The AI Infrastructure Challenge
Building AI is one thing. Running it in production is another. Most teams underestimate the complexity of AI infrastructure — compute costs spiral, models fail to scale, and monitoring becomes a nightmare.
We've built AI infrastructure dozens of times across GCP, AWS, and Azure. We know what works.
Why Multi-Cloud Matters
Most consultancies are locked into one cloud provider. We're not. We choose the right cloud for your needs:
Google Cloud Platform (GCP)
- Best for: TensorFlow models, BigQuery integration, Vertex AI workloads
- Strengths: ML-native services, data analytics, competitive TPU pricing
- When to choose: Existing Google Workspace, data-heavy workloads, TensorFlow ecosystem
Amazon Web Services (AWS)
- Best for: Broad service coverage, SageMaker workflows, hybrid cloud
- Strengths: Mature ecosystem, most services, extensive regional coverage
- When to choose: Enterprise with existing AWS footprint, need for specific AWS services
Microsoft Azure
- Best for: Microsoft stack integration, Azure OpenAI Service, enterprise compliance
- Strengths: Active Directory integration, OpenAI partnership, hybrid cloud
- When to choose: Microsoft-heavy organization, OpenAI models, government/compliance needs
Our Infrastructure Services
MLOps Pipeline Design & Implementation
End-to-end MLOps infrastructure from training to deployment:
- Automated model training pipelines
- Version control for models and data
- A/B testing and gradual rollouts
- Model monitoring and drift detection
- Automated retraining triggers
Scalable Model Serving
Deploy AI models that scale from prototype to millions of requests:
- Auto-scaling inference endpoints
- Load balancing and traffic management
- Caching and optimization strategies
- Multi-region deployments
- Edge deployment for low-latency use cases
Data Pipeline Architecture
Build robust data pipelines that feed your AI systems:
- Real-time and batch data ingestion
- Data transformation and feature engineering
- Data quality monitoring
- Storage optimization (data lakes, warehouses)
- Compliance and governance (GDPR, SOC 2)
Cost Optimization
AI infrastructure is expensive. We make it cost-efficient:
- Right-sizing compute resources
- Spot/preemptible instances for training
- Storage tier optimization
- Reserved capacity planning
- Cost monitoring and alerting
Security & Compliance
Production-ready security from day one:
- Network isolation and VPC design
- Identity and access management
- Encryption at rest and in transit
- Audit logging and compliance reporting
- Secrets management
Real-World Scenarios
Scenario 1: Scaling an LLM Service
Challenge: Startup with a ChatGPT-like service hitting scaling limits at 10K users.
Solution: Migrated to GCP with auto-scaling GPU infrastructure, added caching layer, implemented request queuing. Now handles 500K+ users.
Cost impact: 40% reduction in per-request cost through optimization.
Scenario 2: Multi-Region AI Deployment
Challenge: Enterprise needing low-latency AI inference across US, EU, and APAC.
Solution: Multi-region AWS deployment with edge caching, geo-based routing, and automated failover.
Result: <50ms latency in all regions, 99.99% uptime.
Scenario 3: Hybrid Cloud ML Platform
Challenge: Financial services firm with on-prem data, need for cloud AI capabilities.
Solution: Azure hybrid cloud with on-prem data sync, cloud-based training and inference, strict compliance controls.
Result: AI capabilities without data sovereignty concerns.
Technology Stack We Deploy
- Orchestration: Kubernetes (GKE, EKS, AKS), Kubeflow
- ML Platforms: Vertex AI, SageMaker, Azure ML
- Model Serving: TorchServe, TensorFlow Serving, Triton Inference Server
- Data: Apache Airflow, Prefect, dbt
- Monitoring: Prometheus, Grafana, CloudWatch, Datadog
- IaC: Terraform, Pulumi, CloudFormation
Global Delivery, Local Expertise
With offices in UK and India, we provide:
- 24/7 infrastructure support — UK team during your day, India team while you sleep
- Cost-efficient ops — India team handles routine maintenance at lower cost
- Continuous improvement — Infrastructure evolves around the clock
- Global deployment experience — We've built in every major cloud region
Migration Services
Already have AI infrastructure that needs work?
- Cloud-to-cloud migration (AWS → GCP, etc.)
- On-prem to cloud
- Monolith to microservices
- Infrastructure audit and optimization
Pricing Model
- Fixed-price infrastructure buildout — Clear scope, predictable cost
- Managed services retainer — Ongoing ops and support
- Consulting & advisory — Architecture review, optimization audits
Ready to Build Production AI Infrastructure?
Book a discovery call. We'll assess your current setup and recommend the right cloud platform and architecture for your needs.