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CData Connect AI gives AI agents and assistants a managed way to query enterprise systems through a shared, governed data layer. It connects to live sources such as Salesforce, Snowflake, NetSuite, SAP and ServiceNow, then exposes their data through interfaces including MCP. CData says queries run against source systems rather than requiring it to copy or store the source data. Access controls, identity passthrough and audit features are intended to keep those connections within enterprise governance rules.

What is CData Connect AI?

CData Connect AI is a managed data connectivity platform for AI assistants and agents. It sits between an AI tool and business systems, translating requests into queries or actions that the connected sources can handle. CData positions it as both an MCP platform for agents and a broader enterprise data layer.

The same connections can serve different kinds of clients: MCP for agent workflows; ODBC, JDBC and a virtual SQL Server endpoint for business intelligence tools; and REST/OData for applications. CData says its platform supports hundreds of enterprise sources. Microsoft Marketplace describes 350+ sources in its listing, a figure that should be understood as that listing’s count rather than a guarantee that every connector supports identical operations.

How does it connect an AI agent to business data?

1. Connect to the source

Administrators configure a pre-built connector or an API connector for a SaaS application, database, API or on-premises system. Examples include Salesforce, Snowflake, NetSuite, SAP and ServiceNow. The connector provides a standardized relational interface, so an AI client does not need a separately designed integration for every source.

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2. Expose the connection through an interface

The AI client uses MCP to discover and call available tools. Other clients can use SQL-oriented interfaces such as ODBC or JDBC, the virtual SQL Server endpoint, or REST/OData. CData also describes a dedicated query engine that pushes joins, filters and aggregations to a source where possible.

3. Query the live source

CData says queries run against source systems rather than requiring its platform to copy or store the source data. This approach can keep results closer to the source’s current state, but it also means the source system, connector and network must be available when a query runs. CData’s published materials do not establish that every connector or operation is live in the same way, so teams should confirm query behavior for the systems and actions they plan to use.

4. Add business context and limits

Connector-specific guidance, metadata, dynamic schema discovery, semantic descriptions, derived views and curated data collections can help an agent understand what fields mean and which data it should use. Administrators can also create custom tools. CData advertises “documents as data” for retrieving and editing files and reports; confirm the supported file types and write operations for the intended workflow rather than assuming they are universal.

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How does CData handle permissions and security?

CData describes identity passthrough as a way for a query to run with the requesting user’s source-system permissions. Its use-case materials say queries can inherit user permissions at runtime through OAuth/SAML, helping avoid a separate authorization model and shared service-account sprawl. When configured and supported for a given connector, this can let existing source RBAC rules continue to govern what the user can access.

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That does not make a connection secure by default. The enterprise still needs to configure identities, scopes, roles and tools appropriately, and verify how each connector enforces permissions. CData also lists role- and attribute-based access controls, least-privilege scoping, toolkits, query-level audit logs and observability for agent-to-data activity.

CData’s pricing page lists enterprise SSO, SCIM 2.0, passthrough identity, audit logging, RBAC/ABAC, AES-256 encryption at rest and TLS 1.3 in transit. It also lists SOC 2 Type II, ISO/IEC 27001:2022, GDPR and HIPAA-ready support. These are vendor-stated capabilities and compliance claims; procurement teams should request current attestations, scope documents and details of which services and data flows each claim covers.

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Which AI tools and deployment options are supported?

CData announced on November 18, 2025 that Connect AI MCP connectivity was available directly in Microsoft Copilot Studio and Microsoft Agent 365. CData’s current pages also show compatibility with Claude, ChatGPT, Google, Databricks, Palantir and other MCP-enabled AI tools. Availability and configuration can vary by product and account, so confirm the integration path with CData and the relevant AI provider before planning a rollout.

For developers, CData describes a hosted Connect AI service as well as self-hosted drivers for ODBC, JDBC, ADO.NET, Python and SQL Server interfaces. The choice is between using the managed MCP service and integrating through developer-facing interfaces; CData’s published materials do not specify a complete deployment architecture or feature parity for every option.

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What does CData Connect AI cost?

CData’s pricing page lists the following plans. Prices and included features can change, so check the current offer and contract terms before purchase.

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Standard $99/month, or $79/month when billed annually One user and one data source included
Growth $199/month, or $159/month when billed annually Intended for multiple sources; the precise included source count is not stated in the pricing summary
Business Annual contract; custom pricing Custom source counts, pooled tool calls, passthrough identity, SCIM, SSO, premium support and custom tools

CData’s Developer Center advertises a free Developer Edition and a five-minute quickstart. Treat the quickstart duration as CData’s advertised claim, not a guarantee that a production connection will be configured in five minutes.

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What should teams verify before choosing it?

A managed connectivity layer can reduce the work of building and maintaining separate integrations, but it does not remove the need to test the data path. Before deploying an agent, confirm the details that determine whether it will be useful and governable in your environment:

  • Whether each required connector supports the needed queries, joins, filters, edits or other actions, and whether requests execute live against the source.
  • How user identity is passed through, which OAuth or SAML flows are supported, and whether source permissions are enforced for each planned operation.
  • What metadata, semantic descriptions, derived views and custom tools are needed to keep the agent’s queries relevant and appropriately scoped.
  • What is recorded in audit logs, how agent activity can be monitored, and how roles, attributes and least-privilege rules are administered.
  • Which deployment model, client interfaces and contractual plan fit the required sources, users and tool-call volumes.
  • Current compliance attestations and their scope, especially for regulated data and the particular service components involved.

When comparing CData with direct integrations or another MCP gateway, use the same criteria for each option: connector breadth, live versus replicated data, read/write support, semantic context, identity passthrough, auditing, observability, deployment model, protocol coverage, query and token efficiency, pricing dimensions and compliance evidence.

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How should CData’s performance and scale claims be read?

CData Software’s current homepage claims 98.5% answer accuracy when connected to CData and says that is 25% higher than other MCP providers. These are vendor-reported comparisons, not independently verified benchmark results in CData’s published materials. CData’s Developer Center also presents a figure of 378 real-world prompts alongside accuracy and token-reduction marketing metrics, without a methodology in its published materials. CData’s homepage claims more than 10,000 customers worldwide; that is likewise a company-published figure.

CData describes its product this way: “CData is the data layer that makes AI work in production—live connectivity and replication across hundreds of the most critical enterprise sources, semantic context, and built-in governance.” The statement is from CData Software. The figures and product claims are useful context, but teams evaluating a deployment should test their own connectors, permissions, prompts and workloads.

Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.