AI SLAs and SLOs
As AI features move into production, teams need a way to define what “reliable enough” means. That is where SLOs (service-level objectives) and SLAs (service-level agreements) become useful. They bring operational discipline to systems whose behavior is partly probabilistic.
What to Measure
For AI applications, useful objectives often include:
- Latency: how quickly the system returns a response
- Availability: how often the feature is usable
- Output validity: whether the response matches the required schema or format
- Task success: whether the answer actually helps complete the user goal
Why AI Needs More Than Uptime
A system can be technically available while still failing users. If a chatbot answers slowly, returns malformed JSON, or produces low-quality results, the experience is degraded even though the server never went down.
The Practical Benefit
SLOs help teams decide when a release is good enough, when a regression is serious, and what tradeoffs are acceptable between cost, quality, and speed. They turn AI operations into something measurable instead of purely subjective.