Decision Guide · 1 minute read
AI SLAs: What to Expect and What to Ask For
AI SLAs should cover system uptime and latency like any software, plus AI-specific commitments: accuracy or quality thresholds measured by evaluation, a defined fallback (often human) when confidence is low, monitoring and incident response, and update or re-training cadence. Because AI is probabilistic, a serious SLA commits to measured quality bands and safe fallbacks, not a promise of perfection.
Traditional SLAs assume deterministic software. AI is probabilistic, so its service levels need to be thought about differently. Here's what to expect—and what to demand.
What an AI SLA covers
| Layer | Commitment |
|---|---|
| Uptime | System availability, like any software |
| Latency | Response time under load |
| Quality | Accuracy thresholds, measured by evaluation |
| Fallback | Behavior when confidence is low |
| Support | Monitoring, incident response |
| Updates | Re-training / re-grounding cadence |
Why AI quality is a band, not a guarantee
Because AI is probabilistic, no honest vendor guarantees 100% accuracy. A serious SLA commits to measured quality bands—accuracy evaluated against a specification—plus safe handling of the rest. Anyone promising perfection either misunderstands AI or is overselling; see deterministic AI outcomes.
The fallback matters most
The critical SLA term is what happens when the AI isn't confident. A well-designed system routes low-confidence cases to a fallback—often a human—rather than guessing. The SLA should define this, so uncertainty is handled safely instead of producing confident wrong answers.
Monitoring and incident response
Because AI can drift, the SLA should commit to monitoring and a defined incident response when quality degrades—not just uptime. This is part of the ongoing total cost of ownership.
What to ask a vendor
Ask how they measure quality, what the fallback is, how they monitor drift, and how they respond to incidents. Vague answers signal a demo-grade system, not production—see how to evaluate AI vendors.
Why FISTA
FISTA Solutions commits to measured quality, safe fallbacks, monitoring, and incident response—backed by a verified 99.9% uptime record across 150+ projects. Explore AI enablement.
Negotiating an AI SLA? Talk to FISTA.
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Questions raised by this field note.
Straightforward guidance for evaluating scope, fit, and the next step.
01What should an AI SLA include?
System uptime and latency, plus AI-specific terms: accuracy or quality thresholds measured by evaluation, a defined fallback for low-confidence cases, monitoring and incident response, and an update or re-training cadence.
02Can an AI vendor guarantee accuracy?
Not perfectly—AI is probabilistic. A serious vendor commits to measured quality bands (evaluated against a specification) and safe fallbacks for uncertain cases, not a promise of 100% accuracy. Beware anyone who guarantees perfection.
03What happens when the AI isn't confident?
A well-designed system routes low-confidence cases to a fallback—often a human— rather than guessing. The SLA should define this behavior, so uncertainty is handled safely instead of producing confident wrong answers.
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