Google Cloud

Google Cloud grew 63% in Q1 2026 to $20B revenue — driven by enterprise AI Solutions and AI Infrastructure. Revenue from products built on Google's generative AI models grew 800% YoY. Gemini Enterprise saw 40% QoQ growth in paid MAUs. The cloud backlog doubled year-over-year to $460B+, indicating committed future spend from enterprises standardizing on GCP for AI workloads.

Vertex AI is Google Cloud's unified AI platform — model training, fine-tuning, serving, evaluation, and MLOps in one environment. Key advantages over SageMaker: Gemini model access with Model Garden (50+ foundational models), TPU v5 training clusters at lower cost than equivalent GPU instances, RAG Engine and Agent Builder as higher-level abstractions, and tighter BigQuery integration for in-database ML. SageMaker has more mature batch processing and broader AWS service integration. For AI-first teams, Vertex AI's unified model-to-production workflow is more cohesive.

BigQuery is serverless — no clusters to provision, no capacity planning, no tuning. It scales to petabytes automatically and you pay per query (on-demand) or per slot-hour (capacity). BigQuery ML lets you train and run ML models with SQL, eliminating the need to move data to a separate ML platform. Partitioning and clustering cut query costs dramatically. The closest equivalent is Snowflake (managed, not serverless) or Redshift Serverless (less capable for ML-native analytics).

Cloud Run is the right choice when: each service is a single container, traffic is variable and scale-to-zero matters, you want automatic HTTPS and traffic splitting, and your team doesn't want to manage Kubernetes. GKE is the right choice when: you need custom networking policies, multi-container pods, Kubernetes CRDs, stateful workloads, or are already running Kubernetes on other clouds. GKE Autopilot removes node management — it's between the two. We recommend starting with Cloud Run and migrating to GKE when specific Kubernetes requirements emerge.

GCP infrastructure costs depend on compute choices (Cloud Run is pay-per-request, GKE Autopilot is pay-per-pod, BigQuery is pay-per-query), data volumes, and AI API usage. Sustained Use Discounts apply automatically to GKE and Compute Engine. Committed Use Contracts save 57% on steady-state compute. Development effort depends on architecture complexity, existing codebase, and team size. Share your requirements and we'll provide a detailed estimate covering both infrastructure projections and development costs.

GCP provides VPC Service Controls for API perimeter protection, Binary Authorization for container image attestation, Cloud Armor for DDoS and WAF, Workload Identity for keyless service authentication, Secret Manager for credentials, and Security Command Center for posture management. Compliance certifications include ISO 27001, SOC 2/3, PCI DSS, HIPAA, and FedRAMP Moderate. We configure all security controls through Terraform and run Security Command Center findings as part of continuous compliance checks.

Yes — Firebase and GCP are the same project. Firebase Authentication issues tokens that Cloud Run and Cloud Functions validate via Firebase Admin SDK. Firestore documents can trigger Cloud Functions for backend processing. Firebase Extensions use GCP services under the hood. We architect clean boundaries: Firebase for client-facing real-time features, Cloud Run for backend APIs, BigQuery for analytics, and Vertex AI for ML — all sharing the same GCP project, IAM, and billing.

Google's TPU v5 (Tensor Processing Unit) infrastructure provides 3–5× better price-performance for training large language models compared to equivalent A100/H100 GPU instances. Vertex AI Training jobs manage TPU cluster provisioning and teardown automatically. For teams fine-tuning Gemini or training custom models at scale, TPU v5 clusters accessed through Vertex AI Training significantly reduce training costs versus AWS or Azure GPU instances.

We use Terraform with Google Cloud provider for all GCP provisioning — VPCs, GKE clusters, Cloud SQL instances, Pub/Sub topics, and IAM bindings. For GKE configuration, we use Helm charts deployed by Argo CD for GitOps. Config Sync (GKE Enterprise feature) applies cluster configurations from Git. We structure Terraform into environment-separated modules with remote state in GCS, and enforce policies with Sentinel or OPA for production environments.

We offer Google Cloud managed support covering cost optimization (BigQuery slot analysis, committed use planning), GKE version upgrade management, Vertex AI pipeline monitoring, Security Command Center reviews, and architecture evolution guidance. We also provide team training on GCP services and patterns. Support retainers are scoped to your environment's complexity and operational maturity.