AI Model
Connect Google Gemini
to your enterprise data
Give Google Gemini models governed access to your databases through auto‑generated REST APIs. DreamFactory keeps your data secure — Google Gemini queries through controlled API endpoints, never directly.
How DreamFactory connects Gemini to enterprise data
Google Gemini models — used through the Gemini API or Vertex AI — reach enterprise data through DreamFactory’s governed REST APIs and MCP tools rather than direct database connections. Function-calling and agent patterns get standardized, documented endpoints; your security team gets role-based access control and a complete audit log.
DreamFactory is self-hosted, so the data layer runs in your environment — including on-premises and air-gapped deployments — regardless of where the model runs.
Use Cases
Google Gemini Use Cases
AI Data Access
Secure Gemini Access to Enterprise Data
Learn more →
Legacy Modernization
Connect Gemini to Legacy Systems
Learn more →
Data Governance
Govern Gemini's Access to Your Data
Learn more →
Frequently asked questions
How do Gemini function calls reach our data?
Through auto-generated REST endpoints with OpenAPI documentation, or through MCP tools from the built-in MCP server — both governed by role-based access control.
Can we govern Gemini access separately from other apps?
Yes. Each application or agent gets its own role and API key, with per-role permissions, rate limits, and audit trails.
Where does the data layer run?
On your infrastructure — Linux, VMs, Docker, or Kubernetes, on-premises or private cloud — so enterprise data stays under your control.
Free 30-Minute Demo
Get a Demo — Google Gemini + DreamFactory
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