Build vs. Buy: AI Infrastructure Decisions

Every week, a new AI product launches. "Use our API!" "Just use LangChain!" "Fine-tune your own model!"

The question stays the same: build or buy?

The Decision Framework

Use External AI When:

You need the best model (GPT-4, Claude 3.5)

You don't have ML engineering capacity

Your use case is generic (chat, summarization)

You can afford API costs at scale

Build Local/Custom When:

Privacy matters (data can't leave)

Cost matters (predictable vs. variable)

Latency matters (local vs. network)

Specific capability (fine-tuned for your domain)

My Stance

I use both:

External API: For capability when local isn't enough

Local ONNX: For privacy, speed, cost-sensitive tasks

The "AI platform" maximalism (everything via API) is ending. Hybrid is the new normal.

The Build Economics

Building your own AI infrastructure:

Hardware: $500-2000 for a capable local rig

Models: Free (open weights)

Integration: One-time dev cost

Running cost: Electricity (~10$/month)

Vs. API costs:

Pay per token: ~$1-10/1M tokens depending on model

Scales with usage: Success = higher bills

For a solo builder:

Build local first

Add external for scale or capability

What Roman Did

RyzenAI uses local inference (Qwen ONNX). When that's insufficient, we fall back to external models for specific tasks.

This hybrid approach keeps:

Costs predictable

Privacy intact

Capability available

The answer isn't "build OR buy." It's build + buy, with clear boundaries.

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Article 6 of 10 - AI Industry Series