CData Connect AI - 2026 Pricing, Features, Reviews & Alternatives

CData Connect AI is the AI gateway that makes AI more accurate, for less. Built on CData's enterprise MCP platform, it gives every agent, copilot, and model one governed path to hundreds of enterprise systems, including Salesforce, NetSuite, SAP, ServiceNow, Workday, Snowflake, and Databricks, over live data instead of stale copies.

Unlike gateways that only proxy traffic to model providers, Connect AI starts at the source connection. Its Context Engine carries your schemas, metric definitions, business rules, and company knowledge with every request, so answers are grounded in how your business actually works, and a self-learning loop turns every correction and successful query into shared context that every agent on every model inherits. Identity is verified on every call, policy is enforced down to the record, and joins, filters, and calculations run in the platform so the model reads only what it needs.

Increase accuracy

Context Engine: A unified context graph of system context (schemas, objects, and relationships known on day one), curated data models, semantic metric definitions, and company knowledge, retrievable by any model over MCP. Every answer applies the same definitions, no matter which tool or model asks.

Self-learning loop: Every user correction, recurring join or filter, successful tool-call pattern, and definition surfaced from wikis, Slack, or Teams becomes a governed proposal. Once an owner approves it, every agent on every model inherits it, so accuracy compounds with use instead of decaying between manual updates.

Virtual Data Layer: Live, modeled access across sources, so agents answer from current records rather than stale copies, with cross-system joins and derived views that shape data for each use case.

Reduce cost

Model Gateway: Route every request to the lowest-cost model that fits the task across Claude, GPT, Gemini, Llama, Mistral, DeepSeek, Qwen, xAI, and local deployments, with automatic failover, per-team and per-agent budgets, and caching.

Fewer tokens per request: Joins, filters, and calculations run in the platform before data reaches the model, and context retrieval returns only the matching concept and its links. In benchmark conditions, a multi-source query dropped from 22 tool calls to one and from 183,541 tokens to 4,427.

Lean toolsets: The MCP Gateway exposes only the tools each agent needs, cutting retries and extra turns from agents picking the wrong tool.

Govern access

MCP Gateway: One governed connection layer to hundreds of enterprise systems through CData-owned, custom, or third-party MCP servers, with every agent registered on a single control plane.

Identity and record-level policy: User and agent identity resolved on every request, with policy enforced in each connected system down to the record, not just at the API edge.

Observability and audit: Guardrails, budgets, and usage visible across every team and tool, with every answer linked to one audit trail. The context graph lives in the gateway, not in any model, so switching providers never leaks or loses accumulated knowledge.

Proven results

In CData Labs benchmarks, Connect AI answered 98.5% of queries correctly versus 65–75% for other MCP providers, and delivered the same correct answer at up to a 178× cost spread across models. Works with Claude Code, Copilot, Cursor, LangChain, CrewAI, Codex, Bedrock, and any custom agent. Trusted by GSK, Salesforce, Palantir, Google, and 10,000+ CData customers.