LLM.co

Private and custom large language models — the build, the boundaries and the bill. Fine-tuning versus retrieval, running models in your own environment, evaluation you can actually trust, data governance, and the questions to ask before a vendor answers them for you. Each episode takes one decision a team is facing — whether your problem needs a custom model at all, how to evaluate output without fooling yourself, what "private" has to mean contractually — and works it through concretely. Written for engineering and data leaders putting a model into production. Five or six minutes, one idea, no demos. Topics include fine-tuning versus retrieval, self-hosted and private deployment, evaluation you can trust, prompt and context design, data governance and retention, cost and latency tradeoffs, and what "private" has to mean contractually. Produced by LLM.co, private and custom large language models. Full details, services and further reading at https://llm.co

LLM.co is a business podcast hosted by Unknown Host, with 0 episodes on record and a Required Pod Score of 80. PitchCentric scores this show on Booking Probability, Listen Score, and live audience signals refreshed every 24 hours.

Unknown Host hosts LLM.co, a business show with 0 episodes published.

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  LLM.co

  Why AI Projects Fail Organizationally Before They Fail Technically

  Sep 11, 20265 minS0

  <p>Technical performance is rarely what kills an AI initiative. This episode of LLM.co examines the organizational and structural breakdowns that derail AI projects long before a model ever has a chance to prove itself — drawing on the full analysis of why AI projects fail organizationally before they fail technically. From how goals get defined to how tools get adopted, the conversation surfaces the patterns that repeat across teams and industries.</p> <p>The episode walks through the most common organizational failure modes, covering:</p> Vague goals that invite misalignment — when a project's objective is broad enough for every stak...

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  LLM.co

  CAPEX vs OPEX in Open Source AI: Why the Hybrid Wins

  Sep 9, 20265 minS0

  <p>When an AI project moves from demo to daily infrastructure, the conversation shifts fast — from model benchmarks to balance sheets. This episode of LLM.co tackles one of the most consequential decisions in any open source AI deployment: how you pay for it. Drawing on the in-depth guide to CAPEX vs. OPEX in open source AI, the episode cuts through the accounting jargon and explains how the choice between owning infrastructure and renting it shapes not just your first invoice, but your team structure, data control, and total costs for years ahead.</p> <p>Here's what this episode covers:</p>...

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