Building custom and self-hosted AI models
A custom model is not always the answer — but when privacy, cost or a niche task demand it, an off-the-shelf API won’t cut it. I have built custom and self-hosted models where it genuinely mattered, and I’ll tell you honestly when it doesn’t.
Self-hosted when data can’t leave
For sensitive domains I run open models — Llama, Mistral, DeepSeek — on Ollama, so data never leaves your infrastructure. On Ealthiness I built a custom clinical model on Ollama with a Weaviate vector database; on Sumus, a custom product-scoring engine on OpenAI plus custom Ollama models. Self-hosting also caps and predicts inference cost at scale.
Fine-tune, ground, or route — the honest call
Most "we need a custom model" problems are actually solved better by RAG (grounding a strong base model in your data) or prompt/tool design than by fine-tuning. I’ll pick the cheapest thing that hits your quality bar — fine-tuning only when the task, tone or format genuinely can’t be reached otherwise — and often route across several models per job.
Evaluated, not vibes
A model you can’t measure is a liability. I stand up evaluation sets and guardrails so you know accuracy before launch and catch regressions after, and design fallbacks (e.g. Gemini/OpenAI behind a self-hosted primary) so the feature stays available when one provider fails.
- Self-hosted Llama/Mistral/DeepSeek on Ollama for privacy and cost control.
- RAG or prompt design first; fine-tuning only when truly needed.
- Evaluation sets, guardrails and provider fallbacks before launch.