Databricks Mosaic AIEnterprise
Databricks' suite for building, governing, and serving production AI — agents, RAG, fine-tuning, and evaluation — on top of your governed data.
AI Infrastructure comparison
Databricks Mosaic AI vs Together AI
Pricing, pros, cons, and ideal use cases — side by side.
Together AIPaid
A cloud platform for fast, cost-efficient inference and fine-tuning of open-weight models at production scale.
At a glance
| Databricks Mosaic AI | Together AI | |
|---|---|---|
| Pricing | EnterpriseConsumption-based, billed within the Databricks platform; enterprise agreements typical. | PaidUsage-based per-token pricing. Dedicated endpoints and fine-tuning are priced separately. |
| Category | AI Infrastructure | AI Infrastructure |
| Ideal for | Enterprises building AI on the Databricks lakehouseData teams that want AI governed alongside dataOrganizations unifying data and AI on one platform | Teams running open-weight models in productionCost-sensitive, high-volume inference workloadsEnterprises fine-tuning private model variants |
Pros & cons
Databricks Mosaic AI
Pros
- AI built directly on governed lakehouse data
- Unity Catalog lineage and access control extend to AI
- Covers agents, RAG, fine-tuning, serving, and evals
- Strong fit for data-mature enterprises
Cons
- Most valuable only on the Databricks platform
- Platform commitment, not a point solution
- Enterprise pricing and procurement
Together AI
Pros
- Fast, cost-efficient open-model inference
- Wide and current model selection
- Fine-tuning and dedicated endpoints available
- Often cheaper than closed APIs at scale
Cons
- Open models shift evaluation and safety onto you
- Quality varies by model and task
- Not a managed governance platform on its own
Which should you choose?
Together AI is the lighter-weight option (Paid), while Databricks Mosaic AI sits higher on the pricing ladder (Enterprise). Databricks Mosaic AI is built around enterprises building ai on the databricks lakehouse; Together AI leans more toward teams running open-weight models in production. Shortlist the one whose strengths line up with your biggest constraint.