Overview

Deployment Models Overview

When building a data or AI product on Databricks, one of the first architectural decisions is selecting the right deployment model. This choice determines where infrastructure lives, who manages it, and how your customers interact with the platform.

Deployment Models

SaaS

In the SaaS model, you (the partner) own and operate the entire Databricks environment—workspaces, compute, storage, and data pipelines. Your customers access your product as a service without needing their own Databricks account.

Common use cases:

• Embedded analytics and dashboards
• Data products and marketplaces
• AI/ML-powered applications
• Customer-facing reporting platforms

Key characteristics:

• You control all infrastructure and operations
• Customers consume the service, not the platform
• You are responsible for multi-tenancy, security, and cost management

Managed Hosted

In the Managed Hosted model, the partner owns and manages the Databricks control plane, but the data plane runs in the customer’s cloud account. This approach is ideal for customers who require their data to remain in their own environment while still benefiting from partner-managed operations.

This model follows the Customer-Managed VPC pattern in Databricks documentation.

Common use cases:

• Enterprise customers with strict data residency requirements
• Regulated industries requiring data sovereignty
• Customers wanting managed services without data leaving their environment

Key characteristics:

• Partner manages the workspace and operations
• Customer’s data stays in their cloud account
• Combines operational simplicity with data control

Sidecar / Embedded

In the Sidecar model, partners operate their own SaaS offering that makes API or SDK calls into the customer’s Databricks tenant. The partner’s application orchestrates workloads—running jobs, executing queries, or building tables—within the customer’s environment.

Common use cases:

• Data integration and ETL tools
• BI and analytics platforms connecting to customer data
• AI/ML platforms that train models on customer data
• Data quality and observability tools

Key characteristics:

• Customer owns and manages their Databricks workspace
• Partner application connects via APIs/SDKs
• Data processing occurs in the customer’s environment

Customer Managed

In the Customer Managed model, the customer owns and operates everything—their Databricks workspace, infrastructure, and data. The partner provides software, guidance, or templates that the customer deploys and manages themselves.

Common use cases:

• Customers with mature platform teams
• Highly regulated environments with strict operational controls
• Organizations requiring full autonomy over their data platform

Key characteristics:

• Customer has full control and responsibility
• Partner provides enablement rather than operations
• Maximum flexibility but requires customer expertise

Comparison

Factor	SaaS	Managed Hosted	Sidecar	Customer Managed
Infrastructure ownership	Partner	Partner (control plane) / Customer (data plane)	Customer	Customer
Data location	Partner’s environment	Customer’s environment	Customer’s environment	Customer’s environment
Operational responsibility	Partner	Partner	Shared	Customer
Customer Databricks account required	No	No	Yes	Yes
Data residency control	Low	High	High	High
Partner control over platform	High	Medium	Low	None
Implementation complexity	Lower	Medium	Medium	Higher
Customer onboarding effort	Minimal	Moderate	Moderate	Significant

Choosing a Model

When selecting a deployment model, consider:

1. Data residency requirements: Does your customer’s data need to stay in their environment?
2. Operational capacity: Do you want to manage infrastructure, or does the customer prefer to?
3. Customer sophistication: Does the customer have a platform team capable of managing Databricks?
4. Regulatory constraints: Are there compliance requirements dictating where data can reside?
5. Scale and economics: Which model provides the best unit economics for your business?

Many partners offer multiple deployment models to serve different customer segments—SaaS for smaller customers seeking simplicity, and Managed Hosted or Sidecar for enterprise customers with specific requirements.