Table of Contents
Quick Answer
Databricks' AI platform is anchored by Mosaic AI (the rebrand of MosaicML after acquisition), supporting the full lifecycle — data prep, model training/fine-tuning, serving, governance via Unity Catalog, and agentic apps via the Mosaic AI Agent Framework. Flagship open model: DBRX (132B MoE). AI/BI Genie brings natural-language BI; Databricks Assistant writes SQL and Python. Pricing is DBU-based (Databricks Units) layered on cloud compute.
- Core: Mosaic AI (training + serving + Agent Framework)
- Open model: DBRX
- Governance: Unity Catalog for AI assets
- Price: Consumption via DBUs
What Is Databricks AI (Mosaic AI)?
Mosaic AI is Databricks' end-to-end generative AI platform on top of the lakehouse. Data teams can fine-tune open models on their own data (without it ever leaving the lakehouse), deploy serving endpoints, build RAG apps with Vector Search, and compose multi-step agents with the Agent Framework. Unity Catalog extends governance to AI — models, prompts, features all cataloged and permissioned.
Why Enterprises Are Using Databricks AI in 2026
Gartner's 2026 Magic Quadrant for DSML placed Databricks as a Leader, specifically citing the lakehouse-native approach. Forrester's 2026 Wave on AI/ML Platforms named Databricks a Leader with top scores for enterprise scale. IDC's 2026 AI Platforms forecast has Databricks at roughly 18% market share of AI-in-data-platform spend.
Databricks' own 2026 numbers: 60%+ of enterprise lakehouse customers are running at least one GenAI workload on Databricks.
Top Use Cases and Features
- Mosaic AI Model Serving — one-click deploy
- Fine-tuning on open models (Llama, DBRX, Mistral) on your data
- Vector Search — managed embeddings + vector DB
- Mosaic AI Agent Framework — multi-step agent orchestration
- Agent Evaluation — automated agent quality scoring
- AI/BI Genie — NL queries against lakehouse
- Databricks Assistant — SQL and Python copilot
- Model Governance in Unity Catalog
- Lakehouse Monitoring for AI
- AI Gateway — multi-LLM routing and governance
Step-by-Step: Getting Started
- Sign into your Databricks workspace (AWS, Azure, or GCP)
- Enable Mosaic AI features in workspace admin
- Try Databricks Assistant on an existing notebook (sparkle icon)
- Index a dataset with Vector Search
- Build a RAG app using the Agent Framework starter template
- Use Agent Evaluation to track quality before production
Pricing Breakdown (2026)
| Component | Pricing |
|---|---|
| Databricks Units (DBUs) | Workload-type dependent |
| SQL Serverless | ~$0.70/DBU |
| All-Purpose Compute | ~$0.55/DBU |
| Model Serving (dedicated) | DBU + GPU time |
| Vector Search | DBU per endpoint |
| Fine-tuning | DBU per training hour |
| AI/BI Genie | DBU per query |
| Mosaic AI Agent Framework | DBU for orchestration |
DBUs include underlying cloud compute on serverless SKUs. Non-serverless adds cloud VM cost.
Conclusion
Databricks AI in 2026 is the most flexible enterprise AI platform for teams that want to train, fine-tune, and serve their own models on their own data. Start with Vector Search + Agent Framework for RAG, then expand to fine-tuning as value is proven.
More enterprise AI at misar.blog.