engineering · 1 min read
SLOs and SLIs for AI Features
Latency and availability SLOs translate to AI; quality SLOs are new and tricky. Here is how Bhogar AI defines and measures both.
BABhogar AI TeamProduct & Engineering
Traditional SLOs (latency, availability) translate to AI directly. Quality SLOs are new and require explicit metric definitions plus continuous measurement.
Why it matters
Defining a quality SLO needs an evaluator (LLM-as-judge or rubric-based), a sampling strategy, and a clear definition of what "below SLO" means in terms of error budget.
How Bhogar AI approaches it
Bhogar AI ships an SLO framework: define latency, availability and quality SLOs per feature; SLIs are calculated from production traffic and eval samples; error-budget burn rate triggers alerts.
- Latency, availability and quality SLOs
- Continuous SLI calculation from production
- Error-budget burn-rate alerts
- Per-feature SLO dashboards
- Compatible with PagerDuty, Opsgenie and Slack
What you get
AI features get the same SRE rigor as the rest of the stack, with quality treated as a first-class reliability dimension.