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

Retour au blog

AI Architecture

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

February 23, 202644 min read

AI reliability AI observability AI SLOs LLM monitoring AI system reliability production AI

Frequently Asked Questions

Partager l'article

Twitter LinkedIn WhatsApp

Satyam

Architecte AI & Cloud. J'aide les équipes à construire des systèmes qui scalent pour des millions.

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

Frequently Asked Questions

Partager l'article

Comments

Leave a comment