A service-level agreement covering AI system availability, latency, accuracy guarantees, escalation, and incident response.
An AI SLA is a service-level agreement that extends traditional SaaS SLA dimensions (availability, latency, support response time) with AI-specific commitments — accuracy or quality guarantees on specific workflows, exception-escalation response times, model-update notification windows, incident response for AI-specific incidents (model drift, hallucination spikes, prompt-injection events).
AI SLAs are evolving as enterprise AI matures. Early AI deployments often inherited their parent product's SaaS SLA without AI-specific commitments. Production-grade enterprise AI increasingly requires explicit AI-side commitments — what's the agreed accuracy on the workflow, what happens if it drops, what's the escalation path for AI-specific incidents.
For high-stakes enterprise AI workflows (regulated, customer-facing, compliance-critical), the AI SLA is where the operational expectations get formalized. Without explicit AI commitments, the customer-vendor expectations can drift in ways that materially affect the deployment.
99.9% availability on the AI inference layer
P95 latency under 2 seconds on a defined workflow class
Accuracy guarantee on specified workflows with quarterly accuracy review
Exception-escalation response time SLA for AI-specific incidents
Model-update notification window with rollback option
Beth's enterprise SLAs cover availability, latency, accuracy targets per workflow class, exception-escalation response time, and model-update notification windows. Specific SLA terms are scoped during enterprise contracting based on the deployment posture and the workflow criticality.
Availability, latency (typically P50/P95/P99), accuracy targets where applicable, exception-escalation response times, model-update notification windows, support response times, and incident response for AI-specific incidents.
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