Architecture advice and implementation for clients who run on Google Cloud or are considering it. We design scalable, secure and cost-efficient GCP environments, and we also build them. We are vendor-neutral: we know AWS and Azure just as well, and we will not recommend Google Cloud if something else suits you better.
Google Cloud consulting at Appfront is not an abstract architecture exercise separate from delivery. For years we have worked with Google Cloud for clients who run production workloads on the platform: web applications, customer portals, data platforms on BigQuery, an integration layer via Pub/Sub and, increasingly, AI solutions built around Vertex AI and the Gemini API. The conversations about architecture, security and cost are held with the same people who then deliver the work.
We are a Dutch software builder with a cloud practice. Our position is deliberately independent: we are not a reseller, we have no licence quotas, and we have no incentive to sell you more Google Cloud services than you need. If Azure serves you better for a particular workload, we will say so. We always start from your situation, not from a vendor's portfolio.
Google Cloud consulting is an umbrella over several more specific engagements. If you want a broader starting point, begin with general digital consulting. If you have just decided to move to the cloud, you often start with cloud migration support. And for clients still choosing a platform, see also our parallel pages on AWS consulting and Azure consulting: the same principles, different tools.
The question "do you offer Google Cloud consulting?" breaks down into quite different areas of work. Below are the six that come up most often in our practice, each in different proportions depending on the engagement.
A suitable GCP design for your workload: which services to use, how they are divided across projects and folders within the resource hierarchy, what network segmentation to apply via Shared VPC, and which redundancy approach to take using multi-zonal or multi-regional deployments. Not a reference architecture lifted from a whitepaper, but a design that fits your traffic patterns, team size and growth assumptions.
A structured review against the pillars of the Google Cloud Architecture Framework: operational excellence, security, reliability, cost, performance and sustainability. We examine the Terraform code, IAM bindings, VPC topology and runtime metrics in Cloud Monitoring. At the end you receive a prioritised list of concrete next steps.
GCP bills tend to climb in a way that rarely feels linear to the business. We analyse where the money actually goes — over-provisioned Compute Engine, expensive cross-region egress, unused static IPs, missing Committed Use Discounts, poorly placed BigQuery slots — and build an approach to make the sensible savings structural.
Folder and project hierarchy with Organisation Policies, identity via Cloud Identity, machine identity via Workload Identity Federation (no more long-lived service account keys), encryption via Cloud KMS, network segmentation via VPC Service Controls and Private Service Connect, audit logging via Cloud Audit Logs, threat detection via Security Command Center. We work from a clear information security policy — not from a checklist after the fact.
From on-premises, from AWS or from a SaaS context. We map the existing landscape, choose the right migration strategy for each workload (rehost, replatform, refactor or replace), and build the migration path without bringing the business to a standstill. For the wider context, see cloud migration guidance.
If you're already on Google Cloud but run a lot of "lift-and-shift" workloads, there is value to be gained from modernising: containers via GKE or serverless via Cloud Run, event-driven flows via Pub/Sub and Eventarc, short transactions on Cloud Functions, an API layer via API Gateway or Apigee. Not as an end in itself, but where it becomes simpler, cheaper or more robust.
It depends on where your GCP practice stands and which decision you have in front of you. We determine together in the first conversation which form fits — often a engagement starts with a review and grows into an implementation phase.
A structured scan of your existing GCP environment against the pillars of the Google Cloud Architecture Framework. We review IAM bindings, Organisation Policies, VPC topology, cost drivers, observability via Cloud Logging and Cloud Monitoring, backup strategy and deployment pipelines. At the end you receive a findings report plus a prioritised list — not an endless document, but concrete improvements your team can start on tomorrow.
A design for a new Google Cloud environment or a redesign of an existing one. A GCP landing zone following Google's Enterprise Foundations Blueprint: organisation and folder hierarchy, project vending, Shared VPC with hub-and-spoke, identity baseline in Cloud Identity, baseline policies via the Organisation Policy Service and an infrastructure-as-code approach using Terraform with the Cloud Foundation Toolkit. At the end you have an environment in which your teams can build safely and predictably.
Strategy without execution remains theory. In this engagement we guide not only the architectural choices but also their realisation — migrating workloads, CI/CD via Cloud Build and Artifact Registry, modernising to GKE, Cloud Run or Cloud Functions, observability via Cloud Logging, Cloud Trace and Cloud Profiler. We stay on board until it works in production and your team can carry on building independently.
Google Cloud now has hundreds of services. To be honest: there is no partner who knows all of them in depth, and neither do we. What we do is deliberately choose the services we work with on a regular basis in client contexts — and where we have deep practical expertise.
For compute, Cloud Run is often the starting point: managed containers, scales to zero, low operational overhead, and suitable for the vast majority of modern web applications and APIs. GKE where the team has Kubernetes maturity: Autopilot for managed nodes, Standard for finer-grained control. Cloud Functions for event-driven or short-lived code. For data, the focus lies on BigQuery, discussed separately below, complemented by Cloud SQL, Spanner for globally distributed transactional systems, AlloyDB for PostgreSQL-compatible OLTP, Firestore for document stores and Cloud Storage for object storage.
For security and identity we use Cloud Identity, Identity Platform or Firebase Authentication for customer-facing authentication, Cloud KMS and Secret Manager, and VPC Service Controls as a perimeter around sensitive data stores. For AI workloads, Vertex AI and the Gemini API are the two services we work with most: Vertex AI as a managed platform for training, fine-tuning, deployment and evaluation, plus the Gemini API for multimodal foundation models. Document AI for data extraction from unstructured documents, Vision AI for image recognition. For event architecture, Pub/Sub, Eventarc and Workflows; for API management, API Gateway and Apigee. For observability, Cloud Logging, Cloud Monitoring, Cloud Trace, Cloud Profiler and Error Reporting. For an AI project on Google Cloud, see AI development.
A large part of our Google Cloud engagements revolve around data. BigQuery is the reason many organisations choose GCP in the first place: a serverless, columnar warehouse that scales without you maintaining clusters, with SQL as the interface and a pricing model that separates storage from compute. That is fundamentally different from a traditional warehouse you pay for around the clock.
What makes BigQuery particularly interesting is that it is built with AI in mind. BigQuery ML lets you train models using SQL statements directly on your data. BigQuery integrates with Vertex AI for heavier models and with Gemini for multimodal queries, so you can ask questions of text, image and structured data within the same session. BigQuery supports Apache Iceberg as an open table format, and with BigLake you can run the query engine over data in Cloud Storage, Amazon S3 or Azure Blob Storage without copying it. That opens the door to data lakehouse architectures that are not tied to a single cloud.
In practice, we design data platforms where Pub/Sub or Datastream handles ingestion, Dataflow runs the streaming and batch transformations, dbt or Dataform structures the modelling, and BigQuery forms the central warehouse. Looker or Looker Studio sits on top for BI, and where needed Vertex AI Pipelines for heavier ML workflows.
No vague deliverables, but artefacts your cloud team, security officer and business owners can put to use straight away.
Six situations in which clients approach us for GCP work. If you recognise one of them, we would be happy to continue the conversation. A no-obligation chat is enough to see whether the fit is right.
You are starting a new platform and have chosen Google Cloud as its foundation, often because BigQuery, Vertex AI or the developer experience of Cloud Run tipped the balance. You want to get it right first time: a clean folder and project structure, IaC via Terraform, an identity baseline in Cloud Identity, and deployment pipelines. Not a messy proof of concept that later has to be painstakingly cleaned up first.
You are building a serious data platform and have chosen BigQuery at its heart, often because serverless pricing, BigQuery ML, Iceberg support and Vertex AI integration suit where your team wants to go. You are looking for a partner who designs ingestion (Pub/Sub, Datastream, Dataflow), modelling (dbt, Dataform), governance (Dataplex) and the BI layer (Looker) as one coherent whole.
You already run on Google Cloud, but the monthly bill has become structurally higher than the business expects. You suspect overcapacity, expensive cross-region egress, BigQuery queries consuming slot budget, or missing Committed Use Discounts, but the exact picture is lacking. A Well-Architected review focused on the cost pillar brings the insight to the table and provides levers to address it structurally.
Your sector places demands on data residency, logging and incident procedures: GDPR, DORA for financial services providers, NIS2 for essential and important entities, NEN 7510 for healthcare. The Google Cloud environment must demonstrably comply, and you are looking for a partner who gets both the design and the evidence in order. See also ISO 27001-compliant software development.
You are experimenting with Vertex AI and the Gemini API and want to take it from prototype to production, with network isolation via Private Service Connect, content filtering, prompt logging that stays within the EU data boundary, cost monitoring per model and a fallback strategy for model deprecations. We have walked that path many times and know where the sharp edges are.
The platform is running in production, clients are enthusiastic, and growth is outpacing the architecture it was designed for. Single-region becomes multi-region, a handful of Cloud Run services becomes dozens, and the original choices start to chafe. You are looking for a partner to take it to the next level of maturity.
For European customers, data residency is often not a minor detail but a basic requirement. Google Cloud offers several EU regions. For Dutch customers, europe-west4 is often a logical choice: the data centre is located in Eemshaven (Groningen), with low latency to the rest of the Netherlands. For disaster recovery, europe-west1 (Saint-Ghislain, Belgium) is a natural partner, and europe-west3 (Frankfurt) is a second good option. Which region suits you depends on your customer base, legal context and service availability, as not every service runs in every region. On top of that, Google offers an EU Data Boundary for customer and support data, a commitment that data for covered services stays within European regions. It is no silver bullet, as data can still leak through incorrectly configured logging destinations or cross-region features, but the framework for data sovereignty on GCP has matured considerably.
For financial service providers, the DORA regulation has applied since 2025: digital operational resilience, exit strategies for critical ICT providers and third-party risk management. For healthcare providers, NEN 7510 remains the framework. Essential and important entities are subject to obligations under NIS2. And for all personal data, the GDPR applies. We configure the Google Cloud design so that it demonstrably supports these frameworks, with evidence drawn systematically from Cloud Audit Logs, Security Command Center and Access Transparency.
A specific point of attention is vendor lock-in without an exit plan. Some managed services gradually make it difficult for an organisation to move away. For every significant service choice, we put the dependency explicitly on the table: what would an exit look like, and do the benefits outweigh the portability you give up? For BigQuery, Iceberg support and BigLake can play a role in an exit strategy, as the data sits in an open format that other engines can also read.
A first conversation in which we understand where your Google Cloud practice stands, which decision is on the table and what the constraints are, in terms of budget, compliance, team skills and timing. This is followed by a brief proposal phase in which we agree which form, whether review, architecture design or guided implementation, best suits your needs.
We obtain read access to your Google Cloud organisation and review Terraform repositories, IAM bindings, Organisation Policies, VPC topology, Cloud Billing and monitoring data. We also speak with cloud engineers, the security officer and application owners, not to know everything, but to ask the right questions.
Across a number of focused sessions, we lay out the options: service choices, folder and project structure, network blueprint (Shared VPC versus Private Service Connect), identity and security baseline, data platform approach around BigQuery, and deployment approach. No black-box analysis: your team takes the decisions along with us and retains ownership.
We translate the choices into a phased plan with sequencing, team allocation and investment. Concrete enough for the board or finance team, flexible enough to adjust as insight develops.
If you wish, we stay on board for the realisation, as a building partner, programme manager or interim cloud engineer. That way advice and delivery are carried out by the same people, and the engagement only concludes once the Google Cloud environment is running in production and your team can continue building on it independently.
What clients usually ask us before we start: honest answers, no sales talk.
An introductory, no-obligation conversation with no sales pressure. We listen to your situation, ask questions and give direction that is genuinely useful to you, even if it turns out that an engagement with us is not the right next step, or that another cloud provider is a better fit.
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