External AI & Bring Your Own LLM

Salesforce's native Models API isn't the only way to put an LLM behind your org — sometimes contracts, regions or a fine-tuned model of your own demand an external path. This track maps the options and builds each one hands-on: the decision, BYO LLM through Einstein Studio, production-grade raw callouts, do-it-yourself grounding and the security layer that makes it defensible.

The series

Five parts, one decision at the top

Part 1 decides which path you're on; Parts 2 and 3 build the two external paths; Parts 4 and 5 are the grounding and hardening work every external path needs. Each part stands alone, anchored in a real company scenario.

The map

Three paths at a glance

The full decision table lives in Part 1 — this is the short version you'll quote in the architecture meeting.

PathModel choiceEinstein Trust LayerYou buildReach for it when
Native Models API Salesforce-managed model list Built in Almost nothing — a few lines of Apex Any current frontier model will do. Start here.
BYO LLM (Einstein Studio) Your deployment on Azure OpenAI, OpenAI, Bedrock or Vertex AI Built in Provider setup + one registration Your contract, region or fine-tuned model must govern — on a supported provider.
Direct Apex callout Anything with an HTTP API, incl. self-hosted None — you are the trust layer Credentials, client, retries, masking, audit, cost controls No platform path reaches the model, and you'll fund the hardening work.

Keep going

Related building blocks

The native side of this story — the first-party path this track keeps comparing against, and the platform's managed grounding.