LLM services need operational guardrails just like any other production system. SLOs make quality, safety and cost targets explicit and give teams a shared language to manage trade-offs.
Define SLIs across four dimensions: latency percentiles, accuracy or groundedness, safety/refusal rate, and unit cost. Choose thresholds that align to user expectations and business value—chat agents tolerate a bit more latency than inline search, but far less hallucination.
Use error budgets to pace change. When drift, incidents or cost spikes eat into the budget, freeze risky releases and focus on stability work: prompt fixes, cache tuning, routing adjustments or provider failover. When budgets are healthy, experiment more aggressively.
Instrument for attribution. Capture provider, model version, prompt template, tools invoked and feature flags so you can localise regressions quickly. Pair automated regression suites with canary rollouts and shadow deployments to detect quality drops before users do.
Link SLOs to runbooks. Escalate when hallucination or refusal rates breach thresholds; flip to conservative prompts, lower-temperature fallbacks or offline modes when upstream providers degrade. Keep leadership dashboards that show error budget burn-down and the operational cost of reliability.
Treat SLOs as evolving contracts. Revisit targets as usage patterns change, new models arrive and costs shift. The goal is not perfection, but predictable behavior that matches the promises you make to customers and regulators.