When enterprises evaluate a traditional SaaS vendor, the core questions are well-understood: Does the software do what it claims? Are the SLAs acceptable? Is the pricing sustainable? Are the APIs open enough to avoid lock-in? These are important, but they all assume a fundamentally deterministic system — one where the same input reliably produces the same output, updates are discrete and versioned, and the product's behavior is bounded by its feature set. AI-as-a-Service (AIaaS) breaks every one of those assumptions. An AIaaS platform delivers a probabilistic inference engine, not a feature bundle. Its outputs depend on the quality, recency, and coverage of training data; on the architecture of its underlying models; and on a continuous lifecycle of monitoring, retraining, and drift detection. Treating an AIaaS evaluation like a SaaS evaluation — scoring uptime, UI/UX, and price-per-seat — means ignoring the most consequential risks and the deepest sources of future value.
The operational differences cascade through every stage of vendor management. Pricing structures shift from predictable per-seat subscriptions to consumption-based models (tokens processed, API calls, inference compute hours), which requires explicit usage modeling and careful budget planning that fixed subscriptions do not. Security expands from perimeter protection to include AI-specific attack surfaces: prompt injection, training data poisoning, adversarial inputs, and model jailbreaking — attack vectors that standard enterprise security frameworks were not designed to address. Data governance becomes dramatically more complex because you must understand not just where your data is stored, but whether it is used to retrain shared models, who owns the IP of AI-generated outputs, and what happens to your data when you terminate the contract. SLAs must evolve beyond uptime to include model performance guarantees — accuracy thresholds, drift remediation timelines, bias monitoring cadence, and retraining frequency. Vendor lock-in is also deeper: in SaaS, you're locked in by data portability; in AIaaS, you're locked in by proprietary model architecture, the investment in fine-tuning on your own data, and the impossibility of reproducing a black-box model elsewhere. AI vendor contracts warrant far more assertive negotiation than standard SaaS agreements — particularly around warranty terms, model performance commitments, and documentation compliance obligations that are frequently absent from boilerplate AI vendor contracts.
Score each criterion 0–3 using the legend below. Multiply by the weight to get a weighted score. Categories marked AI-ONLY have no equivalent in a standard SaaS RFP and should receive careful attention from technical reviewers.
| Criterion & Probe Questions | Weight | Score (0–3) | Weighted | Evaluator Notes |
|---|---|---|---|---|
| Financial Stability Can they provide audited financials, funding details, or investor-grade evidence of runway? Is the company at risk of acquisition or shutdown mid-contract? |
×3 | ___ | ||
| Security Certifications SOC 2 Type II minimum. Do they have ISO 27001? FedRAMP if applicable? What is the incident response SLA and notification window? |
×3 | ___ | ||
| Integration Architecture REST / GraphQL APIs with documented schemas? SDK availability? Webhook support? Compatibility with your existing data stack? |
×2 | ___ | ||
| SLA — Availability & Error Resolution Does the SLA define specific uptime % and financial remedies? Is "commercially reasonable efforts" language avoided? What is the escalation path? |
×2 | ___ | ||
| Reference Customers Are there verifiable customers in your industry vertical? Can you speak directly with a reference? Do case studies cite measurable outcomes? |
×2 | ___ | ||
| Regulatory Compliance (GDPR / CCPA / sector-specific) Can the vendor demonstrate compliance? Do they support data subject rights (access, correction, deletion)? Is data stored in required geography? |
×3 | ___ |
| Criterion & Probe Questions | Weight | Score (0–3) | Weighted | Evaluator Notes |
|---|---|---|---|---|
| Training Data Provenance AI-ONLY What data sources were used to train the base model? How is data quality validated? Is there a process for identifying and mitigating bias in training sets? |
×3 | ___ | ||
| Model Accuracy & Benchmarks AI-ONLY Are there published benchmarks on held-out test sets? Can the vendor run accuracy evaluations on your own data during POC? What metrics (F1, precision, recall, RMSE) are reported? |
×3 | ___ | ||
| Model Customization / Fine-Tuning AI-ONLY Can models be fine-tuned on your proprietary data? Is fine-tuning done in an isolated environment? Who owns the fine-tuned model weights? |
×2 | ___ | ||
| Architecture Transparency AI-ONLY Is the system calling a foundation model API, running RAG, orchestrating multiple models, or a decision-tree with an LLM wrapper? Can the vendor document the full inference pipeline? |
×2 | ___ | ||
| Model Versioning & Backward Compatibility AI-ONLY Does the vendor version models? Can you pin to a specific model version? How much notice is given before model updates that change output behavior? |
×2 | ___ | ||
| Failure Mode Handling AI-ONLY How does the system handle ambiguous inputs, contradictory instructions, or out-of-distribution data? Can the vendor demo graceful degradation? What are the fallback mechanisms? |
×3 | ___ |
| Criterion & Probe Questions | Weight | Score (0–3) | Weighted | Evaluator Notes |
|---|---|---|---|---|
| Model Drift Detection AI-ONLY Does the platform monitor for data drift and concept drift in production? What is the alerting mechanism? Does drift detection include statistical process control or only threshold-based alerts? |
×3 | ___ | ||
| Retraining Cadence & SLA AI-ONLY How frequently are models retrained? Is retraining triggered automatically when drift thresholds are breached? What is the SLA for drift remediation? |
×3 | ___ | ||
| Performance Monitoring Dashboards AI-ONLY Does the vendor provide real-time visibility into model accuracy, prediction confidence, and anomaly rates? Is this available to the customer or only internally? |
×2 | ___ | ||
| Model Performance SLA AI-ONLY Are there contractual accuracy thresholds (e.g., "≥90% precision on your use case")? What are the remedies if accuracy degrades below threshold — model credits, retraining, SLA credits? |
×3 | ___ | ||
| Shadow Model Testing AI-ONLY Before promoting a retrained model to production, does the vendor run it in shadow mode against live traffic? Is there a champion/challenger evaluation framework? |
×1 | ___ |
| Criterion & Probe Questions | Weight | Score (0–3) | Weighted | Evaluator Notes |
|---|---|---|---|---|
| Explainability (XAI) AI-ONLY Are model decisions explainable using feature attribution (e.g., SHAP values, LIME)? Can explanations be surfaced to end users or regulators? Is this available for all model types deployed? |
×3 | ___ | ||
| Bias Detection & Fairness Testing AI-ONLY Does the vendor regularly test models for demographic bias? Across which fairness metrics (disparate impact, equalized odds)? How are issues remediated and disclosed? |
×3 | ___ | ||
| Audit Trail & Immutable Logging AI-ONLY Are all model predictions, inputs, and retraining events logged immutably? Can you retrieve a full decision audit trail for regulatory review? How long are logs retained? |
×3 | ___ | ||
| EU AI Act / Algorithmic Accountability Readiness AI-ONLY Has the vendor classified their system under EU AI Act risk tiers? Do they have a conformity assessment process? Are they compliant with any sector-specific algorithmic accountability regulations? |
×2 | ___ | ||
| Human-in-the-Loop Controls AI-ONLY Can the system route low-confidence predictions to human review automatically? Are override and correction mechanisms built in? How do human corrections flow back into model improvement? |
×2 | ___ |
| Criterion & Probe Questions | Weight | Score (0–3) | Weighted | Evaluator Notes |
|---|---|---|---|---|
| Customer Data Used for Retraining AI-ONLY Is your data used to retrain shared models? Can you opt out? If your data improves the model, do other customers benefit from it? This must be contractually explicit. |
×3 | ___ | ||
| IP Ownership of AI Outputs AI-ONLY Who owns the intellectual property of outputs generated by the model using your data? Is this addressed in the MSA? What is the vendor's position on third-party IP claims against generated content? |
×3 | ___ | ||
| Data Deletion at Termination AI-ONLY Upon contract termination, what happens to your data used in inference and training? Is deletion certified? Are model weights derived from your data destroyed? |
×3 | ___ | ||
| Data Lineage Tracking AI-ONLY Can the vendor trace which training data influenced a specific model version? Is metadata lineage maintained from raw data ingestion through feature engineering to model deployment? |
×2 | ___ | ||
| Data Isolation (Multi-tenant vs. Dedicated) AI-ONLY Is your inference data isolated from other tenants at the model level, not just the storage level? For sensitive use cases, is single-tenant or private model deployment available? |
×2 | ___ |
| Criterion & Probe Questions | Weight | Score (0–3) | Weighted | Evaluator Notes |
|---|---|---|---|---|
| AI Red Team Testing AI-ONLY Has the vendor conducted AI-specific red teaming — including prompt injection, jailbreaking, adversarial inputs, and data extraction via model outputs? Are results available under NDA? |
×3 | ___ | ||
| Training Data Poisoning Controls AI-ONLY What controls prevent malicious data from entering training pipelines? Is there anomaly detection on incoming training data? How is supply chain integrity for training data maintained? |
×2 | ___ | ||
| Prompt Injection Guardrails AI-ONLY For LLM-based services: are there input sanitization and system prompt protection mechanisms? Has the vendor defined a policy on adversarial prompt handling? |
×2 | ___ | ||
| Model Output Validation AI-ONLY Are there guardrails to prevent the model from returning sensitive training data, PII, or harmful content in outputs? Is output filtering configurable by the enterprise customer? |
×2 | ___ |
| Criterion & Probe Questions | Weight | Score (0–3) | Weighted | Evaluator Notes |
|---|---|---|---|---|
| TCO Predictability AI-ONLY Is pricing per-token, per-API-call, or per-inference-hour? Can the vendor provide consumption modeling tools? Run a 3-year TCO projection against your expected usage volumes. |
×3 | ___ | ||
| Model Portability AI-ONLY If you terminate, can you export model weights, fine-tuning artifacts, or at minimum a full specification of what was trained? Or is the model permanently locked to the vendor's infrastructure? |
×3 | ___ | ||
| Exit Strategy & Transition Support Is there a documented transition assistance period in the MSA? What data export formats are supported? What is the migration path if the vendor is acquired or goes bankrupt? |
×2 | ___ | ||
| Proof-of-Concept on Your Data Is the vendor willing to run a rigorous POC on your actual production data — including edge cases and failure scenarios? POC refusal is a significant red flag. |
×3 | ___ | ||
| Innovation Roadmap Transparency What new model capabilities are planned in the next 12–18 months? Is there a customer advisory board? How fast has the product shipped material updates in the last year? |
×1 | ___ |
| Criterion & Probe Questions | Weight | Score (0–3) | Weighted | Evaluator Notes |
|---|---|---|---|---|
| ▌ Necessary — Integration blockers that must be resolved before deployment | ||||
| CRM Bidirectional Data Sync Necessary
Does the AI platform read from and write back to your CRM (Salesforce, Dynamics, HubSpot)? Can AI-generated insights — recommended actions, risk scores, predicted outcomes — be written as native CRM objects (Tasks, Cases, Opportunity fields)? Is sync real-time or batch? Ask specifically: does a field technician's AI recommendation surface inside the CRM record, or only in a separate portal? |
×3 | ___ | ||
| Native UX Embedding in CRM / Ticketing Necessary
Is the AI experience embedded directly into the agent or technician's existing workflow UI — as a panel, sidebar, or Lightning Web Component — or does it require a context switch to a separate application? Every additional screen costs adoption. Ask for a live demo inside your CRM instance, not a standalone environment. Evaluate: does the ML output display where the work happens? |
×3 | ___ | ||
| Authentication & SSO Integration Necessary
Does the platform support SAML 2.0 / OIDC SSO with your identity provider (Okta, Azure AD, Ping)? Is role-based access control (RBAC) synchronized from your IDP, or must it be maintained separately in the AIaaS platform? Dual-credentialing is a security risk and an adoption killer. |
×3 | ___ | ||
| ▌ Helpful — Significantly improves data quality, model accuracy, and workflow continuity | ||||
| Ticketing & ITSM System Integration Helpful
Does the platform integrate with your ticketing system (ServiceNow, Jira, Zendesk, Freshservice)? Can it auto-populate ticket fields, suggest resolution steps, or predict ticket routing based on ML classification? Does it read historical ticket data to train or fine-tune models? Ask whether ticket closure data flows back to improve model accuracy over time. |
×2 | ___ | ||
| ERP & Data Warehouse Integration Helpful
Can the AI platform ingest data from ERP systems (SAP, Oracle, Infor)? Does it have pre-built connectors or require custom ETL? Confirm support for your data warehouse / lakehouse (Snowflake, Databricks, BigQuery, Redshift). AI models improve dramatically when trained on operational data (parts consumption, asset history, work orders) — a vendor who can't reach this data is working with one hand tied. |
×2 | ___ | ||
| API-First Architecture & Webhook Support Helpful
Is the platform API-first with fully documented REST / GraphQL endpoints? Does it support outbound webhooks to push AI events to downstream systems in real time — rather than requiring polling? Can API payloads be customized to match your existing data schemas, or are you forced to transform data to fit the vendor's model? |
×2 | ___ | ||
| iPaaS & Middleware Compatibility Helpful
Does the vendor offer pre-built connectors for major iPaaS platforms (MuleSoft, Boomi, Informatica, Azure Logic Apps, Workato)? Or does integration require custom code on every endpoint? A vendor with strong iPaaS connectors dramatically reduces integration TCO and accelerates deployment timelines. |
×2 | ___ | ||
| Feedback Loop: Human Corrections Back to Model Helpful
When a technician or agent overrides an AI recommendation inside the CRM or ticketing system, does that correction flow back to improve the model? Is this loop automatic or manual? A platform without a feedback loop degrades over time as real-world behavior diverges from training data. |
×2 | ___ | ||
| ▌ Future — Confirm roadmap support; not required at launch | ||||
| IoT / OT / Edge Data Integration Future
Can the platform ingest real-time telemetry from connected assets, sensors, or SCADA/historian systems (OSIsoft PI, Ignition, Azure IoT Hub)? For industrial and field service use cases this often becomes Necessary in Year 2. Confirm whether edge inference (on-device ML) is on the vendor roadmap. |
×1 | ___ | ||
| Mobile SDK & Offline Inference Future
Is there a mobile SDK for embedding AI into field apps (iOS / Android)? Does it support offline or low-connectivity inference for technicians in the field? This is a differentiator for field service organizations where connectivity is unreliable. |
×1 | ___ | ||
A vendor's marketplace footprint reveals far more than their branding suggests. A native listing on your CRM's app exchange means the integration has passed that platform's security review, uses standard authentication patterns, and can be provisioned without custom development. Partnerships at the ISV or Reseller tier often include co-engineering resources, escalation paths, and joint roadmap alignment. Ask specifically: "Is this a certified listing or just a logo on a partner page?"
For each marketplace below, mark whether the vendor has a listed, certified app — and score the overall marketplace presence in the table that follows.
| Category | Description | Max Possible | Weighted Score |
|---|---|---|---|
| A | Core Vendor Viability | 45 | ______ |
| B | Model Quality & Architecture | 48 | ______ |
| C | Model Lifecycle Management | 36 | ______ |
| D | AI Governance, Ethics & Explainability | 39 | ______ |
| E | Data Governance & IP Ownership | 39 | ______ |
| F | AI-Specific Security | 27 | ______ |
| G | Pricing Model & Exit / Lock-In Risk | 36 | ______ |
| H | Ecosystem Integration, CRM / Ticketing Fit & Marketplace | 66 | ______ |