Model Routing Economics Decision Brief

Prepare an economics decision brief for routing AI workloads across models, providers, configurations, or local and hosted routes. Optimize for accepted outcomes and service constraints, not headline token price.

Provide:
- Task types, risk tiers, languages, modalities, context sizes, tool needs, user segments, volumes, and service requirements: [Workload slices and service requirements]
- Model/provider capabilities, context, tools, regions, data terms, availability, and compatibility evidence: [Model route capability evidence]
- Slice-level accepted-output quality, retries, reviewer rework, latency distribution, errors, fallbacks, token/tool usage, and cost: [Quality latency reliability and cost results]
- Forecast volume, concurrency, rate limits, capacity, reserved spend, discounts, minimums, and contract terms: [Volume capacity and commercial constraints]
- Safety and compliance limits, fallback compatibility, portability, migration, concentration, and outage constraints: [Risk fallback and switching constraints]
- Product, finance, model, service, security/privacy, and release owners plus monitoring and change limits: [Decision owners and monitoring limits]

Do not compare routes using different task populations without adjustment. Do not invent benchmark equivalence, prices, acceptance rates, or capacity. Distinguish observed evidence from inference. Separate vendor list price, contracted marginal cost, allocated fixed cost, and full cost per accepted outcome. A cheaper route is not admissible for a slice it cannot safely or reliably serve.

Decision method:

1. Define route eligibility by slice.
   List mandatory capability, data, policy, tool, context, latency, and reliability requirements. Exclude routes that fail a non-negotiable constraint before economic comparison.

2. Normalize evidence.
   Align time period, workload, units, quality definition, accepted-output rule, retries, cache behavior, and reviewer intervention. Flag non-comparable tests and missing production evidence.

3. Build full route economics.
   Calculate cost per attempt and per accepted outcome using supplied inputs. Include tokens, tools, retrieval, infrastructure, retries, fallbacks, validation, review/rework, failed outcomes, and allocated commitments where decision-relevant.

4. Model latency and reliability consequences.
   Use distribution or percentile evidence rather than averages alone. Include rate-limit failure, queueing, cold starts, timeouts, and fallback amplification. Quantify only when supplied data permits.

5. Compare routing strategies.
   Evaluate single route, rule-based slice routing, staged escalation, confidence/validator routing, portfolio routing, and local/hosted split. Include operational complexity, observability, testing, fallback compatibility, and switching burden.

6. Stress test assumptions.
   Vary volume, acceptance, token use, review rate, outage, price, discount, model quality, and traffic mix. Identify route changes and economic breakpoints.

7. Select a routing policy.
   For each slice, choose primary route, fallback or escalation, eligibility gate, budget guardrail, monitoring, and reassessment trigger. Preserve owner authorization for high-risk or data-restricted slices.

Use ChatGPT to compare the supplied routing evidence and scenarios; do not claim that benchmarks, failovers, or cost measurements were executed unless results are provided. The service owner approves routing constraints and the release owner authorizes production changes. For every candidate route, state the expected observation, actual observation when available, acceptance evidence, unresolved service-contract gap, and rollback condition.

Record approval from the service owner for routing constraints and approval from the release owner before any production routing change.

Required deliverable:

# Model Routing Economics Decision Brief

## Workload and Eligibility Matrix
| Slice | Volume | Mandatory requirements | Eligible routes | Excluded route/reason | Owner |
|---|---:|---|---|---|---|

## Evidence Normalization Ledger
| Route/slice | Evidence period | Quality/acceptance basis | Latency basis | Cost basis | Comparability gap |
|---|---|---|---|---|---|

## Route Economics
| Route/slice | Cost/attempt | Acceptance rate | Cost/accepted outcome | P95 latency | Reliability | Review/rework | Confidence |
|---|---:|---:|---:|---:|---:|---:|---|

## Strategy Comparison
| Strategy | Expected economics | Quality/risk | Complexity | Capacity | Concentration/switching exposure |
|---|---|---|---|---|---|

## Routing Decision
| Slice | Primary route | Escalation/fallback | Eligibility gate | Budget guardrail | Stop/review trigger |
|---|---|---|---|---|---|

## Assumptions and Reassessment
List decision-critical assumptions, breakpoints, evidence gaps, monitoring owners, and changes that require a new routing decision.

Completion requires comparable slice-level evidence, full cost per accepted outcome, exclusion of inadmissible routes before optimization, and a monitorable policy with clear fallback limits.