Cloud-Native Platforms on GCP
for Fintech and AI

Most of the infrastructure we build for clients runs on Google Cloud. We build cloud-native architectures on GCP for fintech and AI-powered platforms: serverless containers on Cloud Run, analytics warehouses in BigQuery, AI pipelines on Vertex AI, streaming through Pub/Sub, and infrastructure fully managed through Terraform.

Start on GCP → Vertex AI Expertise

Terraform

Infrastructure as Code

Cloud Run

Serverless container hosting

99.9%+

SLA availability for clients

Multi-region

Disaster recovery architecture

What We Build on GCP

Our GCP work ranges from greenfield cloud-native builds to migrations off AWS or on-premise infrastructure. We specialize in architectures that combine serverless compute, managed AI services, and real-time data pipelines, the combination most fintech and AI-first companies end up needing.

Everything we deploy is codified in Terraform: environments are repeatable, there's an audit trail, and rollback is always an option. We don't click through the console, every resource is version-controlled and goes through code review.

✓ Cloud Run for containerized APIs and microservices with auto-scaling to zero

✓ GKE (Kubernetes Engine) for complex, long-running workloads

✓ Cloud SQL and AlloyDB for managed relational databases

✓ Pub/Sub for event-driven microservice communication

✓ Secret Manager and IAM for a zero-trust security posture

✓ Cloud Armor and load balancing for DDoS-resistant entrypoints

Serverless API Platform

We deploy FastAPI or Node.js APIs to Cloud Run, with instant scaling from zero to thousands of requests per second. No EC2 sizing decisions, no idle compute costs.

GCP AI Stack

Vertex AI for model training and serving, Gemini APIs for LLM features, and Vertex AI Search for enterprise RAG, all running inside your own GCP project under your data controls.

Streaming Data Pipelines

Pub/Sub to Dataflow to BigQuery pipelines for real-time analytics. We design event processing to be idempotent, so transactions never get counted twice.

Observability and FinOps

Cloud Monitoring dashboards, structured logging through Cloud Logging, budget alerts, and cost anomaly detection. You get visibility into both system health and cloud spend.

Google Cloud FAQ

Why GCP instead of AWS or Azure for AI and data workloads?+

Google Cloud has the most integrated AI stack of the three: Vertex AI, Gemini APIs, BigQuery ML, and years of Google's internal ML infrastructure exposed as managed services. For clients building LLM-powered products or real-time analytics, that usually means faster time to production and lower cost per inference than AWS or Azure. That said, we work fine with clients who already have commitments on another cloud.

How do you manage infrastructure changes without breaking things?+

All infrastructure is managed through Terraform. Changes go through a pull request, with `terraform plan` output reviewed before any `apply`. We use remote state in GCS with state locking to prevent concurrent changes. Destructive changes, like dropping a database or deleting a VPC, need explicit approval. We also keep staging environments that mirror production, so changes get tested before they touch anything live.

We're on AWS. What does a migration to GCP look like?+

We start with a discovery phase: mapping existing AWS services to their GCP equivalents and figuring out what can move as-is versus what's worth rearchitecting. Databases need a careful migration plan (Cloud Database Migration Service handles most of this well). We run parallel environments during cutover and shift traffic gradually instead of flipping a switch. A mid-size application migration usually takes 6 to 12 weeks with no planned downtime.

How do you handle compliance requirements on GCP?+

GCP has one of the broadest compliance certification portfolios around (SOC 1/2/3, PCI DSS, HIPAA BAA, ISO 27001, and more). For regulated clients, we configure Customer-Managed Encryption Keys (CMEK), VPC Service Controls to prevent data exfiltration, Access Transparency logs, and organization policy constraints. We document every control so your auditor has what they need.

What does GCP cost, and how do you keep it under control?+

GCP pricing is competitive, especially for data and AI workloads. We set up billing budgets with alert thresholds, use committed use discounts for predictable workloads, and configure auto-scaling policies that scale to zero during off-hours. Our FinOps work includes monthly cost reviews where we catch waste and right-size resources before it becomes a real line-item problem.

Cloud-Native Platforms on GCP for Fintech and AI