What This Design Does:

SentinelFlow is a resilient AI inference platform designed as a cloud-native Kubernetes architecture. It combines an API layer, inference routing, model serving, asynchronous workers, Redis, PostgreSQL, Kafka, and an OpenTelemetry-based observability stack. The design demonstrates scalable inference workloads, service-to-service communication, autoscaling, disruption protection, and network-level security controls.

Design Snapshot

SentinelFlow — Resilient AI Inference Platform design snapshot

Caveats and Consideration:

This design is intended as a reference architecture and visual demonstration of Kubernetes-based AI infrastructure. Container images, credentials, storage classes, ingress configuration, and environment-specific networking should be reviewed and replaced before production deployment. Resource limits, persistent storage, secrets management, and observability retention should be tuned according to workload requirements.

Compatibility:

Design Details

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