Reference
Token Economics
Most platforms now offer token usage dashboards. Tracking is not the problem — Oracle, Microsoft, and SAP all show you what you spent. The problem is optimization at scale across providers and business units: routing decisions, caching strategies, budget enforcement, and cost attribution as a financial controllership discipline. The FinOps Foundation documents 30–200x cost variance between optimized and unoptimized deployments. This section explores the optimization layer that sits above any single vendor’s monitoring.
Disclaimer: All pricing, calculations, and architectures are generic and synthetic. They reference publicly available provider rates and common infrastructure patterns — not the proprietary systems or intellectual property of any current or former employer.
Representative pricing as of February 2026. LLM API rates change frequently — verify with providers before use in production cost models. Sources: provider pricing pages. All figures represent marginal token costs only and do not reflect total cost of ownership including infrastructure, engineering, operations, or organizational change.
| Model | Provider | Input $/1M | Output $/1M | Context | Tier | Best For |
|---|---|---|---|---|---|---|
| Claude Sonnet 4.6 | Anthropic | $3.00 | $15.00 | 200K | 1 | Financial analysis, compliance docs |
| GPT-5.4 | OpenAI | $1.75 | $14.00 | 200K | 1 | Complex reasoning, exception triage |
| GPT-5 Mini | OpenAI | $0.25 | $2.00 | 200K | 2 | Forecasting, high-volume tasks |
| Claude Haiku 4.5 | Anthropic | $1.00 | $5.00 | 200K | 2 | Budget checks, routing |
| Gemini 2.5 Flash | $0.30 | $2.50 | 1M | 2 | High-volume ad-hoc queries | |
| DeepSeek V3.2 | DeepSeek | $0.28 | $0.42 | 128K | 2 | Classification, extraction |
| GPT-5 Nano | OpenAI | $0.05 | $0.40 | 400K | 2 | High-volume simple tasks |
Tier 1 vs Tier 2 Cost Difference
75–95%
Understanding cost tiers helps match model capability to task requirements. Tier 2 models handle classification, extraction, and routine tasks at a fraction of Tier 1 pricing.
NSCP Model Policy
Tier 1SOX reporting, compliance, complex analysis
Tier 2Ad-hoc queries, dev/test, classification
BudgetAuto-downgrade at 80% utilization
Blended Cost/1K Tokens
80/20 input/output token split
Claude Sonnet 4.6$0.0054
GPT-5.4$0.0042
GPT-5 Mini$0.0006
DeepSeek V3.2$0.0003
Inference Gateway Architecture
Token Tracking PipelineForward Path — Request
Agent
Initiates call
Initiates call
→
Inference
Gateway
Auth + Route
Gateway
Auth + Route
→
Pre-Call Check
Budget + Token Est.
Budget + Token Est.
→
Model Router
Tier enforcement
Tier enforcement
→
LLM API
Provider
Provider
Return Path — Response + Logging
Agent
Receives result
Receives result
←
Gateway
Strip metadata
Strip metadata
←
Post-Call Log
Actuals + Budget
Actuals + Budget
←
LLM Response
+ Usage headers
+ Usage headers
Downstream Consumers
Token Logger
→
fact_token_usage
→
Token Economics Dashboard
Budget Controller Agent
TGC Controls (TGC-001–006)
Gateway Design Targets
Overhead: <12ms p99
Availability: 99.95%
Peak RPS: 247
Cache hit: 44.2%
Availability: 99.95%
Peak RPS: 247
Cache hit: 44.2%
Pre-Call Checks
✓ Agent budget available?
✓ Model tier authorized?
✓ Token estimate within limit?
✓ Prompt hash — cache check
✓ Rate limit headroom?
Post-Call Actions
→ Write to fact_token_usage
→ Decrement agent budget
→ Update cache registry
→ Fire alerts if threshold crossed
→ Tag cost to business unit
Calculator Inputs
Input/Output split: 80% input / 20% output tokens
Payback assumes estimated platform setup cost ($5K–$60K based on volume)
Payback assumes estimated platform setup cost ($5K–$60K based on volume)
Monthly Token Cost
—
Cost per Run
—
Cost per 1K Tokens
—
Annual Projection
—
Tokens per Dollar
—
Prompt Caching
Cache frequently used system prompts and static context. Dramatically reduces input token processing cost.
Savings: 50–90% on cached portions
Intelligent Model Routing
Route simple tasks to Tier-2 models. Reserve Tier-1 for complex reasoning, SOX compliance, and financial judgment.
Savings: 60–80% on routed calls
Batch Processing
Group similar requests to amortize system prompt overhead. Effective for reconciliation checks and exception classification.
Savings: 20–30% on batch-eligible tasks
Token Budget Allocation
Set daily/monthly token budgets per agent. Controller enforces hard limits, triggers downgrades at 80%, blocks at 90%.
Prevents runaway costs
Prompt Engineering
Optimize prompt structure to reduce tokens without degrading quality. Use structured output formats.
Typical reduction: 15–25% on input tokens
Semantic Caching
Cache responses for semantically similar queries using embedding-based similarity. Return cached responses with metadata.
Hit rate improvement: 10–20%
Combined Optimization Impact
| Optimization | Applicable Calls | Reduction | Monthly Reduction | Effort |
|---|---|---|---|---|
| Model Routing | 70% | 65% | $812 | Low |
| Prompt Caching | 44% hit | 70% | $218 | Med |
| Prompt Engineering | 100% | 20% | $110 | Med |
| Batch Processing | 35% | 25% | $64 | High |
| Semantic Caching | 15% | 100% | $47 | High |
| Total (combined) | ~80% | $1,251/mo | ROI: 12x |