System design for real-time AI: the 10 core components, from Kafka to edge inference
goyalshaliniuk · x · 2026-10-29
An engineer-friendly breakdown of the 10 core components of real-time AI systems, each mapped to its tool ecosystem:
- Stream processing: Kafka, Flink, Spark Streaming for continuous live data
- Real-time inference: TensorFlow Serving, Triton, ONNX Runtime
- Event-driven systems: AWS EventBridge, Pulsar for reactive scaling
- Data queues: RabbitMQ, Kafka, SQS to buffer load
- Monitoring & alerting: Prometheus, Grafana, Datadog
- Auto-scaling: Kubernetes HPA, AWS Auto Scaling
- Low-latency serving: millisecond outputs via TensorRT, FastAPI
- Real-time feature engineering: Tecton, Feast, SageMaker Feature Store
- Model optimization: quantization via TensorRT, OpenVINO, Optimum
- Edge computing: NVIDIA Jetson, Google Coral, AWS IoT Greengrass
A solid architecture overview map for anyone building high-speed, low-latency AI applications.
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