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AI DevelopmentJune 13, 2026·5 min read
When Not to Build a Custom LLM Stack
Most products should orchestrate existing models, not train their own. Here's how we decide.
Vikram Singh Rathore
Founder & Principal Engineer
Fine-tuning and custom training make sense when you have proprietary data at scale and a narrow task. They rarely make sense for v1 MVPs.
Use orchestration when
- You're proving product-market fit
- Your differentiation is workflow, not model weights
- You need to swap providers as pricing shifts
We default to OpenAI, Groq, or Gemini behind a provider interface — the same pattern in Nexus AI and Jobsflix. Custom training is a phase-two conversation with metrics, not a slide-deck promise.
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