Build vs. Buy in AI: Your Own Model or an API?

Mon, 01 Jan 0001 00:00:00 +0000

info Quick Definition

Build vs. buy in AI is the decision to use a model through a third-party API (buy) or to host and operate an open model on your own or rented infrastructure (build). The right answer depends on token volume, the data the product handles, and the team’s technical capacity, not on which option sounds more serious.

What do build and buy mean?

With “buy” you pay per use to a provider that maintains the model, the infrastructure and the upgrades. Your cost grows with every call. With “build” you host an open model, serve it with an inference engine, and pay for GPUs, people and operations. There is also a middle option: running an open model through a hosted inference provider that charges per token and runs the infrastructure for you. Many comparisons skip it. To understand how closed and open models differ, read [[open-vs-closed-models|open vs. closed models]].

RAG (Retrieval-Augmented Generation): Answers Grounded in Your Documents

Mon, 01 Jan 0001 00:00:00 +0000

info Quick Definition

RAG (Retrieval-Augmented Generation) is a technique in which the system searches a collection of documents for relevant fragments and includes them in the request to the model, so the answer is grounded in those texts rather than only in what the model learned during training.

What is RAG?

RAG combines two parts. A retriever searches your document base for the fragments most related to the question, usually by comparing [[embeddings|embeddings]]. A generator, the [[what-is-an-llm|LLM]], writes the answer using the question and those fragments as context.

Architecture on Fernando Ruiz

Build vs. Buy in AI: Your Own Model or an API?

What do build and buy mean?

RAG (Retrieval-Augmented Generation): Answers Grounded in Your Documents

What is RAG?