How this lab works
AI Ops, in plain terms
This is the unglamorous 70% nobody demos: running the thing reliably at a cost you can defend. You turn the dial from pilot to production and watch cost, latency, reliability, and drift react — then fire an incident and watch it recover.
Set the scale
Drag the volume dial from a quiet pilot up to full production traffic.
What works in a demo often breaks at scale — you need to see the system under real load.
Read the operating envelope
Load and caching map onto colored zones for SLO and cost — safe, margin, or breaks.
It shows exactly where your operating point sits and how close it is to the edge.
Tune and compare configs
Switch model tier, caching, and reranker, and compare mixes side by side at the current scale.
The cheapest compute is rarely the cheapest overall — human escalation usually dominates the bill.
Watch the live numbers
Monthly cost, reliability, p95 latency, and drift risk update as you change anything.
These are the numbers an SRE and a CFO both ask about — and where Realize's run cost comes from.
Fire an incident
Inject a failure and watch alerts trip, the error budget burn, and the system recover (MTTR).
Resilience is proven by how fast you recover, not by hoping nothing goes wrong.