KthologyBuilding Scalable AI Solutions: Architecture Best Practices
Data pipelines, serving patterns, observability, and cost control for production AI.
TechnicalBy Alex Kumar10 min read
Launching an AI feature is easy; keeping it fast, reliable, and cost-efficient at scale is the work. Here are the architecture patterns we use.
Data pipelines first
- Event streams for real-time signals; enforce schemas to avoid drift.
- Feature store to align training and serving.
Serving patterns
Synchronous APIs
Low-latency chat/search; autoscale, warm pools, batching.
Async workers
Heavy jobs; queue requests, return job IDs, notify on completion.
Guardrails, observability, cost
- Validation, filters, circuit breakers, fallbacks.
- Track latency p50/p95/p99, error rates, drift, and user feedback loops.
- Model routing, quotas, batching, caching to manage spend.
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