Custom AI vs. SaaS AI
Custom AI and SaaS AI represent two ends of a build-versus-buy spectrum. SaaS AI is a vendor's model behind a vendor's interface—ChatGPT Enterprise, Jasper, Writer—where you get speed to deployment but inherit someone else's architecture, limitations, and roadmap. Custom AI is a system built for your specific data, workflows, and success criteria, typically combining foundation models with proprietary retrieval layers, custom evaluation pipelines, and interfaces designed for your users. The tradeoff is real: SaaS AI ships in days, custom AI ships in weeks or months. But SaaS AI gives every competitor the same capability, while custom AI can become a durable advantage. The right answer depends on whether the AI capability you need is a commodity or a differentiator. Email summarization is a commodity—buy it. A system that underwrites insurance risk using your proprietary claims data is a differentiator—build it.
Related terms:
Fine-Tuning
Fine-tuning continues training a pretrained language model on a smaller, task-specific dataset so it internalizes particular behaviors, styles, or domain knowledge. While it yields more consistent formatting and terminology than prompting alone, it requires curated data, additional training time, and can lead to loss of general capabilities.
Gravity Wells
Gravity wells describe economic dynamics where scarce resources flow disproportionately to entities with the greatest ability to pay and deploy, creating self-reinforcing concentrations of power. In the AI economy, they form around critical bottlenecks in compute, power, and talent, determining who captures resources and who scrambles for scraps.
Temperature
Temperature is a parameter controlling a language model’s randomness: at 0 it always picks the most probable next token for deterministic, reliable output, at 1 it samples more broadly for varied, creative results, and above 1 it becomes increasingly random. Choosing the right temperature (e.g., 0 for consistent data extraction or 0.7–0.9 for brainstorming) balances reliability and diversity.