Automatic

Choosing between a public or private LLM isn't just a technical call — it's a strategic one with major implications for security, cost, and competitive advantage. This episode breaks down exactly what CTOs need to weigh before committing.

Show Notes

For CTOs navigating today's AI landscape, few decisions carry more long-term weight than choosing between public and private large language models. This episode of Automatic unpacks that choice in full — moving beyond the hype to examine the real trade-offs across data governance, product performance, and total cost of ownership. The analysis draws on the full private vs. public LLMs breakdown to give technical leaders a practical framework they can actually act on.

Here's what the episode covers:

  • Public LLMs: the appeal and the catch. Vendor-hosted models like GPT-4, Gemini, and Claude offer instant capability and zero infrastructure overhead — but your data transits someone else's servers, and fine-tuning options can be limited or non-compliant in regulated environments.
  • Private deployments: control at a cost. Hosting open-weights models like Llama 3 or Mistral on your own infrastructure gives you full data sovereignty, custom fine-tuning, and auditable access logs — but demands serious investment in GPU compute, ML talent, and ongoing maintenance.
  • The three axes that actually drive the decision: data governance and regulatory exposure, product fit and performance requirements, and a 24-month total cost of ownership model that accounts for far more than token pricing.
  • The break-even reality. Research suggests the economics typically flip somewhere between 40 and 150 million tokens per month — below that threshold, public APIs usually win; above it, private infrastructure starts making balance-sheet sense.
  • The hybrid playbook. A growing number of sophisticated organizations are routing low-sensitivity tasks through public APIs while keeping proprietary and regulated data on private models — with orchestration tools handling automatic classification and routing.
  • Edge and on-device models as a third path. Small-parameter models running locally are unlocking new options for mobile and field applications where cloud latency is a constraint and privacy requirements are strict.

The core argument: this isn't a binary choice but a portfolio decision. The most effective approach blends public APIs for general-purpose work, private models for sensitive and proprietary data, and edge deployments for offline resilience — with governance and observability baked in from day one, not retrofitted later. If you enjoyed this episode, check out Real-Time Joins: Making SQL Cry for another deep dive into the infrastructure decisions that quietly define how modern systems perform.

LLM

What is Automatic?

Agentic AI and automation from the perspective of whoever has to maintain it in six months. Where an agent genuinely belongs in a process, where a plain script is enough, how to design a handoff to a human, and what breaks quietly at scale.

Each episode takes one automation decision and reasons it through end to end — including the maintenance burden, the failure modes and the honest question of whether the process should exist at all. Written for operators and technical leads, deliberately free of hype. Five or six minutes an episode.

Topics include where an agent belongs versus a plain script, designing human handoffs, error handling and observability, maintenance burden, process mapping before automation, measuring what a workflow saves, and knowing when a process should be deleted instead.

Produced by Automatic.co, agentic AI and automation consulting. Full details, services and further reading at https://automatic.co