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 Temporal
Pricing, pros, cons, and ideal use cases — side by side.
TemporalFreemium
A durable execution platform that makes long-running, failure-prone workflows — including AI agents — reliable by default.
At a glance
| Databricks Mosaic AI | Temporal | |
|---|---|---|
| Pricing | EnterpriseConsumption-based, billed within the Databricks platform; enterprise agreements typical. | FreemiumOpen-source and self-hostable for free. Temporal Cloud is a usage-based managed service. |
| 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 agents in productionPlatform teams building reliable workflow infrastructureEnterprises with long-running, multi-step AI processes |
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
Temporal
Pros
- Makes long-running agent workflows crash-proof
- Automatic retries and full execution history
- Battle-tested well beyond AI use cases
- Self-host or managed Temporal Cloud
Cons
- Significant learning curve and architectural commitment
- Not AI-specific — you build the agent layer yourself
- Self-hosting the cluster is non-trivial
Which should you choose?
Temporal is the lighter-weight option (Freemium), while Databricks Mosaic AI sits higher on the pricing ladder (Enterprise). Databricks Mosaic AI is built around enterprises building ai on the databricks lakehouse; Temporal leans more toward teams running agents in production. Shortlist the one whose strengths line up with your biggest constraint.
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