Scenario: General Enterprise IT

1. COST PREDICTABILITY (High — 80% Weight)

Why Dominant: Most enterprises have fixed IT budgets with limited appetite for variable cloud costs. Usage-based API pricing can escalate unpredictably with user adoption. CFO visibility into AI spending is critical.

Implication: On-premise CapEx + predictable OpEx is easier to budget. API costs require careful usage governance or risk budget overruns. Hybrid allows tiered usage (expensive queries → on-prem).

2. OPERATIONAL COMPLEXITY (High — 75% Weight)

Why Dominant: Enterprise IT teams are already stretched. Adding GPU infrastructure, model ops, and monitoring overhead must justify itself. Time-to-value and maintenance burden matter more than absolute performance.

Implication: API-first is faster to deploy (days vs. months). On-premise requires hiring or training ML engineers. Hybrid adds complexity but offers flexibility.

3. DATA LOCALITY / PRIVACY (Moderate — 60% Weight)

Why Considered: Confidential business data (M&A plans, financials, customer lists) should not leak. However, most enterprises tolerate third-party SaaS (Salesforce, G Suite) under contract. Data classification determines sensitivity.

Implication: API vendors with strong DPAs (Data Processing Agreements) may be acceptable for non-critical data. On-premise required only for highest-sensitivity data (M&A, trade secrets). Hybrid can route by data classification.

4. LATENCY CONTROL (Moderate — 50% Weight)

Why Considered: Most enterprise productivity use cases tolerate 1-3 second response times. However, customer-facing chatbots and code completion benefit from low latency. Batch jobs (e.g., nightly document summarization) are latency-insensitive.

Implication: API latency (200-500ms base + model inference) is acceptable for most use cases. On-premise sub-50ms P99 is rarely business-critical unless high-frequency trading or real-time systems involved.

5. GOVERNANCE / AUDITABILITY (Moderate — 55% Weight)

Why Considered: Legal, compliance, and security teams want visibility into what data is processed and how. However, strict audit requirements (like FDA Part 11) typically do not apply. SOC 2 / ISO 27001 controls are usually sufficient.

Implication: API vendors with SOC 2 Type II compliance can satisfy most governance needs. On-premise offers more granular logging but requires internal tooling. Logs must be retained per corporate policy (e.g., 7 years).

1. Runaway API Costs

Scenario: LLM tool rolled out to 5,000 employees without usage caps. Usage explodes. Monthly bill jumps from $10K to $200K in three months.
Consequence: Budget crisis, emergency spending approvals, tool access restricted mid-project, user frustration.
Mitigation: Implement per-user quotas, cost monitoring dashboards, alerts at thresholds (e.g., 150% of forecast). Consider on-premise for heavy users.

2. Inadvertent Data Leakage

Scenario: Employee pastes confidential M&A document into API-based chatbot. Vendor logs prompts for model training or monitoring. Data potentially exposed.
Consequence: Legal exposure, competitor advantage, regulatory inquiry (if public company), loss of customer trust.
Mitigation: User training on data classification. DPA negotiation with no-training clause. On-premise for confidential data. Content filtering at ingress.

3. On-Premise Expertise Gap

Scenario: Enterprise deploys on-premise LLM without ML engineering capacity. Model degrades, hardware underutilized, no one can troubleshoot.
Consequence: Sunk CapEx, user abandonment, revert to API (double cost), loss of internal credibility for AI initiatives.
Mitigation: Honest skills assessment before deployment. Hire or train staff, or partner with managed services. API-first is safer if skills lacking.

4. Shadow AI Proliferation

Scenario: IT does not provide approved LLM tools. Employees use personal ChatGPT accounts with company data. No governance, no audit trail.
Consequence: Unmanaged data exposure, compliance violations, inconsistent quality, security team blind spots.
Mitigation: Provide approved tools quickly (API or on-premise). Policy enforcement (DLP rules). Security awareness training.

□ Cost Modeling & Budgeting

• API cost forecast based on user count and usage patterns
• On-premise TCO modeled (hardware, software, staff, facilities)
• Break-even analysis (API vs. on-prem over 3-year horizon)
• Budget approval secured with contingency (e.g., +30%)
• Cost monitoring and alerting configured

□ Data Governance & Privacy

• Data classification scheme applied (public, internal, confidential, restricted)
• DPA negotiated with API vendor (if applicable)
• No-training clause confirmed for proprietary data
• Content filtering or data masking implemented if needed
• User training on acceptable use and data handling

□ Operational Readiness

• Skills assessment completed (ML engineering, DevOps, GPU expertise)
• On-call rotation and escalation path defined
• Monitoring and alerting configured (uptime, latency, costs)
• Disaster recovery plan documented and tested
• Vendor support contract in place (if on-premise hardware)

□ Security & Compliance

• Security review completed (InfoSec sign-off)
• Authentication and authorization integrated (SSO, RBAC)
• Logging captures user actions and data access per policy
• Vendor SOC 2 / ISO 27001 certification verified (if API)
• Data residency requirements met (GDPR, CCPA if applicable)

API-First is most pragmatic when:
• Time-to-value is critical (deploy in days not months)
• ML/GPU expertise is limited or absent
• Usage volume is uncertain (scale elastically)
• OpEx budget available but CapEx constrained
• Data sensitivity is low-to-moderate (non-confidential)

Hybrid balances flexibility and control when:
• Mixed data sensitivity (some confidential, some not)
• Cost control needed at scale but fast start desired
• Some ML expertise available but not deep bench
• Existing on-premise GPU capacity can be leveraged
• Business units have different risk tolerances

On-Premise Only justifies itself when:
• High volume (>1M queries/month) makes API uneconomical
• Strong ML engineering team in place
• High data sensitivity (trade secrets, M&A, financials)
• CapEx budget available and amortization acceptable
• Existing data center with GPU infrastructure

On-Premise is high-risk when:
• No ML engineering capacity (hiring plan undefined)
• No GPU infrastructure (new data center build required)
• Usage volume low (<100K queries/month)
• Business pressure for immediate results (3-6 month deployment too slow)
• IT team already over-stretched with existing systems