WELCOME TO AIOpsSRE.COM
You’re Running AI in Production.
Now You Need Reliability Engineering to Match.
This site covers the operational side of AI — how to monitor it, debug it, deploy it safely, and keep it running at scale. Built for SREs, platform engineers, and DevOps practitioners who are dealing with LLMs in the real world.
5 Things to Understand First
AI systems need Observability just like any other production service
LLM calls, vector DB queries, and agent pipelines all have latency, error rates, and token costs that can blow up silently. If you’re not tracking these, you’re flying blind. Start with token usage metrics, latency percentiles, and Error Budgets for your AI subsystems.
Token costs are your new compute budget — and they’re non-deterministic
Traditional infra costs are predictable. LLM token costs depend on prompt structure, user behavior, and model responses. You need budget guardrails, per-request cost tracking, and alerts before you’re surprised by a $40K bill.
SRE principles translate directly — with some new wrinkles
Error budgets, SLOs, on-call Runbooks, postmortems — all of these apply to AI systems. The wrinkles: models can degrade without throwing errors (quality drift), and model updates can break things in ways that are hard to test.
Incident management for AI is different — the blast radius is often invisible
A bad model response doesn’t 500. Users just get wrong answers. You need output quality monitoring, hallucination detection strategies, and runbooks for “the AI is confidently wrong.” This is where SRE meets AI safety in practical terms.
Browse by Topic
AIOps
Applying AI to IT operations — alert correlation, anomaly detection, and automated remediation.
SRE
Site reliability engineering — SLOs, error budgets, toil reduction, and building reliable systems.
Observability
Metrics, traces, logs, and making your systems understandable when things go wrong.
Incident Management
On-call, runbooks, postmortems, and building a culture that learns from failure.