AI Economics and Strategy

Training cost by scale, build vs fine-tune vs API, model roadmap, required competencies. Updated April 2026.

Overview

Building your own LLM is an economic decision before it is a technical one. The total cost varies by orders of magnitude depending on scale, and the right strategy depends on the use case.

This document answers:

1. How much does it cost to train at each scale?
2. When is it worth building from scratch vs fine-tune vs using an API?
3. What is the ideal roadmap to evolve your own model?
4. What team and competencies are needed?

Training Cost by Scale

Pretraining from scratch

* Rate of R$ 5.70/USD, approximate cloud values (Lambda Labs, CoreWeave). On-premise can be 30–50% cheaper in 3-year TCO.

GPU cost breakdown

Note: AWSGCPAzure are 10–15× more expensive than GPU-specialized providers. For LLM training, always use LambdaCoreWeaveRunPod or on-premise.

Fine-tuning (QLoRA/LoRA)

Fine-tuning is 100–1000× cheaper than pretraining.

Build vs Fine-Tune vs API

Decision matrix

When to use each approach

API (GPT-4o, Claude, Gemini)

Worth it when:

• Prototyping a product (validate demand before investing)
• The use case is not core to the business
• There is no sensitive data
• Usage volume is low (< 1M tokens/day)

Not worth it when:

• The product IS the AI (lock-in is an existential risk)
• Sensitive data (health, legal, financial)
• High volume (monthly cost explodes)
• Critical latency (< 100ms)

Fine-Tune (open-source model)

Worth it when:

• The use case is core but does not justify pretraining
• You need domain customization (code, legal, medical)
• You want privacy without pretraining cost
• You have quality proprietary data

Not worth it when:

• The base model already solves 95% of cases
• You lack quality data to fine-tune
• You need capabilities the base model does not have (e.g., advanced math reasoning)

Build from scratch

Worth it when:

• AI is the company's main product
• You need capabilities no existing model has
• You have budget and team for 6–12 months of development
• You want sustainable competitive differentiation

Not worth it when:

• It is the first time building AI (start with fine-tune)
• The market is evolving too fast (model becomes obsolete)
• You do not have enough proprietary data

Recommendation for Koder

Phase 1 (0–3 months): API + Fine-tune

• Use API to prototype products
• Fine-tune Qwen2.5-Coder-32B for Kode (coding assistant)
• Cost: \(500–\)3K

Phase 2 (3–9 months): Advanced fine-tune

• Fine-tune with RLVR (reinforcement learning from verifier rewards)
• Build a proprietary Koder code dataset
• Train your own reward model
• Cost: \(5K–\)30K

Phase 3 (9–18 months): Own model

• Pretrain a 7–30B model focused on code + natural language
• Dataset of 1–3T tokens (The Stack + curated CommonCrawl + Koder data)
• Cost: \(150K–\)600K

Model Roadmap

Maturity model

Level 0: External API
  → GPT-4o, Claude, Gemini
  → Zero control, maximum vendor lock-in

Level 1: Fine-tune an open model
  → Llama-3.1-8B or Qwen2.5-Coder-32B fine-tuned
  → Domain customization, self-hosted
  → 1–4 weeks to produce

Level 2: Advanced fine-tune + RLVR
  → SFT + DPO + RLVR with your own reward model
  → Aligned with Koder user preference
  → 1–3 months

Level 3: Pretrain a niche model
  → 7–13B model pretrained on code + technical docs
  → Differentiation in coding tasks
  → 3–6 months

Level 4: Full-stack own model
  → 30B+ model pretrained from scratch
  → Own architecture (e.g., MoE, hybrid)
  → Sustainable differentiation
  → 6–18 months

When to scale

Team and Competencies

Minimum team for each level

Required technical competencies

Hiring profile

Senior ML Engineer (distributed training):

• 3+ years with PyTorch at scale
• Experience with FSDPDeepSpeedMegatron
• Has trained or fine-tuned a 7B+ model
• Understands parallelism (data, tensor, pipeline)

Data Engineer (data pipeline):

• Experience with SparkRayDask
• Data pipeline at TB+ scale
• Deduplication, filtering, data quality

MLOps Engineer:

• Model versioning, experiment tracking
• Deploy models to production
• Monitoring, A/B testing, canary

Total Cost of Ownership (TCO)

3-year comparison

Conclusion: Self-hosted fine-tune is the sweet spot for most companies. Pretraining only pays off for companies where AI is the core of the business.

On-premise vs Cloud

Rule of thumb: If you will use GPUs for > 2 consecutive years, on-premise is cheaper. If it is intermittent or uncertain, cloud.

For Kode

Strategy recommendation

Short term (0–6 months):

1. Fine-tune Qwen2.5-Coder-32B with Koder code data
2. RLVR with a reward model based on unit tests
3. Self-hosted with 2× RTX 4090
4. Cost: \(3K–\)10K

Medium term (6–12 months):

1. Pretrain a 7B model focused on code + documentation
2. Dataset: The Stack + curated CommonCrawl + Koder data
3. Infra: 4× A100 80GB (cloud or on-premise)
4. Cost: \(150K–\)300K

Long term (12–24 months):

1. 30B+ model with own architecture (MoE?)
2. Multimodal (code + diagrams + docs)
3. Cost: \(500K–\)1.5M

Suggested budget

References