Designing and Implementing a Complete LLM Cost Optimization Pipeline

Course: LLM Cost Engineering — From Token Economics to Production Monitoring

Student Level: Undergraduate Year 3 / Postgraduate

Submission Platform: Moodle (Learning Management System)

Individual / Group: Individual Assignments

Purpose

This assignment requires you to design and implement a comprehensive cost optimization framework for a real or hypothetical LLM-powered application. You will leverage every core concept introduced across Chapters 1–9: understand token economics, compress prompts, route tasks to appropriate models, implement caching and batch strategies, design fallback chains, evaluate fine-tuning ROI, and build production cost monitoring. By the end, you will have a reusable framework that reduces LLM costs by 40–60% while maintaining quality.

Connection to Course Learning Outcomes (CLOs)

CLO	Description	How This Assignment Addresses It
CLO-1	Understand LLM pricing models and cost drivers	Tasks 1–2: Analyze pricing, calculate token overhead, measure TER
CLO-2	Measure and reduce token waste through compression	Tasks 2–3: Compress prompts, compare before/after quality and cost
CLO-3	Design capability-based routing for cost efficiency	Task 4: Implement routing strategies and model selection logic
CLO-4	Apply caching and batch strategies for discounts	Task 5: Integrate provider caching and batch queuing
CLO-5	Engineer resilience with fallback chains and budgets	Task 6: Build circuit breakers and degradation patterns
CLO-6	Evaluate fine-tuning break-even and distillation ROI	Task 7: Calculate TCO and determine ROI threshold
CLO-7	Build production telemetry and cost observability	Task 8: Implement monitoring, dashboards, and alerting

Learning Objectives

By completing this assignment, you will be able to:

• Analyze LLM pricing across multiple providers and calculate total cost of ownership.

• Identify token waste patterns in real prompts and reduce them by 40–70%.

• Design a routing system that matches task complexity to model cost and capability.

• Integrate caching (provider-native, exact-match, semantic) and batch APIs to capture 50–90% discounts.

• Implement fallback chains, circuit breakers, and budget-aware degradation for production reliability.

• Evaluate fine-tuning break-even economics and determine when custom models justify their cost.

• Engineer end-to-end observability with cost tracking, dashboards, and alerts.

• Synthesize all components into a unified framework that can be deployed to a production application.

Task Description

• Duration: 10–14 days from assignment release

• Type: Individual Assignment

• Difficulty Level: Medium → Advanced

You are tasked with choosing a real or hypothetical LLM application (e.g., customer support chatbot, document summarization service, RAG-powered Q&A system, content generation platform). Your framework must demonstrate all nine cost optimization techniques from the course.

Task 1: Cost Landscape Analysis  (10 Marks)

Analyze LLM pricing across providers and establish a cost baseline for your chosen application.

Requirements

• Create a pricing matrix comparing 8–10 models from at least 3 providers (OpenAI, Anthropic, Google, etc.)

• Calculate cost per token (input, output, cached, batch) for each model

• Estimate monthly costs for your application across three scenarios:

• Current (no optimization): all requests → most expensive model

• Conservative (basic optimization): some routing

• Aggressive (full optimization): routing + caching + batching

• Identify cost drivers: which workloads consume the most tokens? Which features are most expensive?

• Demonstrate 100× cost range: show the absolute cheapest and most expensive model option

Deliverables

Pricing comparison table, cost baseline calculations, scenario visualizations, and analysis of cost distribution.

Task 2: Tokenization & Efficiency Audit  (15 Marks)

Measure token usage and identify waste in your application's prompts.

Requirements

• Implement a token counter for your application's system prompt, user queries, and outputs

• Audit a real system prompt and identify token waste: redundant instructions, unused tool definitions, verbose formatting

• Calculate Token Efficiency Ratio (TER) for 3 different feature types (goal: >20%)

• Build a TokenBudget class that pre-calculates request costs before API submission

• Demonstrate token differences across models (GPT-4o, Claude, Gemini tokenize differently)

• Create a compression potential analysis: which prompts have the highest savings opportunity?

Deliverables

Token audit report, TER measurements, compression opportunity inventory, and before/after token counts for 5 prompts.

Task 3: Prompt Compression  (15 Marks)

Reduce input tokens by 40–70% through compression techniques.

Requirements

• Implement 5 compression strategies:

• Manual distillation (remove redundancy)

• Few-shot pruning (reduce examples)

• Schema-driven formatting (structured output)

• Rolling summaries (for multi-turn conversations)

• LLM-powered compression (use cheap model to distill)

• Compress your system prompt by at least 40%; document specific cuts

• Compare quality before/after: run the same user query through original and compressed prompts, measure output quality

• Build a cost/quality trade-off matrix: is 50% token reduction worth the quality loss?

• Demonstrate output control: add max_tokens, JSON schema, and conciseness instructions to reduce output verbosity

• Calculate monthly savings for your application (original vs. compressed)

Deliverables

Compression techniques with examples, before/after quality comparison, cost savings calculation, and trade-off recommendations.

Task 4: Model Routing  (15 Marks)

Implement intelligent routing that matches tasks to appropriate-cost models.

Requirements

• Define a capability framework with 3 task tiers:

• Tier 1 (simple): classification, routing, extraction (~$0.15/MTok)

• Tier 2 (moderate): summarization, analysis (~$2.00/MTok)

• Tier 3 (complex): reasoning, planning (~$5–15/MTok)

• Classify 20 production tasks into tiers based on actual complexity

• Implement a ModelRouter class with 3 routing strategies:

• Rule-based (intent → tier → model)

• Embedding-based (semantic similarity to reference tasks)

• LLM-based (use cheap model to decide routing)

• Build a routing matrix: task → recommended model → estimated cost

• Simulate cost savings: if 70% of tasks route to Tier 1 and 25% to Tier 2, how much cheaper than all-Tier 3?

• Implement cascade/fallback: if primary model fails, escalate to more expensive model

Deliverables

Routing code and strategy comparison, routing matrix, cost simulation (target: 30–40% savings), and fallback logic.

Task 5: Caching & Batch Strategies  (15 Marks)

Capture provider discounts through caching and batch processing.

Requirements

• Implement provider-native caching: show cached tokens cost 10% of normal price

• Build an exact-match cache class with TTL and hit/miss rate tracking

• Implement semantic caching: use embeddings to match similar queries and reuse results

• Design a hybrid queue with real-time queue (urgent) and batch queue (24-hour latency, 70% of traffic)

• Build a batch payload generator (JSONL format) and simulate batch cost (50% discount)

• Calculate cache ROI: for N repeated queries, how much do you save vs. original cost?

• Measure hit rate in realistic workload simulation

Deliverables

Caching implementations, cache hit/miss analysis, batch cost comparison, ROI calculations, and hybrid queue design diagram.

Task 6: Fallback & Resilience Patterns  (10 Marks)

Engineer production reliability and budget-aware degradation.

Requirements

• Build a FallbackChain class: primary → secondary → tertiary model, logging which model handled each request

• Implement a CircuitBreaker: detect provider outages and stop retrying after N failures

• Design budget-aware degradation:

• If monthly spend hits 80% of budget → degrade to cheaper models

• If spend hits 95% → switch to Tier 1 only

• Log all degradation events

• Simulate provider outage: show how requests failover and cost impact

• Demonstrate graceful failure: when all models fail, return a meaningful error

Deliverables

Fallback chain code, circuit breaker state transitions, budget degradation logic, and outage simulation results.

Task 7: Fine-Tuning & Distillation Economics  (10 Marks)

Evaluate when custom models justify their cost.

Requirements

• Calculate break-even analysis: fine-tuning cost vs. inference cost, break-even curve for 3 scenarios

• Build a distillation pipeline (conceptual): teacher model labels data, student model is fine-tuned, cost comparison

• Evaluate true TCO over 1 year: upfront training, ongoing inference, quality maintenance costs

• Determine ROI threshold: 'Fine-tuning makes sense if > X queries/month'

• Compare alternatives: fine-tuning vs. prompt optimization vs. routing

Deliverables

Break-even calculations and curves, distillation pseudocode, 1-year TCO comparison, and ROI recommendation.

Task 8: Production Monitoring & Observability  (20 Marks)

Build a complete cost monitoring system with dashboards and alerts.

Requirements

• Design a telemetry schema capturing: request ID, timestamp, feature, user, model used, input/output/cached tokens, cost, latency, error status

• Implement SQLite logging: create schema, seed 10,000+ simulated API calls, query for cost trends and anomalies

• Build a cost tracking @decorator that wraps API calls, auto-logs telemetry, and aggregates by feature/model/user

• Create 3 dashboards (Plotly or similar): spend by feature (pie), cost trends over time (line), cost per model (bar), top 5 expensive features (ranked)

• Implement cost alerting: alert if daily spend > threshold, feature cost spikes >20%, or TER drops below 15%

• Run an optimization loop: detect highest-cost feature, propose optimization, simulate savings, log before/after metrics

Deliverables

Telemetry schema, SQLite database with 10K+ logged calls, @decorator implementation, dashboards, alerting logic, and optimization loop demo.

Difficulty & Scope

Level	Description
Basic (50–64%)	All 8 tasks completed with runnable code. Cost calculations correct. Some visualization. Minimal analysis or comparison.
Proficient (65–79%)	All tasks completed with clear code structure. Compression works, routing strategies implemented, caching and batch design sound. Cost simulations show realistic savings. Analysis compares trade-offs. Report is well-structured and readable.
Advanced (80–100%)	All tasks at high standard. Code is modular, extensible, production-ready. Compression achieves 40–60% token savings. Routing saves 30–40% cost. Caching ROI calculated accurately. Fine-tuning break-even determined for realistic workload. Monitoring system is end-to-end and generates clear, actionable insights. Analysis includes production deployment considerations. Report reads like a technical architecture document.

Marking Rubric

Criteria	Marks	Description
Cost Landscape Analysis	10	Pricing matrix complete, cost baseline calculated, 3 scenarios estimated, cost drivers identified
Tokenization & Efficiency Audit	15	Token counter built, system prompt audited, TER measured for 3 features, compression opportunity identified
Prompt Compression	15	5 compression strategies implemented, 40%+ reduction achieved, quality comparison provided, savings calculated
Model Routing	15	Capability framework defined, 3 routing strategies implemented, routing matrix created, 30–40% savings simulated
Caching & Batch Strategies	15	Exact-match cache, semantic cache, batch queue implemented, hit rates measured, ROI calculated
Fallback & Resilience	10	FallbackChain, CircuitBreaker, budget degradation implemented, outage simulation, graceful failure
Fine-Tuning Economics	10	Break-even analysis, distillation pipeline, TCO calculated, ROI threshold determined
Production Monitoring	20	Telemetry schema, SQLite logging, @decorator, 3 dashboards, alerting, optimization loop
Total	100

Formatting & Structural Requirements

Element	Requirement
Code	Jupyter Notebook (.ipynb) with clear markdown headers separating each task
Report	6–10 pages, PDF or DOCX
Font	Times New Roman or Calibri, 12pt body text, 14pt headings
Spacing	1.5 line spacing
Margins	2.54 cm (1 inch) on all sides
Heading Structure	H1 for Task titles, H2 for sub-sections, H3 for analysis questions
Page Limit	Report: 6–10 pages (excluding code). Notebook: no page limit
Citation Style	IEEE format
Required Sections in Report	(1) Executive Summary, (2) Application Overview, (3) Cost Baseline & Drivers, (4) Optimizations Implemented, (5) Cost Savings & ROI, (6) Production Deployment Strategy, (7) Conclusion & Learnings
Code Quality	Well-commented, PEP 8 compliant, docstrings on all classes/methods, modular design
Visualizations	Include charts (pricing comparison, cost trends, savings projections) and tables (routing matrix, TCO)

Permitted Resources & Academic Integrity Policy

Permitted

• Course notebooks (Chapters 1–9) and all provided course materials

• Python standard library documentation

• Pandas and Plotly documentation (recommended for data analysis and visualization)

• Stack Overflow for syntax-level clarifications

• Libraries: pandas, plotly, tiktoken, sqlite3, json — pre-approved

• LLM API documentation (OpenAI, Anthropic, Google)

Not Permitted

• Copying code verbatim from external tutorials, GitHub repos, or AI-generated solutions without understanding

• Sharing code, notebooks, or reports with other students

• Using pre-built cost optimization frameworks or libraries (you must build from scratch)

AI Use Policy

• AI tools (ChatGPT, GitHub Copilot, etc.) may be used for concept clarification and debugging assistance only

• All code logic must be your own implementation

• Any AI-assisted content must be explicitly declared in the AI-Use Declaration

• Undeclared AI-generated code or text will be treated as plagiarism

Declaration Statement

Must be signed and included at the top of your report:

I, [Full Name], Student ID [ID], hereby declare that this submission is entirely my own work unless otherwise referenced and acknowledged. I have not engaged in plagiarism, collusion, or contract cheating. I have declared all AI tool usage below.

AI Tools Used: [List tools, e.g., 'ChatGPT for debugging DataFrame operations' / 'GitHub Copilot for SQL syntax' / 'None']

Signature: _________________ Date: _________________

Step-by-Step Submission Instructions on Moodle

• Log in to Moodle at your institution's URL using your student credentials.

• Navigate to "LLM Cost Engineering" in your course list.

• Click on "Assignment 1: Cost Optimization Framework" under the Assignments section.

• Prepare your submission as a single ZIP file with the following structure:

<YourName>_LLMCost_Assignment1.zip

├── notebook.ipynb          (Jupyter Notebook with all code and outputs)

├── report.pdf              (or report.docx)

└── outputs/                (optional folder)

    ├── pricing_data.csv

    ├── cost_projections.json

    ├── telemetry.db

    └── dashboard_screenshots/

• File Naming Convention: Lastname_Firstname_LLMCost_Assignment1.zip

• Click "Add submission" → "Upload a file" → drag and drop your ZIP file.

• Accepted file formats: .zip only (maximum 50 MB).

• Click "Save changes" — verify your file appears in the submission area.

• IMPORTANT: Click "Submit assignment" to finalise. A draft is NOT a submission.

• You will receive a confirmation email from Moodle. Save this as proof of submission.

Deadline: Submit within 14 days from assignment release date.

Late Submission Policy: Submissions more than 7 days late will not be accepted without documented extenuating circumstances.

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