Technical deep dive

CloudCast AI control plane

A reference architecture for teams who need API-grade AI with governance and observability baked in—not bolted on after an incident.

Logical stack (AWS / GCP / Azure compatible)

Edge & API Gateway · AuthN/Z · Rate limits · WAFControl plane services (policy, routing, feature flags, prompt registry)Ingestion · validation · orchestration · confidence · structured output APIModel endpoints (VPC / private)Retrieval & vector stores (encrypted)Observability plane — traces, metrics, logs, audit streamsOpenTelemetry · Prometheus-compatible · immutable audit sink · alerting

API-first design

Every workflow exposes stable JSON contracts and webhooks. Clients integrate with BI tools, ERP, and custom apps without scraping chat transcripts.

Multi-cloud posture

Deploy to AWS, GCP, or Azure with the same logical layers. Use regional residency patterns for data sovereignty and latency.

Monitoring layer

Golden signals for inference: latency, error rate, saturation, plus business SLOs tied to workflow outcomes—not just GPU charts.

Observability metrics

Trace coverage, drift indicators, cache hit rate, cost per successful task, and audit export volume—so finance and ops share one vocabulary.

For CTOs

This page is a conversation starter. Bring your constraints—data residency, on-prem LLMs, existing Kubernetes—and we map them to a phased rollout with clear blast-radius controls.

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