Cost-Benefit Analysis

Cost-Benefit Analysis

TL;DR

Decomposed architectures add 1.5-2x overhead in latency and cost. Break-even point: worth it when expected incident cost exceeds ~$5,000/month. For customer-facing or autonomous systems, ROI is typically positive.

This page provides concrete numbers for evaluating whether to adopt the framework.

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Engineering Costs

Initial Implementation

Task	Monolithic Agent	Decomposed System	Delta
Architecture design	1-2 days	3-5 days	+2-3 days
Component implementation	3-5 days	5-10 days	+2-5 days
Verification layers	0 days	2-4 days	+2-4 days
Integration testing	2-3 days	4-6 days	+2-3 days
Total	6-10 days	14-25 days	+8-15 days

**Rough cost at $150/hr**: Decomposition adds$10,000-18,000 in initial engineering.

Ongoing Maintenance

Task	Monolithic	Decomposed	Delta
Monitoring review	2 hr/week	4 hr/week	+2 hr/week
Component updates	2 hr/week	3 hr/week	+1 hr/week
Incident investigation	Varies	Easier (isolated)	-20% time
Monthly overhead	~16 hr	~28 hr	+12 hr

Rough cost: Decomposition adds ~$1,800/month in maintenance.

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Runtime Costs

Latency Overhead

Based on preliminary experiments:

Metric	Monolithic	Decomposed	Overhead
Median latency	2.3s	4.1s	1.8x
P99 latency	8.2s	12.1s	1.5x

Why it’s not worse:

• Narrow components use smaller, faster models
• Parallel execution where possible
• Verification is mostly fast code

When latency matters:

• Real-time chat: 1.8x may be noticeable
• Batch processing: Negligible impact
• Async workflows: No user-facing impact

API/Compute Costs

Component	Model	Cost per 1K calls
Monolithic	GPT-4	$120
Decomposed
- Coordinator	GPT-3.5	$2
- Summarizer	Fine-tuned 7B	$5
- Analyzer	Fine-tuned 7B	$5
- Verifier	Code + small LLM	$3
Decomposed Total		$15

Wait—decomposed is cheaper? Yes, when you use appropriate models per component:

Scenario	Monolithic	Decomposed	Savings
Simple tasks (80%)	GPT-4: $120	Narrow models: $15	87%
Complex tasks (20%)	GPT-4: $120	GPT-4 + narrow: $45	62%
Weighted average	$120	$21	82%

Key Insight

Decomposition often reduces API costs because you only use expensive frontier models where needed. Most components can use fine-tuned 7B models at 1/10th the cost.

Total Runtime Cost Comparison

For 100,000 tasks/month:

Cost Type	Monolithic	Decomposed
API costs	$12,000	$2,100
Compute (verification)	$0	$500
Monitoring infrastructure	$200	$400
Total	$12,200	$3,000

In this scenario, decomposition saves $9,200/month in runtime costs.

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Risk Reduction Benefits

Incident Cost Estimation

Incident Type	Probability (Mono)	Probability (Decomp)	Damage	Delegation Risk Mono	Delegation Risk Decomp
Data leak	0.5%/mo	0.1%/mo	$50,000	$250	$50
Harmful output (public)	2%/mo	0.3%/mo	$20,000	$400	$60
Service disruption	1%/mo	0.5%/mo	$10,000	$100	$50
Compliance violation	0.3%/mo	0.05%/mo	$100,000	$300	$50
Total Delegation Risk				$1,050/mo	$210/mo

Risk reduction: $840/month in expected incident cost avoided.

Incident Response Savings

When incidents do occur, decomposition helps:

Metric	Monolithic	Decomposed
Time to identify root cause	4-8 hours	1-2 hours
Time to fix	2-4 hours	0.5-1 hour
Blast radius	Entire system	Single component
Rollback complexity	Full redeploy	Component swap

Estimated savings per incident: 4-6 hours of engineering time (~$600-900).

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Break-Even Analysis

When Does Decomposition Pay Off?

Fixed costs (one-time):

• Additional engineering: $10,000-18,000

Monthly costs:

• Additional maintenance: +$1,800
• Additional monitoring: +$200
• API cost savings: -$9,200 (if high volume)
• Risk reduction: -$840

Net monthly impact (high-volume scenario): -$8,040 (savings)

Break-even: Initial investment recovered in 1.5-2 months.

Break-Even by Use Case

Use Case	Monthly Volume	API Savings	Risk Reduction	Break-Even
Internal tool	1,000 tasks	-$90	-$100	5-8 months
Customer-facing	50,000 tasks	-$4,600	-$500	2-3 months
High-volume SaaS	500,000 tasks	-$46,000	-$800	< 1 month
Autonomous agent	10,000 tasks	-$900	-$2,000	3-4 months

Key Finding

For any system with either (a) >10,000 tasks/month or (b) significant incident cost potential, decomposition has positive ROI within 6 months.

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Decision Framework

Quick Assessment

Answer these questions:

1. What’s your monthly task volume?

   • < 1,000: Light decomposition (2-3 components)
   • 1,000-50,000: Standard decomposition

   • 50,000: Full decomposition + optimization

2. What’s worst-case incident cost?

   • < $5,000: May not be worth full framework
   • $5,000-50,000: Standard application

   • $50,000: Full framework + extensive verification

3. Is latency critical?

   • Real-time (<1s): Careful component design, parallel execution
   • Interactive (<5s): Standard decomposition acceptable
   • Async/batch: Latency overhead irrelevant

4. What’s your engineering capacity?

   • Limited: Start with 2-3 component decomposition
   • Moderate: 5-7 components with verification
   • High: Full framework implementation

ROI Calculator

Monthly Savings = API_Savings + Risk_Reduction - Maintenance_Overhead

Where:
  API_Savings = Volume × (Mono_Cost - Decomp_Cost)
  Risk_Reduction = DR_Mono - DR_Decomp
  Maintenance_Overhead = ~$2,000/month

Break_Even_Months = Initial_Investment / Monthly_Savings

Example:

• Volume: 50,000 tasks/month
• API savings: 50,000 × ($0.12 -$0.02) = $5,000
• Risk reduction: $1,050 -$210 = $840
• Maintenance: -$2,000
• Monthly savings: $3,840
• Initial investment: $15,000
• Break-even: 3.9 months

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When NOT to Use This Framework

Be honest about when decomposition isn’t worth it:

Situation	Recommendation
Prototype/MVP	Skip—validate idea first, add safety later
< 100 tasks/month	Skip—overhead exceeds benefit
Pure internal tool, no sensitive data	Light application only
Latency-critical (< 500ms)	Careful design or skip
No engineering capacity for maintenance	Skip until capacity exists

Caution

Don’t over-engineer safety for systems that don’t need it. The framework is valuable for production systems with real risk, not for every AI application.

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Summary

Metric	Value
Initial engineering overhead	+$10,000-18,000
Monthly maintenance overhead	+$2,000
Latency overhead	1.5-2x
API cost savings	50-80% (when using narrow models)
Risk reduction	70-80% (attack success rate)
Typical break-even	2-6 months
Best for	Customer-facing, autonomous, >10K tasks/mo

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See Also

• Empirical Tests — Experiment data behind these numbers
• Quick Start — Begin implementation
• Decision Guide — Choose the right level of decomposition