Embed

CData Embed is the embedded data connectivity layer from CData Software, designed for software vendors and AI platform builders who need to give their AI agents and copilots live, governed access to customer business systems without building brittle custom integrations. It targets product teams shipping AI features that must query, reason, and act on real-time data across hundreds of sources — from Salesforce and Jira to SAP and Snowflake — while inheriting each customer's native permissions and authentication. CData Embed is the OEM-friendly version of CData Connect AI, packaged so that ISVs can embed it directly into their own products, enabling multi-tenant, secure data access without replication or data movement.

CData Embed connects to over 350 business systems via live, read-write access, using a scoped Model Context Protocol (MCP) architecture that precisely controls what each agent can see and do. It offers three tool types: Universal Tools provide a normalized, schema-aware interface across all connected systems; Source Tools expose system-specific operations for predictable, auditable execution; and Custom Tools let organizations define pre-optimized queries with explicit data access limits to reduce token usage and prevent unintended exposure. The platform supports multi-step, cross-system workflows on frameworks like LangChain, LangGraph, and Crew AI, and can be deployed in minutes via point-and-click configuration. In benchmarks, CData Connect AI achieved 98.5% accuracy across 378 real-world prompts, outperforming competing MCP providers by over 25 percentage points.

CData Embed competes most directly with Snowflake (which offers native AI connectivity but requires data to be in Snowflake), Zapier (which provides broad app integration but lacks enterprise governance and semantic context), and SAP HANA Cloud (which excels in SAP ecosystems but is limited to SAP data). CData's advantage is its breadth — 350+ sources — combined with live, in-place access that preserves source-native permissions, semantic relationships, and real-time data fidelity without replication. However, it is not a data warehouse or analytics platform; it is purely a connectivity and governance layer for AI agents.

The honest trade-offs: First, CData Embed is a proprietary platform — you are locked into CData's connectivity infrastructure, which may be overkill if you only need a handful of sources. Second, while it reduces integration time, it does not eliminate the need for data modeling or schema design; you still need to define Workspaces and Toolkits. Third, the 98.5% accuracy benchmark is impressive but was measured on a specific set of 378 prompts; real-world performance will vary by data source and query complexity. Fourth, pricing is not publicly disclosed, which makes budgeting difficult for small teams and may favor enterprise deals. Fifth, CData Embed is designed for software vendors embedding it into their products — it is not a self-service tool for end-user analysts or data scientists.

Not publicly disclosed; pricing is likely enterprise-based and negotiated per deployment (self-reported as not stated in sources).