AI Infrastructure Architecture
Cloud, hybrid and on-premise AI infrastructure design for enterprise-scale model deployment.
Enterprise AI infrastructure spans GPU provisioning, model serving, feature stores, MLOps pipelines, data platforms and monitoring. This blueprint covers the full infrastructure stack for organisations deploying AI at scale — from greenfield builds to modernising existing environments.
When to Use This Blueprint
- Designing infrastructure for first enterprise AI platform
- Scaling from proof-of-concept to production AI systems
- Sovereign or on-premise AI deployment requirements
- Multi-cloud AI platform design
- Migrating from scattered AI tools to a unified AI platform
Architecture Components
Compute Layer
GPU provisioning (A100, H100, inference-optimised). Cloud (AWS SageMaker, GCP Vertex, Azure ML) vs on-premise. Batch vs real-time inference design.
Model Serving Infrastructure
vLLM, TGI, Triton Inference Server. Batching strategies, quantisation, KV-cache optimisation. Multi-model serving.
Feature Store
Online vs offline feature stores. Feast, Tecton, Hopsworks. Feature engineering, versioning, sharing across teams.
Model Registry & Versioning
MLflow, Weights & Biases, SageMaker Model Registry. Version control, staging environments, rollback capability.
Data Platform Integration
Data lake, warehouse and streaming integration. Lakehouse patterns for AI training pipelines. Data quality monitoring.
MLOps Pipeline
Training pipelines, automated retraining triggers, CI/CD for ML, experiment tracking, hyperparameter optimisation.
Sovereign AI Patterns
Air-gapped deployments, data residency controls, on-premise LLM hosting (quantised open-weight models), network isolation.
Decision Framework
Decision: Deployment Model
Decision: Model Serving
Implementation Phases
Week 1–3
Infrastructure Design
Requirements assessment, cloud/on-prem decision, architecture design, security review, cost modelling.
Week 4–7
Platform Build
Compute provisioning, model serving setup, feature store, MLOps pipeline, monitoring instrumentation.
Week 8–10
Integration & Testing
Data platform integration, CI/CD, load testing, failover testing, security validation.
Week 11–12
Handover & Documentation
Runbook creation, team training, SLA definition, operational handover.
Governance Controls
Key Metrics to Track
Need Help Implementing This Blueprint?
Our AI architects can design and implement this architecture for your organisation — governance-first, production-grade and aligned to your specific requirements.