AI Service Level Agreement (SLA) Generator

Why AI Service Level Agreements Matter

AI systems present unique challenges that traditional SLAs don't address. Model performance can degrade over time as data patterns change (model drift), and outputs are inherently probabilistic rather than deterministic. Standard uptime and response time metrics, while still important, aren't sufficient for AI services.

As a technology attorney who has drafted hundreds of AI agreements, I created this generator to help businesses establish clear expectations, performance metrics, and remedies specifically tailored to AI services. A properly structured AI SLA protects both parties by clearly defining acceptable service levels, measurement methodologies, and remedies when things go wrong.

Key Components of an Effective AI SLA

1. Service Description and Scope

Your SLA should precisely define what the AI service does and doesn't do — specific capabilities, intended use cases, supported platforms, and service limitations. Vague descriptions are the number one source of disputes.

2. AI-Specific Performance Metrics

Model Accuracy measures how often the AI produces correct outputs. Response Time addresses the complex computations that affect responsiveness. Availability remains important, but 99.99% uptime may be unnecessarily expensive for non-critical AI applications.

3. Data Ownership and Privacy

AI services require data to function, raising questions about ownership. Common models include client-owned, provider-owned, shared ownership, and license-based arrangements. Your selection should align with regulatory requirements and how the data will be used.

4. Support and Maintenance

AI systems require regular updates to prevent performance degradation. Define support levels, response times, scheduled maintenance windows, model update frequency, and procedures for addressing issues.

5. Liability and Indemnification

AI systems can produce unexpected outputs with significant consequences. Proper liability provisions balance protection for both parties. Standard options include limitation to fees paid, 12-month fee caps, or fixed amount limitations with carve-outs for intentional misconduct.

Legal Tips for AI Service Level Agreements

1. Establish clear model performance baselines — Require testing against a representative validation dataset to establish initial metrics.
2. Include progressive remedies — Start with notifications for minor issues, escalate to service credits, and reserve termination rights for persistent problems.
3. Address model drift explicitly — Require regular model retraining when performance degrades below thresholds.
4. Consider regulatory requirements — Ensure the agreement addresses industry-specific regulations (HIPAA, SOX, GDPR).
5. Document testing methodologies — Specify how metrics will be measured, including sample sizes, frequencies, and dispute procedures.
6. Clarify permitted data uses — If the provider can use your data to improve models, specify exactly how and with what limitations.
7. Include transparency provisions — Require documentation of significant model changes and advance notification of updates.

If you need assistance tailoring an AI SLA to your specific situation, feel free to schedule a consultation.