Reliable AI Systems: SLOs, Observability & Resilience

Q: How do you define SLOs for AI systems?

AI SLOs extend traditional SLOs with AI-specific metrics: response quality (accuracy, relevance), latency P95/P99, hallucination rate, cost per query, and model freshness. Set separate SLOs for each and track error budgets.

Q: What should you monitor in production AI systems?

Monitor: inference latency distribution, model accuracy/drift, input data distribution changes, output quality scores, error rates by category, cost per query, GPU utilization, and cache hit rates.

Q: How do you build fault-tolerant AI systems?

Use model fallback chains (try primary → secondary → cached response → graceful degradation), circuit breakers on external AI APIs, request queuing for rate limits, and automated rollback when quality metrics degrade.

Q: What is AI observability?

AI observability goes beyond traditional monitoring to include: input/output logging for debugging, prompt versioning and A/B testing, embedding drift detection, retrieval quality tracking, and end-to-end trace analysis across multi-model pipelines.

Satyam Kumar

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Building Reliable AI Systems: SLOs, Observability, and Failure-Tolerant Architecture

February 23, 202644 min read

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Satyam

AI & Cloud Architect. Helping teams build systems that scale to millions.

Building Reliable AI Systems: SLOs, Observability, and Failure-Tolerant Architecture

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