CData Software has released three free products designed to let developers build AI applications on enterprise data without sacrificing governance or security. The company launched Connect AI Developer Edition, a free version of its enterprise platform; the CData Connect AI Python SDK, available as open source; and CData CLI, a command-line interface for developers. These tools address a persistent tension in modern software development: the need to move fast while meeting the security and compliance requirements that IT departments demand.

Why Are Developers Struggling With Enterprise Data Access?

For years, developers building AI applications have faced a frustrating choice. They could move quickly by accessing enterprise data directly, but risk bypassing the governance controls that protect sensitive information. Or they could follow proper security channels, which often meant waiting for IT approval, navigating complex authentication systems, and dealing with rate limits and API versioning issues that slow development cycles. This friction has become especially acute as teams rush to build AI agents and retrieval-augmented generation (RAG) systems, which require clean, reliable access to company data like customer records, financial information, and operational systems.

"Developers have been forced to choose between moving fast and meeting the governance their company requires. That tradeoff doesn't hold up anymore," said Raviv Levi, Chief Product and Technology Officer at CData.

Raviv Levi, Chief Product and Technology Officer at CData

The new tools aim to eliminate that choice entirely. Instead of developers asking IT for permission each time they need data, Connect AI acts as a governed intermediary layer. IT deploys the platform once, sets up access policies, and developers can then query enterprise systems directly through familiar interfaces. Business teams get the AI workflows they need, developers get a stable data interface, and IT gets visibility and control over every query.

What Makes These Tools Different From Existing Solutions?

The three products work together to cover different developer workflows and preferences. Connect AI Developer Edition is the foundation, exposing enterprise APIs as a consistent, queryable data layer with standardized schema, read and write support, and automatic handling of authentication, rate limits, versioning, and pagination. Developers write queries; the platform handles the rest. The free version includes the full enterprise feature set, including support for Model Context Protocol (MCP), a standard that lets AI coding assistants like Claude Code, Codex, and Cursor integrate directly with data sources.

The Python SDK provides database-compatible access to Connect AI, allowing developers to pull governed enterprise data into existing Python workflows without changing how they write code. It works with pandas, SQLAlchemy, cursor-based queries, and any other database-compatible tool that Python developers already use daily. The CData CLI is designed for developers who prefer command-line interfaces and want to speed up development and testing for analytics pipelines, business intelligence integrations, and ETL (extract, transform, load) workflows. The CLI is built to work with AI coding assistants, which can use it directly to scaffold connectivity without forcing developers to dig through documentation.

How to Integrate Enterprise Data Into Your AI Development Workflow

• Use Connect AI Developer Edition: Deploy the free platform once to expose enterprise systems like Salesforce, Snowflake, NetSuite, Microsoft 365, and Workday as queryable data layers with built-in governance and authentication handling.
• Leverage the Python SDK: Pull governed enterprise data directly into Python environments using familiar database-compatible interfaces, without rewriting existing code or bypassing security controls.
• Adopt Toolkits for scoped access: Package governed data access into single MCP Server URLs scoped to specific use cases, so AI agents get exactly what they need and nothing more, reducing security surface area.
• Integrate with MCP-capable assistants: Connect your development environment to AI coding assistants like Claude Code or Cursor, which can use the CLI and MCP servers to scaffold connectivity and reduce manual setup overhead.

One particularly useful feature is Toolkits, which let teams package governed data access into a single MCP Server URL scoped to specific use cases. This means AI agents get exactly what they need and nothing more, reducing the security surface area and making it easier to audit what data each agent can access. The approach is especially valuable for organizations deploying multiple AI agents across different departments, where fine-grained access control is essential.

The free Developer Edition is designed to work out of the box with any MCP-capable coding assistant, client, or framework. This is significant because MCP is becoming a standard way for AI tools to integrate with external systems and data sources. By supporting MCP natively, CData is positioning its tools to work seamlessly with the broader AI development ecosystem, rather than requiring developers to learn proprietary APIs or custom integrations.

What Does This Mean for Enterprise AI Development?

The release reflects a broader shift in how enterprises are approaching AI development. Rather than treating AI as a separate, isolated workload, companies are increasingly building AI capabilities directly into their existing software and data infrastructure. This requires tools that can bridge the gap between developer velocity and enterprise governance, which is exactly what CData is attempting to do.

The free pricing model is also noteworthy. By offering the full enterprise feature set at no cost, CData is lowering the barrier to entry for teams experimenting with AI-powered data access. This could accelerate adoption of governed data layers across organizations of all sizes, from startups building their first AI agent to large enterprises modernizing legacy data access patterns. The open-source Python SDK further signals that CData is betting on developer adoption and community contribution, rather than trying to lock teams into proprietary tooling.

For IT teams, the appeal is clear: these tools provide visibility and control over data access without requiring developers to ask for permission every time they need to query a system. For developers, the appeal is equally straightforward: they can access the data they need without navigating complex approval workflows or dealing with authentication and rate-limiting issues. The tools essentially automate the governance layer, making it invisible to developers while keeping IT in control.