It’s 2026 and your team has been told to ship a custom LLM on Databricks — but the reality is a maze: DBUs draining faster than anyone budgeted, a fine-tuned model that works in a notebook yet stalls under provisioned throughput, a RAG prototype that hallucinates in front of stakeholders, and a governance team asking who touched which model and why. The docs cover each Mosaic AI service in isolation, but nobody hands you the connective tissue: how training, serving, Vector Search, agents, and Unity Catalog actually fit into one production system that finance and compliance will both sign off on.
This is for developers and ML engineers who already live in the Databricks Lakehouse and can read Python, SQL, and a REST payload without hand-holding. We assume you understand what an embedding and a token are and have deployed *something* before. Out of scope: teaching you Spark from zero, general prompt-engineering theory, or non-Databricks stacks — this is specifically about building, serving, and governing custom LLMs on Mosaic AI.
Straight talk on the AI itself: Mosaic AI is strong at managed fine-tuning, provisioned-throughput serving, and lineage you can audit — and genuinely weak at silently making cost, latency, and eval trade-offs for you. Retrieval quality, agent tool-use, and “is this answer safe to ship” are exactly where human review is non-negotiable. We flag every place you must test and judge for yourself instead of trusting a green checkmark.
What This Guide Covers
- Why the Lakehouse for custom LLMs — where Mosaic AI beats bolt-on API stacks in 2026, and where it doesn’t
- The full architecture mapped end to end, so you know which service owns which job before you write code
- Workspace and DBU setup that gets you to a first live endpoint without burning budget on false starts
- Fine-tuning and pretraining strategy — when to adapt a base model versus train, and how to scope each
- Model serving and provisioned throughput tuned for real traffic instead of demo-day loads
- Retrieval apps on Vector Search — how to structure indexes so RAG answers stay grounded
- Agent orchestration with the Mosaic AI Agent Framework, including tool and multi-step patterns
- Agent Evaluation — building quality gates and judges you actually trust before release
- Governance and lineage in Unity Catalog so every model, dataset, and change is accountable
- Real DBU and cost math, including honest comparisons against OpenAI-style API pricing
- Model options — DBRX, Llama, and bring-your-own-weights, and how to choose among them
- Deployment patterns and the pitfalls that quietly break LLM systems in production
- Production case studies across RAG, agents, and fine-tuning you can pattern-match to your own work
- Roadmap and migration guidance — when Mosaic AI is the right call and when to walk away
Delivered as instant online access the moment you check out. No upsell, no drip-feed, no waiting — the complete guide is yours to read immediately.
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