In traditional software, a "99.9% uptime" SLO is unambiguous. In AI, a system can be "up" (returning 200 OK) while being completely broken (serving hallucinations or stale predictions). This requires an SRE mindset for AI and a shift from binary uptime to multi-layered observability.
Defining Meaningful AI SLIs
To build robust SLOs, you must first define your Service Level Indicators (SLIs). For production LLMs, this usually includes:
- TTFT (Time to First Token): < 200ms for interactive chat.
- Inference Latency: < 2s for real-time fraud detection.
- Quality Proxy: > 95% of responses must pass automated eval filters.
Managing the Error Budget
Your error budget isn't just for downtime; it's for experiments, canary releases, and A/B testing. If a new model version degrades your quality SLI, it should consume the error budget, and you should consider graceful degradation strategies to maintain user experience.
Final Takeaway
SLOs for AI systems must bridge the gap between infrastructure availability and model correctness. By defining clear SLIs for latency and quality, you provide your engineering and product teams with a common language for reliability and risk.
Struggling to define or meet SLOs for your AI systems? We help teams establish meaningful SLIs, monitor error budgets, and build high-availability infrastructure. Book a free infrastructure audit and we’ll help you define your path to reliability.
