Multimodal Prompt Pipeline Token Spend and API Quota Forecasting Pipeline

Role: Staff AI Financial Systems Engineer specializing in multimodal API economics and predictive capacity modeling.

Context

• Cost Matrix: {{multimodal_api_pricing_tier}}
• Usage Records: {{historical_token_consumption_data}}
• Routing Logic: {{vision_model_routing_policy}}
• Capital Guardrails: {{budget_growth_ceiling}}
• Projection Cadence: {{forecast_cadence_interval}}
• Cohort Migration: {{user_tier_expansion_profile}}

Task

Author a production-ready Python analytics and time-series forecasting script that estimates multimodal token burns, vendor API quota exhaustion dates, and cost trajectories across dynamic model routing rules over {{forecast_cadence_interval}}.

Method

1. Ingest {{historical_token_consumption_data}} to establish baseline consumption rates across image analysis (vision input) and image synthesis (diffusion output).
2. Decompose time series into trend, prompt-length seasonality, and anomaly variance.
3. Integrate {{user_tier_expansion_profile}} to model non-linear demand shifts across user volume cohorts.
4. Apply the multi-tier routing logic from {{vision_model_routing_policy}} to simulate token allocation across vendor options.
5. Project cumulative costs against rate cards specified in {{multimodal_api_pricing_tier}}.
6. Compute days-to-exhaustion metrics against {{budget_growth_ceiling}} using bootstrap confidence intervals (80%, 95%).
7. Model automated failover triggers when primary model API quotas hit predictive exhaustion.
8. Produce a formatted tabular projection output and an alert payload flagging margin compression risks.

Constraints

• Code MUST implement a clean statistical forecasting pipeline using Statsmodels or Prophet wrapper patterns in Python.
• The script MUST output deterministic error metrics (MAPE, RMSE) via automated backtesting on historical data splits.
• MUST NOT depend on external unauthenticated network calls during runtime.
• All financial projections MUST be formatted to four decimal places for per-prompt unit economics.

Output format

Executable Python script structured as follows:

1. Abstract Data Models and Cost Ingestion Class
2. Time-Series Feature Engineering & Decomposition Engine
3. Routing-Aware Multi-Tier Financial Forecaster
4. Quota Exhaustion & Budget Alerting Module
5. Execution Harness with Automated Backtest Diagnostics (160-240 lines total)

Self-review

• Ensure the routing simulation strictly respects rules declared in {{vision_model_routing_policy}}.
• Validate that cost formulas correctly apply distinct rates for image resolution tiers from {{multimodal_api_pricing_tier}}.
• Confirm that exhaustion projections flag threshold breaches defined in {{budget_growth_ceiling}}.