iDBQuery vs ChatGPT Enterprise for data analysis: what's the difference?

Both let you ask questions in natural language, but they work on very different inputs. ChatGPT Enterprise is a general assistant: to analyse company data you upload a file into the conversation, and it reasons over that snapshot. iDBQuery is built to sit on top of your actual data estate.

• Live sources, not uploads. iDBQuery connects to MySQL, PostgreSQL, MongoDB, Excel, CSV, Google Sheets, PDFs, REST APIs and SQL warehouses, and builds one live queryable model across them. You never export a spreadsheet to ask a question.
• Cited answers. Every number traces back to the exact source row, so you can verify a figure instead of trusting a summary.
• Query-in-place and deploy anywhere. Run it in the cloud, your own VPC, or fully air-gapped, with a Desktop app and Embedded SDK that keep data local — useful when the data is too sensitive to paste into a general chatbot.
• Real SQL under the hood. It writes and runs SQL against your live model, so results reflect the current state of the data, not a stale copy.

ChatGPT Enterprise is excellent for open-ended reasoning, drafting and one-off file crunching. iDBQuery is the better fit when the question is *about your systems* and the answer has to be current, joined across sources, and auditable. Many teams use both: a general assistant for writing, and iDBQuery for trustworthy answers on their own data.