What Is Google Cloud Platform and Why SpeedMVPs Uses It
Google Cloud Platform is Google's public cloud offering, covering compute, storage, databases, networking, and AI/ML services under one roof. For AI product teams, the most relevant services are Vertex AI (managed model training, evaluation, and serving), Cloud Run (serverless containers), BigQuery and BigQuery ML (analytics and in-database machine learning), AlloyDB and Cloud SQL (managed relational databases), and Pub/Sub for event-driven architectures. SpeedMVPs reaches for GCP in specific scenarios rather than by default. If a client's data is already in Google Drive, BigQuery, or Workspace, GCP becomes the natural home for the AI layer - pulling data out to a different provider adds latency, cost, and GDPR complexity around data transfers. GCP is also the right choice when Vertex AI's AutoML or custom model serving removes enough engineering overhead to justify the ecosystem lock-in. Cloud Run is a particularly clean option for containerised AI backends: it scales to zero, handles concurrency automatically, and integrates directly with Artifact Registry and Cloud Build for CI/CD. For teams operating under EU AI Act obligations or NHS Digital frameworks, GCP's UK and EU data regions (including europe-west2 in London) provide auditable data residency that satisfies ICO expectations around personal data processing in AI systems.
Setting Up Google Cloud Platform in a Production AI Project
Getting GCP right from the start avoids the security and cost surprises that catch teams out at scale. Here is the setup sequence SpeedMVPs follows on every GCP-backed AI MVP. First, create a dedicated GCP project for the product with a separate project for each environment (dev, staging, production). Enable billing alerts immediately - GCP costs can spike fast with GPU-backed Vertex AI endpoints or large BigQuery scans. Second, configure IAM with least-privilege service accounts. Your Cloud Run service gets one service account with only the roles it needs: Secret Manager accessor, Cloud SQL client, Storage object viewer. Never use the default compute service account in production. Third, store secrets in Secret Manager rather than environment variables or config files. Reference them in Cloud Run by secret name, not by value, so rotation does not require redeployment. Fourth, set up Artifact Registry as your container registry. Build images via Cloud Build triggered on GitHub pushes, tag them with the commit SHA, and deploy to Cloud Run using the immutable digest rather than a mutable tag like "latest". Fifth, for the AI layer, create a Vertex AI endpoint only if you are serving a fine-tuned or custom model. For standard LLM inference, use the Vertex AI Model Garden API (which wraps Gemini and partner models) or call external LLM APIs directly from Cloud Run. Vertex AI endpoints carry a minimum cost even at zero traffic, so do not provision them speculatively. Sixth, configure VPC Service Controls if you are processing sensitive data - this restricts which GCP services can communicate with which, and provides the audit trail ICO expects for high-risk AI processing under UK GDPR Article 35 Data Protection Impact Assessments. Finally, set up Cloud Monitoring with custom dashboards for LLM latency, error rates, and token consumption. Export logs to BigQuery for long-term retention and compliance queries.
Key Features and Capabilities
Vertex AI is GCP's flagship AI service and the main reason AI teams choose the platform. It covers the full model lifecycle: dataset management, training jobs, evaluation, model registry, and online or batch prediction endpoints. The Model Garden gives access to Google's Gemini models, open-source models like Llama and Mistral, and partner models, all through a unified API with enterprise SLAs. BigQuery ML is underrated for AI MVPs. It lets you run classification, regression, clustering, and even LLM inference directly inside BigQuery using SQL syntax. For products where the AI task is fundamentally analytical - churn prediction, document classification, anomaly detection over structured data - BigQuery ML removes the need for a separate model serving layer entirely. Cloud Run is the right default compute for AI backends. It runs any container, handles HTTP and gRPC, scales from zero to thousands of instances automatically, and integrates with Cloud Tasks for async workloads and Pub/Sub for event-driven processing. For long-running AI tasks (document processing, batch embeddings), Cloud Run Jobs provides a clean execution model without the complexity of Kubernetes. AlloyDB is GCP's PostgreSQL-compatible database with columnar storage for analytics and built-in pgvector support. For AI products that need both transactional data and vector search in one database, AlloyDB avoids the operational overhead of running a separate vector store while offering better performance than standard Cloud SQL for mixed workloads. GCP's data residency controls are genuinely useful for UK and EU compliance. The london region (europe-west2) satisfies ICO data transfer requirements, and Organisation Policies can enforce that resources are only created in approved regions, making compliance audits straightforward.
Real-World Workflow: Google Cloud Platform in an AI MVP
Consider a typical scenario SpeedMVPs has delivered: an AI document review tool for a UK professional services firm. The client had ten years of case documents in Google Drive and needed an AI layer that could answer natural-language questions against that archive with citations. The architecture used Google Drive as the source of truth, syncing documents to Cloud Storage via a Cloud Scheduler job. A Cloud Run worker processed new documents: chunking text, generating embeddings via the Vertex AI embeddings API, and storing vectors in AlloyDB with pgvector. The query API ran on Cloud Run, retrieving relevant chunks from AlloyDB, assembling a prompt with Gemini 1.5 Pro via the Vertex AI API, and streaming the response back to the frontend. All processing stayed within europe-west2. The client's data never left GCP's London region, satisfying the firm's GDPR data processing obligations and their professional indemnity insurer's requirements around client data. The VPC Service Controls configuration meant that only the approved Cloud Run services could access AlloyDB and Cloud Storage - lateral movement within the project was prevented at the network level. BigQuery received all query logs, user interactions, and LLM response metadata. This gave the client a compliance audit trail for the ICO and a product analytics dataset that required no additional tooling. The whole system was delivered in three weeks from project kick-off, with Terraform managing every GCP resource so the client's internal team could audit, modify, and extend the infrastructure without depending on SpeedMVPs after handover.
Cost and Pricing Considerations
GCP pricing is complex and the AI services carry meaningful minimum costs that founders often underestimate. Vertex AI online prediction endpoints charge per node hour even at zero traffic - a single n1-standard-4 node in europe-west2 costs roughly GBP 0.17 per hour, so an idle endpoint costs around GBP 120 per month before a single prediction is served. For most AI MVPs, this is wasteful. Use Cloud Run with direct Gemini or Vertex AI API calls instead, and reserve dedicated endpoints for custom models that genuinely need them. BigQuery charges per byte scanned on on-demand pricing. A naive query over a large dataset can generate surprising bills. Partition tables by date, cluster on frequently-filtered columns, and use materialised views for common aggregations. BigQuery ML inference costs depend on the model used - Gemini inference in BigQuery is priced per character, not per query. Cloud Run is cost-efficient for AI backends. You pay per CPU-second and memory-second consumed, and scale-to-zero means idle services cost nothing. The main cost risk is long-running containers that do not complete within the 60-minute timeout - use Cloud Run Jobs for those workloads. Google's Committed Use Discounts (CUDs) offer 37 to 55 percent savings on compute for 1 or 3-year commitments. For established AI products with predictable workloads, CUDs on Cloud Run and Cloud SQL are straightforward to model. SpeedMVPs includes cost modelling in every GCP project handover so clients understand their burn rate before the first user signs up.
Alternatives to Google Cloud Platform
AWS is the most direct alternative and the default choice for teams without an existing GCP dependency. AWS has a larger service catalogue, more mature third-party tooling, and broader enterprise procurement frameworks in the UK public sector. Bedrock provides managed LLM access similar to Vertex AI Model Garden. The tradeoff is operational complexity - AWS services tend to require more configuration than their GCP equivalents. Azure is the right choice for organisations already on Microsoft 365, with Azure OpenAI Service providing UK-hosted GPT-4o access under enterprise SLAs. Azure's compliance certifications (ISO 27001, Cyber Essentials Plus, NHS DSP Toolkit alignment) make it the default for UK public sector AI projects. Vercel and Railway handle the compute layer for most AI MVPs without the overhead of a full cloud platform. If you do not need Vertex AI, BigQuery ML, or GCP-specific services, deploying a Next.js backend on Vercel or a containerised API on Railway is significantly simpler and cheaper at MVP scale. For teams that specifically need EU data residency without GCP, Mistral AI's API is hosted in EU data centres, and Scaleway (a French cloud provider) offers an alternative to US hyperscalers for GDPR-sensitive workloads. SpeedMVPs assesses the right platform for each project based on data residency requirements, existing ecosystem dependencies, and the specific AI services needed.