DevOps & Infrastructure
SRE for AI Applications: Service Level Objectives (SLOs) & Error Budget Management
Establish site reliability engineering standards for LLM applications: 99.9% availability, latency SLOs, and automated rollback triggers.
Defining Realistic AI SLOs
Unlike traditional microservices with 50ms SLOs, generative AI SLOs track TTFT (< 800ms for p95) and token generation success rate.
Related Technical Guides
Deepen your understanding with these closely related production architectures and tutorials:
Deploying FastAPI in Production: Multi-Stage Dockerfile, Gunicorn & Uvicorn Workers
Production-ready Docker deployment with multi-stage builds, non-root security, Gunicorn process management, and health checks.
FastAPI & OpenTelemetry: Distributed Tracing, Prometheus Metrics & Grafana
Diagnose millisecond-level bottlenecks across SQL queries, Redis calls, and LLM streaming responses with OpenTelemetry tracing.
Multi-Agent Debugging & Distributed Tracing with LangSmith & Langfuse
Inspect every sub-agent decision, tool execution latency, and token consumption with waterfall call trees and observability platforms.