SLA Design for AI Systems

Service level agreements for AI systems need to cover dimensions that traditional software SLAs don't address. Uptime and response time matter, but so do output quality, accuracy, consistency, and how the system handles edge cases. Defining meaningful SLAs for AI is challenging because performance can be subjective (was that response "good enough"?), variable across different types of input, and difficult to measure automatically. A well-designed AI SLA specifies availability and latency targets, defines quality metrics with clear measurement methods, sets expectations for how the system handles failures and edge cases, and includes provisions for performance degradation over time. It should also address data handling - where your data goes, how long it's retained, and who can access it. Be realistic about what vendors can guarantee; AI systems inherently involve some uncertainty, and SLAs that demand perfection will either be unenforceable or priced accordingly. Focus on the metrics that genuinely matter for your use case, build in regular review periods, and ensure you have the monitoring in place to verify compliance independently.