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Cloud & Infrastructure

Google Cloud

Google Cloud where it genuinely leads — data, analytics and ML — built and operated by senior engineers who will tell you when it is the wrong platform.

Overview

Google Cloud Platform is Google’s public cloud, and it carries the fingerprints of the company that built it. The parts of GCP that came out of Google’s own infrastructure — its data warehouse, its container platform, its global network — are among the best-engineered services in any cloud, because Google ran them at planetary scale internally for years before selling them. BigQuery, GKE and the network backbone are not me-too products chasing AWS; they are the reason a lot of teams choose GCP in the first place, and they are where the platform is genuinely ahead rather than merely competitive.

We build and operate GCP workloads with a clear view of where it wins and where it does not. Where Google Cloud is strongest is data-heavy and ML-heavy work: a serverless data warehouse you can query without managing a single node, a managed Kubernetes platform from the people who created Kubernetes, and an ML stack that runs on the same hardware Google uses for its own models. For a data platform, an analytics pipeline, or a machine-learning system, GCP is frequently the right answer on the merits. We design those systems, wire them together, and run them in production rather than handing over a diagram and leaving.

We are equally blunt about the other side. GCP has a smaller market share than AWS, a thinner catalogue of services, a smaller partner and consultant pool, and a reputation — earned — for deprecating products people had come to depend on. For a generic enterprise workload with no particular data or ML centre of gravity, AWS or Azure often have deeper coverage, more mature tooling around the edges, and an easier hiring market. We will say so before you commit, because picking a cloud is a decision you live with for years, and the right one depends on what you are actually building.

Best for — Data-heavy and ML-heavy workloads — data warehouses, analytics pipelines, and machine-learning systems — plus container-first architectures on Kubernetes, where BigQuery, Vertex AI and GKE are genuinely ahead of the alternatives.

Why teams choose Google Cloud

  • A serverless data warehouse that just runs

    BigQuery separates storage from compute and manages both for you — there is no cluster to size, patch or tune, and it will scan terabytes in seconds by throwing Google’s infrastructure at the query. For analytics workloads this removes an entire operational discipline that a self-managed warehouse demands, and the pay-per-query model means you are not paying for an idle cluster overnight.

  • The strongest ML and AI platform of the big clouds

    Vertex AI, access to TPUs, and native integration with BigQuery mean the path from data in the warehouse to a trained, served model is shorter on GCP than anywhere else. If machine learning is central to what you are building rather than bolted on, this integration is a real advantage, not a marketing one.

  • Google’s network and Kubernetes pedigree

    GCP runs on the same global backbone that serves Search and YouTube, so cross-region traffic rides Google’s private network rather than the public internet, and GKE is managed Kubernetes from the team that created Kubernetes. For container-first, globally distributed systems, both are best-in-class rather than adequate.

Why businesses choose Google Cloud

  • Your centre of gravity is data or machine learning, and you want the platform that is genuinely ahead on both rather than the one with the biggest catalogue of everything.
  • You are building on Kubernetes and containers and want the most coherent managed story, from GKE for orchestrated workloads to Cloud Run for stateless services with no cluster to run.
  • You value clean APIs, a strong global network, and a serverless-first approach to data, and you would rather manage less infrastructure than tune more of it.
  • You want senior engineers who will tell you honestly when GCP is the right platform and when AWS or Azure would serve your particular workload better — before you commit, not after.

What we build with Google Cloud

The capabilities this technology is genuinely strong at — and what we most often build with it.

  • BigQuery for the data warehouse

    BigQuery is the anchor of most GCP data work we do: a serverless, columnar warehouse that separates storage from compute, scales query capacity automatically, and integrates with the ML tooling so you can train models on warehouse data in place. We design the schema, partitioning and clustering deliberately, because query cost on BigQuery is driven by bytes scanned, and a careless table layout turns a cheap warehouse into an expensive one.

  • Vertex AI for machine learning

    Vertex AI unifies training, tuning, serving and pipelines for machine-learning workloads, with access to Google’s TPUs for the training-heavy jobs where they pay off. We use it where ML is genuinely central — model serving behind an endpoint, batch prediction on warehouse data, managed pipelines — rather than reaching for it when a simpler hosted model call would do.

  • GKE and Cloud Run for containers

    For orchestrated, stateful or complex workloads we run GKE, Google’s managed Kubernetes, tuned for autoscaling and cost. For stateless services that do not need a full cluster, Cloud Run runs containers serverlessly — scaling to zero when idle and up under load — which removes an entire operational surface. We pick between them by workload, not by habit.

  • Cloud Functions and Pub/Sub for event-driven work

    Cloud Functions handle event-driven glue and lightweight endpoints, and Pub/Sub provides managed, globally available messaging for decoupling services and feeding data pipelines. We build event-driven architectures on this pairing where the workload is genuinely asynchronous, and we are careful about where eventual consistency and delivery semantics actually matter to the business.

  • Cloud Storage, Spanner and Bigtable for data at rest

    Cloud Storage handles objects, backups and the data-lake layer; Cloud Spanner provides horizontally scalable, strongly consistent relational data for workloads that have genuinely outgrown a single database; and Bigtable serves wide-column, high-throughput access for time-series and large operational datasets. We choose the store to fit the access pattern rather than forcing everything into one.

  • The global network and networking primitives

    GCP’s VPC is global by default, and cross-region traffic rides Google’s private backbone rather than the public internet, which makes multi-region architectures faster and simpler to reason about than on clouds where the network is regional. We use global load balancing, private service access and the network’s reach to keep latency low without the plumbing overhead other platforms impose.

Use cases

  • Data warehouse and analytics platform

    A serverless analytics platform on BigQuery, fed by Pub/Sub and batch loads, where teams query terabytes without managing a cluster and cost is driven by usage rather than an always-on machine. This is GCP at its strongest, and the workload it suits best.

  • Machine-learning systems on warehouse data

    Training and serving models with Vertex AI directly on data that already lives in BigQuery, from batch prediction pipelines to real-time endpoints, where the short path between the warehouse and the ML tooling is the whole reason to be on GCP.

  • Container-first services on GKE and Cloud Run

    Kubernetes-native platforms on GKE for complex, stateful workloads, with Cloud Run absorbing the stateless services that do not need a cluster — a coherent container story from the people who created Kubernetes.

  • Event-driven and streaming pipelines

    Real-time ingestion and processing built on Pub/Sub, Cloud Functions and BigQuery, decoupling producers from consumers and landing data in the warehouse for analytics, where the messaging backbone and warehouse integration do the heavy lifting.

When Google Cloud is the right choice

  • Your workload is data-heavy — a warehouse, an analytics platform, event pipelines, or reporting at scale. BigQuery’s serverless model, where you pay for queries and storage rather than provisioning and tuning a cluster, is a genuine advantage that AWS and Azure have spent years trying to match, and it is often the single best reason to choose GCP.
  • You are building machine learning or AI into the product. Vertex AI, access to Google’s TPUs, and the tight integration between BigQuery and the ML tooling make GCP the strongest of the three big clouds for training and serving models, particularly when the data already lives in the warehouse.
  • You are committed to Kubernetes and containers. Google created Kubernetes, and GKE remains the most polished managed offering, with Cloud Run providing a serverless container platform that removes cluster management entirely for stateless services. If your architecture is container-first, GCP’s story is coherent end to end.
  • Wrong for: a generic enterprise workload with no data or ML centre of gravity. If you are running standard line-of-business applications, a mixed bag of Windows and .NET, or you need the widest possible catalogue of managed services, AWS or Azure will usually give you deeper coverage and an easier hiring market. GCP’s advantages do not show up on that kind of work.
  • Wrong for: an organisation that cannot tolerate a service being deprecated. Google’s track record of retiring products is real, and while its core cloud services are stable, the caution is legitimate. If long-term vendor commitment on every service is a hard requirement, weigh that honestly rather than assuming it away.

Google Cloud: pros and cons

Strengths

  • BigQuery is a genuinely category-leading serverless data warehouse — no cluster management, fast on huge datasets, and priced per query and per byte stored, which suits spiky analytics workloads that would waste money on an always-on cluster.
  • The ML and AI story is the strongest of the three major clouds: Vertex AI, TPUs, and tight BigQuery integration make GCP the natural home for data-centric machine learning.
  • Kubernetes and containers are first-class. Google created Kubernetes, GKE is the most polished managed offering, and Cloud Run gives you serverless containers with no cluster to run at all.
  • The developer experience is clean — well-designed APIs, coherent tooling, and a global network on Google’s own backbone that makes multi-region networking simpler and faster than stitching it together elsewhere.

Trade-offs

  • Market share and ecosystem are smaller than AWS. That means fewer third-party integrations built GCP-first, less community content when you hit an edge case, and a smaller pool of engineers who already know the platform deeply.
  • The service catalogue is thinner than AWS’s. For a niche managed service or a very specific piece of infrastructure, AWS often has an offering GCP does not, and you end up building or self-hosting what you could have consumed elsewhere.
  • The partner and consultant pool is smaller. Finding certified specialists, agencies and contractors is harder than for AWS or Azure, which matters for staffing, second opinions and long-term support.
  • Google has a reputation for deprecating products, and it is deserved. The core cloud services are stable, but the company’s wider history of retiring things makes some organisations wary of committing, and that caution is not irrational — it is a real factor to weigh.

Architecture

The first architectural decision on GCP is honest scoping: is this a workload that plays to Google’s strengths, or one that happens to be going on GCP because someone picked the cloud before the workload was understood? For data and ML systems, the architecture almost writes itself around BigQuery as the warehouse, Pub/Sub as the messaging spine, and Vertex AI where models live — and the platform rewards that shape. For a generic enterprise workload, we design more defensively, aware that some services we would reach for on AWS simply do not have an equivalent here.

We lean on the parts of GCP that are global by design — the VPC, the load balancing, the network backbone — to keep multi-region architectures simpler than they would be elsewhere, and we choose the data store by access pattern rather than defaulting to one. Spanner for strongly consistent relational data that has outgrown a single node, Bigtable for high-throughput wide-column access, Cloud Storage for the lake and objects, BigQuery for analytics. We keep services loosely coupled through Pub/Sub where the workload is genuinely asynchronous, and we resist the temptation to spread across a dozen managed services when three would do, because every service added is another thing to operate, secure and understand.

Performance

BigQuery’s performance is its headline feature: it will scan enormous datasets in seconds by parallelising across Google’s infrastructure, and it does so without you provisioning or tuning anything. The engineering work moves from managing a cluster to managing query cost, because performance and price are both driven by bytes scanned. We design partitioning and clustering so that queries touch only the data they need, materialise expensive aggregations where it pays, and watch the query plan rather than trusting that serverless means free — a badly shaped table scans the whole thing every time and the bill shows it.

Across the rest of the platform, the global network is the quiet performance advantage. Cross-region traffic on Google’s private backbone avoids the public internet, so multi-region latency is lower and more predictable than on clouds where you stitch regions together yourself. On the compute side we size GKE and Cloud Run for the real load profile, use Cloud Run’s scale-to-zero for spiky or intermittent services, and reach for TPUs on Vertex AI only where the training workload genuinely benefits — they are a real advantage for the right jobs and wasted money for the wrong ones.

Security

GCP gives you strong security primitives, and its identity model — IAM built around resource hierarchy, projects, folders and the organisation — is coherent once you understand it. We design that hierarchy deliberately, granting least-privilege roles at the right level rather than scattering broad permissions, using service accounts with tight scopes, and keeping the blast radius of any single credential small. VPC Service Controls, Cloud KMS for key management, and default encryption at rest and in transit are the building blocks we assemble rather than assume.

The honest caveat is the same as on any cloud: the platform gives you the tools, but a secure system is your responsibility, not a default you inherit. Misconfigured IAM, an over-permissioned service account, a public storage bucket, or a gap in the resource hierarchy will undo good primitives. We treat identity and network boundaries as first-class design work, audit permissions rather than accreting them, and use GCP’s own tooling — Security Command Center, audit logs — to watch for drift. Google removes some categories of undifferentiated security work; it does not make an application secure on its own.

Scalability

Scalability is where GCP’s pedigree shows most clearly, because the flagship services were built to scale before they were sold. BigQuery scales query capacity without you sizing a cluster, Spanner scales a strongly consistent relational database horizontally in a way that is genuinely rare, Bigtable handles enormous throughput, and Pub/Sub absorbs message volume globally. For data and event-driven workloads, the platform’s ceiling is high and you reach it without re-architecting, which is exactly what you want from a cloud you are betting years on.

On the compute side, GKE autoscales pods and nodes to load, and Cloud Run scales stateless services from zero to many instances and back, so you pay for demand rather than peak. We design for this by keeping services stateless where possible, pushing state into the managed data layer, and using the global network to distribute load across regions without hand-built plumbing. The discipline is cost, not capacity: serverless scaling means an inefficient query or a runaway service scales your bill just as smoothly as your traffic, so we build in the observability and cost controls to catch it early.

Google Cloud integrations & ecosystem

The technologies we most often pair with it — each links to how we work with it.

How we work

We start by pressure-testing the cloud choice itself. If your workload plays to Google’s strengths — data, analytics, ML, containers — we will say so and get on with building. If it does not, and AWS or Azure would serve you better, we will tell you that before you commit, because we would rather lose a GCP project than build you a system on the wrong platform. Once the choice is settled, we design the identity hierarchy, the data architecture and the network boundaries early, since those are the decisions that are cheap to get right up front and expensive to unpick later.

From there we build in thin vertical slices, each deployed and running in a real GCP project rather than a slide, so the architecture meets production feature by feature. The engineers who design the system are the ones who write the Terraform, wire up the services and keep it running afterwards — that is what operating what we build means. It shows up as deliberate cost control on BigQuery and serverless compute, a lean set of services rather than a sprawl, and infrastructure your own team can read and own, because we would rather the system outlive our engagement than depend on it.

The service behind it

Delivered throughCloud Engineering

What we build with Google Cloud

The disciplines this technology most often shows up in — from a first build to taking over and stabilising an existing one.

How we deliver

  1. 01

    Discover

    We map the system, the constraints and the business it serves — including the parts nobody documented.

    Architecture brief

  2. 02

    Architect

    Decisions get made, written down and defended before a line of production code exists.

    Decision records

  3. 03

    Build

    Short cycles against working software. You see progress in the product, not in a status deck.

    Shipping increments

  4. 04

    Operate

    Monitoring, incident response and iteration. The system is alive, so the engagement is too.

    Runbooks & SLOs

Industries we use Google Cloud in

Domain knowledge changes what gets built. A few of the sectors we know before the first meeting.

Also in Cloud & Infrastructure

Related terms

Why teams choose us for Google Cloud

  • Senior engineers only

    GCP rewards judgement — the difference between a BigQuery schema that stays cheap and one that quietly scans terabytes per query is architecture, not luck. There are no juniors learning the platform on your bill and your data.

  • We operate what we build

    We run the GCP systems we design, so we optimise for years of predictable operation and cost, not a demo. That means deliberate query and service design, tight IAM, and observability built in rather than bolted on afterwards.

  • Honest about when GCP is wrong

    If your workload has no data or ML centre of gravity and AWS or Azure would give you deeper coverage and easier hiring, we will tell you before you commit. We would rather turn down the work than put you on the wrong cloud for years.

  • Fair comparison, not vendor loyalty

    We build across the major clouds and have no incentive to sell you GCP where it does not fit. Our recommendation reflects your workload, not a partnership badge, which is precisely why it is worth having.

Typical timeline

  1. 01

    Discovery and cloud fit

    One to two weeks confirming GCP is the right platform for the workload, then settling the data architecture, identity hierarchy and network design before build volume grows.

  2. 02

    First vertical slice

    Two to three weeks delivering one real capability end to end in a live GCP project — a working pipeline, a served model, a deployed service — to prove the design against reality rather than a plan.

  3. 03

    Iterative build

    Feature-by-feature delivery in short cycles, each deployed and running, with cost controls on BigQuery and serverless compute handled as we go rather than discovered on the invoice.

  4. 04

    Hardening and handover

    Cost and query optimisation, an IAM and security review, observability and alerting in place, and infrastructure-as-code documentation so your team can own and extend the platform.

How pricing works

  • Fixed-scope builds for well-defined systems — a data warehouse and pipeline, an ML platform, or a container migration to GKE and Cloud Run — quoted once we understand the data volumes and the workload shape.
  • Monthly senior engagement for ongoing GCP work where scope evolves and you want continuity from the engineers who designed and now operate the platform.
  • Focused audits and rescues for existing GCP estates — a BigQuery bill that has run away, an IAM hierarchy that has sprawled, or an architecture that fought the platform — priced by assessment.

Hire Google Cloud engineers

Need Google Cloud capacity on your own team? We embed named senior engineers into your existing team — reporting to your leads, working in your rituals — so you add capacity without a hiring cycle.

Hire Google Cloud engineers

Common questions

Should we choose GCP over AWS?

It depends entirely on the workload. If your centre of gravity is data, analytics or machine learning, GCP is often the better choice on the merits — BigQuery and Vertex AI are genuinely ahead, and the container story on GKE and Cloud Run is the most coherent of the big clouds. If you are running a generic enterprise workload with no particular data or ML focus, AWS usually has a deeper service catalogue, a larger ecosystem, and an easier hiring market, and it is frequently the safer pick. We assess your actual workload before recommending either, rather than defaulting to one.

What is BigQuery and why do teams choose GCP for it?

BigQuery is Google’s serverless data warehouse. Unlike a traditional warehouse, there is no cluster to provision, size, patch or tune — it separates storage from compute and scales query capacity automatically, scanning terabytes in seconds by throwing Google’s infrastructure at the problem. You pay for the bytes you store and the bytes your queries scan, so spiky analytics workloads do not pay for an idle cluster overnight. It is one of the strongest reasons to be on GCP, though it also means query cost is driven by table design, which is where careful partitioning and clustering earn their keep.

Is Google Cloud’s reputation for deprecating products a real risk?

The reputation is earned, and the caution is legitimate — but it needs proportion. Google has retired plenty of consumer products and some developer services, which has made organisations wary. In practice its core cloud infrastructure — BigQuery, GKE, Cloud Storage, the compute and networking primitives — is stable and long-supported, and that is what most serious workloads depend on. We factor deprecation risk into the services we choose, favouring the well-established core over anything that looks like it might be an experiment, and if long-term commitment on a specific service is a hard requirement, we weigh that honestly rather than waving it away.

Why is GCP considered strong for machine learning?

Two reasons, and they compound. First, Vertex AI gives you a unified platform for training, tuning, serving and pipelines, with access to Google’s TPUs for the training-heavy jobs where they pay off — this is hardware Google built for its own models. Second, and more importantly, the integration between BigQuery and the ML tooling means the path from data in the warehouse to a trained, served model is shorter than on the other clouds. If machine learning is central to your product and your data already lives in the warehouse, that short path is a genuine advantage rather than a marketing line.

How do you keep a GCP bill under control?

The same discipline that makes serverless convenient makes it easy to overspend, so we design for cost from the start rather than reacting to the invoice. On BigQuery that means partitioning and clustering tables so queries scan only what they need, materialising expensive aggregations, and watching query plans — because cost is driven by bytes scanned, not by a fixed cluster. On compute we use Cloud Run’s scale-to-zero for spiky services, size GKE to the real load profile, and reach for TPUs only where they genuinely pay off. Then we put observability and budget alerts in place so a runaway query or service is caught early, not at month end.

Building on Google Cloud?

A technical conversation with the engineers who would do the work. If we are not the right fit, we will say so on the call.

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