
  • Kubernetes-native architecture — GKE is the most opinionated and best-operated managed Kubernetes
  • Open source alignment matters — GCP leans into Kubernetes, Terraform, Apache Beam, and open formats

CONSIDER AWS WHEN

  • You need the broadest managed service catalogue — AWS has 200+ services, GCP has fewer
  • Custom silicon for general compute — Graviton offers price-performance GCP can't match on standard workloads
  • Your team already operates on AWS and there's no data-driven reason to switch

WE SAY NO WHEN

  • "GCP because Google uses it." That's not your architecture requirement.
  • "Migrate everything to GCP in four weeks." That ship has sailed.
  • "GCP for our Microsoft-heavy enterprise." Azure integrates better. We'll tell you that on the first call.

What we build on Google Cloud

Six product surfaces.
One quality bar.

The shapes of Google Cloud development we deliver most. Each deployed with cost controls and observability from day one.

  • S01

    Data analytics on BigQuery

    Partitioned, clustered tables. Scheduled queries. BI Engine for sub-second dashboards. Query governors to prevent accidental spend. BigQuery used as the warehouse it's built to be.

    BIGQUERYLOOKERDATAFORMDBT
  • S02

    ML & AI on Vertex AI

    Model training, fine-tuning, and deployment on Vertex AI. TPU access for large-scale training. Gemini and open-source model serving. MLOps pipelines that run in production, not notebooks.

    VERTEX AITPUGEMINICLOUD FUNCTIONS
  • S03

    GKE application platforms

    Autopilot for most workloads. Standard mode for GPU and custom node pools. Istio for service mesh. ArgoCD for GitOps. GKE used as a platform — not a default hosting choice.

    GKEAUTOPILOTISTIOARGOCD
  • S04

    Event-driven & serverless

    Cloud Functions for lightweight event handling. Cloud Run for containerised serverless. Pub/Sub for messaging. Eventarc for event routing. Pay only for what executes.

    CLOUD RUNCLOUD FUNCTIONSPUB/SUBEVENTARC
  • S05

    Data pipelines & ETL

    Dataflow for streaming and batch. Dataproc for Spark workloads. Cloud Composer for orchestration. Pipelines that process at scale and fail loudly when something goes wrong.

    DATAFLOWDATAPROCCOMPOSERPUB/SUB
  • S06

    Cloud migrations to GCP

    On-prem to GCP, AWS to GCP, or legacy GCP to modern GCP. Workload by workload with Migrate to Containers and Database Migration Service.

    MIGRATE TO CONTAINERSDMSTRANSFER SERVICEVPC

The playbook

Patterns we
ship on repeat.

GCP patterns from real production deployments — not Qwiklab exercises.

  • P01

    Terraform-first infrastructure

    Every resource in Terraform modules. State in GCS with locking. No console changes. Drift detection via Terraform Cloud or Atlantis.

  • P02

    BigQuery cost guardrails

    Partitioning and clustering on every table. Custom quotas per project. Maximum bytes billed on every query. No accidental full-table scans reaching your invoice.

  • P03

    GKE Autopilot by default

    Autopilot for every workload that doesn't need GPU or custom node configuration. Pod-level billing. No idle node cost. Google manages the nodes.

  • P04

    Committed use discounts matched to workload

    CUDs applied to stable baseline compute. Preemptible VMs for batch and CI. Sustained use discounts captured automatically. Every discount mechanism matched to the right workload.

  • P05

    Security Command Center

    Threat detection and security posture from day one. Organisation-level policies. Findings prioritised by severity and exposure.

  • P06

    Cloud Build + ArgoCD

    Build in Cloud Build. Deploy via ArgoCD to GKE. Stage-gated with approval gates and rollback. No manual kubectl applies to 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

Engagement shape

Eight to ten weeks
to a measurable ship.

A typical Google Cloud development engagement. We deploy workload by workload — the current infrastructure stays live while we work.

  • W01

    Audit + RFC

    Two senior GCP architects. BigQuery cost analysis, GKE cluster review, IAM audit, billing structure assessment. A ranked, dollarized RFC.

  • 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.

  • 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.

  • W09+

    Handoff + FinOps

    Cost dashboard live. Security posture green. Runbook handed to your team — or we stay on retainer.

Stack

Tools we
reach for first.

Our default Google Cloud development stack — picked for production.

  • IaCTerraform · Pulumi · Google Cloud Deployment Manager
  • ComputeGKE · Cloud Run · Cloud Functions · Compute Engine
  • DataBigQuery · Cloud SQL · Firestore · Cloud Spanner · Pub/Sub
  • AI/MLVertex AI · TPU · Gemini API · Dataflow
  • CI/CDCloud Build · GitHub Actions · ArgoCD · Flux
  • MonitoringCloud Monitoring · Cloud Logging · Datadog · Sentry

Engagement

Three ways
to work with us.

No hourly retainer that bills for "thinking time." Pick a lane that matches your stage; everything is fixed-quote or transparently rated.

FIXED SCOPEone-off build

Ship a GCP deployment, end-to-end.

A defined scope, a fixed price, a senior-only team. From landing zone to production workloads in 8–14 weeks.

$15k–$30k

FIXED SCOPE

  • Senior engineers only
  • Fixed quote in week 1
  • Code, infra, runbook — yours
Plan a fixed build
DEDICATED TEAMmonthly

Hire dedicated Google Cloud engineers.

Embedded engineers in your Slack, your standups. Senior cloud architects specialising in GCP data and Kubernetes. Pause, resize, end with 30 days' notice.

$5k / eng / mo

PER ENGINEER

  • Same senior bar as fixed-scope
  • Embedded in your team
  • Founder-direct escalation
Hire dedicated GCP devs
ENGAGEMENTcustom

Strategic Google Cloud partnership.

A long-term partner for data-driven organisations — BigQuery optimisation, Vertex AI pipelines, GKE platform engineering, hiring help.

custom

PROCUREMENT-FRIENDLY

  • Multi-quarter roadmap
  • Architecture & hiring partner
  • Procurement-friendly paper
Speak to the founder
FAQ

Sharp questions,
straight answers.

GCP vs AWS vs Azure, BigQuery costs, GKE Autopilot — the questions we get on every Google Cloud discovery call.
GCP if data analytics, AI/ML, or Kubernetes-native architecture is your primary workload — BigQuery, Vertex AI, and GKE are best-in-class. AWS for the broadest service catalogue. Azure if you're a Microsoft shop. We'll tell you which fits on the first call.
Partition and cluster every table. Set maximum bytes billed per query. Custom quotas per project. No `SELECT *` on unpartitioned tables reaching your invoice. BigQuery billing is by bytes scanned — the cost control is in the schema design and query governance.
Autopilot for most workloads — Google manages the nodes, you pay per pod, no idle node cost. Standard when you need GPU node pools, custom machine types, or specific node configurations. Most apps don't need Standard.
Yes. The engineers who write the RFC ship the infrastructure. No handoff mid-engagement. Direct access throughout.
Yes. We audit what's there, tag what's untagged, right-size what's over-provisioned, and deploy new workloads with Terraform alongside existing resources. No rip-and-replace.

Founder-direct

Tell us whatyou're building.

Thirty minutes with the founder. We'll bring a senior GCP architect, the relevant playbook, and a candid read on whether Google Cloud is the right platform — or whether AWS or Azure fits your workload better.

Send a written briefAsync — scoping doc back in 48hhello@entalogics.comEmail — replies within 24hChat on WhatsAppFaster, founder-direct