AI Infrastructure Architecture

The difference between an AI demo and an AI product is infrastructure. Demos run on a single API call. Products run on distributed systems with failover, caching, monitoring, load balancing, and cost controls. Most teams skip the infrastructure layer and pay for it in production outages and unpredictable bills.

We design AI infrastructure that handles real traffic, real failure modes, and real cost constraints.

Cloud, Hybrid, or On-Prem

There is no universal answer to where your AI should run. The decision depends on data sensitivity, query volume, latency requirements, and budget.

Cloud API — Fastest to deploy, lowest upfront cost, but per-token pricing creates unpredictable bills at scale. Best for early-stage products validating product-market fit.

Managed Inference — Services like AWS Bedrock or Azure AI provide API-like simplicity with better cost controls and data residency. Good for mid-stage companies with compliance requirements.

Self-Hosted — Running models on your own GPU infrastructure (vLLM, TensorRT-LLM) eliminates per-token costs. Best for high-volume applications processing millions of tokens daily.

Hybrid — Most production systems end up here. Frontier models via API for complex reasoning, self-hosted open-source models for high-volume simple tasks, and a routing layer that directs traffic intelligently.

What We Architect

Inference Pipeline — Model serving, load balancing, failover, and auto-scaling. We design for P99 latency targets, not just averages.

Data Pipeline — Embedding generation, vector indexing, retrieval, and caching for RAG systems. We optimise for both accuracy and throughput.

Monitoring and Observability — Logging, cost tracking, quality metrics, and alerting. You cannot optimise what you cannot measure.

Security Layer — Network isolation, encryption at rest and in transit, access controls, and audit trails. Compliance-ready from day one.