Every successful cloud implementation starts with architecture design that accounts for performance requirements, compliance constraints, cost targets, and future AI ambitions. We evaluate workloads individually: which belong in public cloud, which need private infrastructure, which should be containerized, and which should be rearchitected as serverless or cloud-native applications.

Our architecture deliverables include network topology diagrams, security zone definitions, identity and access management models, disaster recovery designs, cost projections, and migration sequencing plans. For organizations planning AI adoption, we design cloud environments with the compute, storage, and networking foundations that ML workloads will require—eliminating the expensive re-architecture that happens when AI becomes a priority after initial cloud deployment.

We follow AWS Well-Architected, Azure Well-Architected, and GCP Architecture Framework best practices, adapted to your specific requirements. Architecture reviews are conducted before migration, after deployment, and annually to ensure your cloud environment evolves with your business and the rapidly changing cloud services landscape.

AI is the most demanding workload in modern cloud computing. Training large language models, running inference at scale, and processing massive datasets require GPU-accelerated compute, high-throughput storage, and networking optimized for distributed computing. We deploy AI cloud platforms that deliver enterprise-grade ML infrastructure with proper governance and cost controls.

Cloud-based AI services we architect include managed ML platforms (AWS SageMaker, Azure ML, Vertex AI), custom GPU clusters for model training and fine-tuning, inference endpoints optimized for latency and throughput, vector databases for RAG applications, data lakehouse architectures for ML feature engineering, and MLOps pipelines for automated model lifecycle management. For organizations with data sovereignty requirements, we deploy equivalent capabilities on private infrastructure or air-gapped environments.

Our AI services team helps organizations not just provision infrastructure but also identify high-value AI use cases, develop models, and deploy them to production. The cloud platform is the foundation; the business value comes from what you build on top of it.

IaaS replaces physical servers with virtual machines, block storage, and virtual networks that scale on demand. We architect IaaS deployments across AWS EC2, Azure Virtual Machines, and GCP Compute Engine, implementing auto-scaling groups, load balancers, and high-availability configurations that maintain uptime during demand spikes and hardware failures.

PaaS services eliminate server management entirely. We deploy applications on managed platforms: Azure App Service for web applications, AWS Lambda and Azure Functions for event-driven serverless computing, managed Kubernetes (EKS, AKS, GKE) for containerized microservices, and managed databases (RDS, Azure SQL, Cloud SQL) that handle patching, backup, and scaling automatically.

For organizations modernizing legacy applications, we guide the transition from IaaS to PaaS: identifying components that benefit from managed services, re-architecting monolithic applications into microservices, and implementing CI/CD pipelines that automate deployment. This progression reduces operational overhead and improves reliability while preparing applications for integration with AI services.

The shared responsibility model means cloud providers secure the infrastructure; you secure everything you build on top of it. We implement comprehensive cloud security: IAM with least-privilege policies and MFA enforcement, VPC architectures with proper network segmentation, encryption at rest and in transit using KMS-managed keys, CSPM tools that continuously scan for misconfigurations, and SIEM integration that correlates cloud events with on-premises telemetry.

Governance frameworks prevent the configuration drift, cost overruns, and shadow IT that plague unmanaged cloud environments. We implement guardrails through SCPs (AWS), Azure Policy, and GCP Organization Policies that enforce encryption requirements, restrict regions, limit instance sizes, and require tagging—ensuring every cloud resource meets organizational standards from the moment it is created. For regulated industries, governance policies map directly to compliance requirements, maintaining continuous audit readiness.

Cloud cost optimization is an ongoing discipline, not a one-time project. Our FinOps practice applies continuous analysis and action: identifying oversized instances and recommending right-sizing, purchasing reserved instances and savings plans for predictable workloads, leveraging spot instances for fault-tolerant batch processing, and eliminating idle resources (unattached EBS volumes, stopped instances, unused load balancers) that generate charges without delivering value.

Cost governance prevents future waste. Tagging policies track spend by project, department, and environment. Budget alerts notify stakeholders before spending thresholds are exceeded. Automated scheduling powers down non-production environments outside business hours, saving 60-70% on dev/test costs. Our clients typically reduce cloud spend by 30-40% after initial optimization and maintain those savings through ongoing FinOps practice.

Cloud-based disaster recovery eliminates the need for expensive secondary data centers. We replicate your critical systems to cloud infrastructure that activates automatically during a disaster: site failure, ransomware attack, natural disaster, or hardware catastrophe. Recovery time objectives as low as 15 minutes are achievable for critical workloads using pilot-light or warm-standby architectures.

Our DRaaS implementations include continuous replication, automated failover testing, documented recovery procedures, and compliance-ready evidence that your disaster recovery capability meets regulatory requirements. For AI workloads, we ensure training data, model checkpoints, and inference infrastructure are protected by backup and recovery systems that match their business criticality.