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Cost modeling and ROI analysis for specialized LLM development. Use when deciding whether to train a custom model, estimating total cost, or calculating break-even vs frontier APIs. Covers training costs, inference costs, and time-to-ROI projections.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
SOC 직업 분류 기준
Create or link this repo's Atlas research graph so hypotheses, experiments, runs, and decisions are tracked. Use when the canvas says 'no graph for this project', when the user asks to initialize/set up Atlas, or before starting research that should be recorded.
Data structure for annotated matrices in single-cell analysis. Use when working with .h5ad files or integrating with the scverse ecosystem. This is the data format skill—for analysis workflows use scanpy; for probabilistic models use scvi-tools; for population-scale queries use cellxgene-census.
Benchling R&D platform integration. Access registry (DNA, proteins), inventory, ELN entries, workflows via API, build Benchling Apps, query Data Warehouse, for lab data management automation.
Microscopy image analysis for cell biology. Cell segmentation (Cellpose, watershed), object tracking (trackpy), morphology quantification, colony counting, colocalization analysis, and cytoskeleton characterization. For pathology WSI use pathml; for flow cytometry use flow-cytometry-analysis.
Comprehensive molecular biology toolkit. Use for sequence manipulation, file parsing (FASTA/GenBank/PDB), phylogenetics, and programmatic NCBI/PubMed access (Bio.Entrez). Best for batch processing, custom bioinformatics pipelines, BLAST automation. For quick lookups use gget; for multi-service integration use bioservices.
Unified Python interface to 40+ bioinformatics services. Use when querying multiple databases (UniProt, KEGG, ChEMBL, Reactome) in a single workflow with consistent API. Best for cross-database analysis, ID mapping across services. For quick single-database lookups use gget; for sequence/file manipulation use biopython.
| name | model-economics |
| description | Cost modeling and ROI analysis for specialized LLM development. Use when deciding whether to train a custom model, estimating total cost, or calculating break-even vs frontier APIs. Covers training costs, inference costs, and time-to-ROI projections. |
| category | ml-training |
| version | 1.0.0 |
| author | Synthetic Sciences |
| license | MIT |
| tags | ["Economics","Cost Analysis","ROI","Break-Even","Training Cost","Inference Cost","Model Flywheel","Business Case"] |
| dependencies | [] |
Use this skill as the first step of any model-specialization assessment to determine:
Important: Use websearch to get current pricing — these change frequently. Reference tiers as of early 2026:
| Model | Input ($/M) | Output ($/M) | Notes |
|---|---|---|---|
| Claude Opus 4.6 | $5.00 | $25.00 | Highest capability |
| GPT-5.2 Pro | $21.00 | $168.00 | Reasoning-heavy |
Savings potential: Highest. A specialized 8B model costs ~$0.05-0.15/M tokens to serve = 100-500x cheaper.
| Model | Input ($/M) | Output ($/M) | Notes |
|---|---|---|---|
| GPT-5.2 Chat | $1.75 | $14.00 | Most popular |
| Claude Sonnet 4.5 | $3.00 | $15.00 | Good balance |
| Qwen3 Max | $1.20 | $6.00 | Cost-effective |
Savings potential: Strong at volume. Break-even is achievable with moderate traffic.
| Model | Input ($/M) | Output ($/M) | Notes |
|---|---|---|---|
| Gemini 3 Flash | $0.50 | $3.00 | Fast, cheap |
| GLM 4.7 | $0.40 | $1.50 | Very cost-effective |
| Mistral Small | $0.10 | $0.30 | Cheapest commercial |
Savings potential: Harder to beat on cost alone. Specialize for quality, not cost.
Many open-weight models available on OpenRouter at $0. If you only need basic capability, self-hosting these is essentially free beyond GPU cost.
Cheapest option for supported models. No GPU management.
| Model Size | Method | Cost/1K examples | Duration |
|---|---|---|---|
| 8B | LoRA | ~$2-5 | 15-30 min |
| 14B | LoRA | ~$5-10 | 30-60 min |
| 32B | LoRA | ~$10-25 | 1-3 hours |
| 70B | LoRA | ~$25-60 | 3-8 hours |
Load tinker-training-cost skill for exact per-token rates.
| GPU | Cost/Hour | Best For |
|---|---|---|
| A100-40GB | ~$1.10 | 7-14B LoRA, 7B full |
| A100-80GB | ~$1.60 | 14-32B LoRA, 14B full |
| H100-80GB | ~$3.25 | 32-70B LoRA, large batches |
Typical training runs:
| GPU | Cost/Hour | Best For |
|---|---|---|
| A100-80GB | ~$1.50 | Standard training |
| H200-141GB | ~$3.00 | Large models, fast training |
| B200-192GB | ~$4.50 | Cutting-edge, maximum throughput |
Best for: Multi-day training, large-scale RL, dedicated inference.
The key calculation: what does it cost to serve the specialized model vs the frontier API?
def inference_cost_per_million_tokens(gpu_cost_per_hour, tokens_per_second):
"""Calculate cost per million tokens for self-hosted inference.
Args:
gpu_cost_per_hour: GPU rental cost (e.g., $3.25 for H100)
tokens_per_second: Model throughput (varies by model size and batch)
"""
tokens_per_hour = tokens_per_second * 3600
cost_per_token = gpu_cost_per_hour / tokens_per_hour
cost_per_million = cost_per_token * 1_000_000
return cost_per_million
| Model Size | GPU | Tokens/sec (batch) | Cost/M tokens |
|---|---|---|---|
| 8B | A100-80GB | ~3,000-5,000 | $0.09-0.15 |
| 8B | H100-80GB | ~5,000-8,000 | $0.11-0.18 |
| 14B | A100-80GB | ~1,500-3,000 | $0.15-0.30 |
| 14B | H100-80GB | ~3,000-5,000 | $0.18-0.30 |
| 32B | H100-80GB | ~800-1,500 | $0.60-1.10 |
| 70B | 2xH100 | ~500-1,000 | $1.80-3.60 |
Key insight: A specialized 8B model on Modal/TensorPool costs $0.05-0.15/M tokens vs $5-25/M for premium frontier = 50-500x cheaper per request.
def break_even_analysis(
monthly_api_spend: float,
training_cost: float,
monthly_inference_cost: float,
setup_hours: float = 40,
hourly_rate: float = 100,
):
"""Calculate break-even timeline for model specialization.
Args:
monthly_api_spend: Current monthly frontier API cost
training_cost: One-time training cost (GPU + data prep)
monthly_inference_cost: Monthly cost to serve specialized model
setup_hours: Engineering time to set up pipeline
hourly_rate: Engineering cost per hour
"""
engineering_cost = setup_hours * hourly_rate
total_upfront = training_cost + engineering_cost
monthly_savings = monthly_api_spend - monthly_inference_cost
if monthly_savings <= 0:
return {
"viable": False,
"reason": "Specialized model costs more to serve than frontier API",
"monthly_savings": monthly_savings,
}
months_to_break_even = total_upfront / monthly_savings
return {
"viable": True,
"total_upfront_cost": total_upfront,
"training_cost": training_cost,
"engineering_cost": engineering_cost,
"monthly_api_spend": monthly_api_spend,
"monthly_inference_cost": monthly_inference_cost,
"monthly_savings": monthly_savings,
"months_to_break_even": round(months_to_break_even, 1),
"annual_savings": monthly_savings * 12 - total_upfront,
"year_2_savings": monthly_savings * 12, # Fully amortized
}
# Example: Replace GPT-4o for a specific task
result = break_even_analysis(
monthly_api_spend=5000, # $5K/month on GPT-4o
training_cost=500, # $500 for LoRA fine-tune + data prep
monthly_inference_cost=200, # $200/month on Modal (8B model)
setup_hours=40, # 1 week of engineering
hourly_rate=100, # $100/hr engineer
)
# Result: Break-even in ~1.1 months, $52K annual savings after year 1
| Month | Frontier Cost | Specialized Cost | Cumulative Savings | Notes |
|---|---|---|---|---|
| 0 | — | $4,500 upfront | -$4,500 | Training + engineering |
| 1 | $5,000 | $200 | $300 | First month live |
| 2 | $5,000 | $200 | $5,100 | Break-even |
| 3 | $5,000 | $200 | $9,900 | |
| 6 | $5,000 | $200 | $24,300 | |
| 12 | $5,000 | $300 | $52,200 | Includes retrain cost |
Route easy requests to specialized model, hard ones to frontier:
Ask the user these to determine viability:
What's your monthly frontier API spend?
How constrained is the task?
What production data do you have?
What's your quality bar?