Case Study

Building a High-Availability AI Framework for a Gambling Product on GCP: From Manual Management to Fully Automated Scalability

Overview

We started by analyzing their existing setups to identify inefficiencies. We found that not all cloud environments were right for their specific workloads. So, we decided to consolidate their operations into two main setups: Azure for their cloud needs and an on-premise solution for tasks that required more direct control.

Industry: iGaming / Gambling Technology
Cloud Platform: Google Cloud Platform (GCP)
Engagement Start: April 2023
Focus Areas: High Availability · Autoscaling · Observability · Compliance

A leading gaming technology platform providing high-performance online betting systems faced growing operational and scalability challenges as user volume surged.
They needed a resilient, secure, and AI-ready infrastructure that could handle real-time data, heavy workloads, and strict compliance requirements — all while maintaining uptime and speed during traffic spikes.

DasMeta was engaged to assess, modernize, and implement a next-generation AI framework on Google Cloud, ensuring the platform could support continuous innovation and machine learning workloads safely and efficiently.

Challenge

Choosing the Right Tools:

We implemented ITIL processes, which are basically best practices for IT service management, to help them manage their infra better. This gave us a structured approach to make their operations smooth and predictable.

Monitoring Setup:

We also introduced them to Grafana, a tool for monitoring their systems in real-time. This way, they could immediately see if something was off and fix it before it became a bigger problem.

Challange

By simplifying their cloud environments, we were able to shift human resources from routine maintenance to more critical development tasks, further enhancing their product capabilities and innovation.

The company’s existing infrastructure was spread across multiple Google Cloud projects — each configured manually, inconsistently, and without centralized control.

They struggled with:

No unified Disaster Recovery (DR) or Infrastructure as Code (IaC) process.
Manual scaling causing downtime during traffic peaks.
Limited observability, leading to slow incident detection and long MTTR.
Security risks due to partial Cloudflare and WAF configurations.
Outdated data architecture, limiting analytics and AI adoption.

In a regulated and high-load industry, these issues directly impacted uptime, cost predictability, and customer trust.

What We Did

1. Phase - Infrastructure as Code (IaC) Modernization

We transitioned the entire environment to Terraform Cloud (HCP) for consistent, automated provisioning.

Implemented version-controlled IaC across all environments (dev, staging, prod).
Built DR automation pipelines for quick failover and full recovery.
Standardized deployments with modular Terraform templates.

Phase 2 - High Availability & Autoscaling

We re-architected the platform for resilience and elasticity:

Configured autoscaling policies for compute and container workloads.
Set up multi-zone redundancy to ensure continuous uptime.
Added spike-protection mechanisms for unpredictable traffic surges.
Optimized load balancing and caching for lower latency and faster response.

Phase 3 - Security Reinforcement

We enhanced edge and infrastructure security through:

Cloudflare WAF and DDoS protection integration.
Unified IAM access control and principle-of-least-privilege policies.
Real-time security dashboards and automated alerting.
Continuous vulnerability scanning integrated into CI/CD.

Phase 4 - Observability & Monitoring

We deployed a robust observability stack for proactive issue detection:

Grafana + Prometheus dashboards for live metrics and service health.
Centralized logging and alerting for faster troubleshooting.
Reduced MTTR through detailed performance insights and alert automation.

Phase 5 - Data Layer Optimization

To support faster data processing and better reliability:

Introduced Redis for in-memory caching.
Integrated Kafka for event streaming and asynchronous workloads.
Implemented ClickHouse for analytical data storage and reporting.

Phase 6 - Compliance & Governance

Given the industry’s strict standards, we:

Established auditable change logs and documentation for all infrastructure.
Applied data protection policies and encryption in transit and at rest.
Ensured environment segregation for compliance and operational safety.

Results

After implementation, the platform achieved a measurable leap in reliability, performance, and security.

Key Outcomes:

Disaster Recovery with IaC: Automated environment restoration and reduced recovery time.
Stronger Security: Cloudflare WAF and DDoS protection ensured continuous availability.
Improved Observability: 60% faster incident detection and resolution (lower MTTR).
Elastic Scalability: Autoscaling and spike protection enabled 99.98% uptime.
Enhanced Data Performance: Redis, Kafka, and ClickHouse improved speed and throughput.
Compliance Ready: Infrastructure aligned with governance and audit requirements.

Conclusion

This project shows how a well-designed cloud architecture can transform operational stability and speed.

The new environment can recover itself through Infrastructure as Code, scale automatically under load, and detect issues before they impact users. Security layers with Cloudflare WAF and DDoS protection keep the edge clean, while improved monitoring and alerting mean problems are fixed in minutes, not hours.

Performance is now predictable. Traffic spikes no longer cause downtime. Data flows seamlessly through Redis, Kafka, and ClickHouse — powering real-time operations with speed and stability. Today, the platform runs on a self-healing, compliant, and future-proof foundation that gives both developers and operations teams what they need most: confidence.

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