ChatGPT for data analysis: a database-native alternative
Updated 2026-10-07
Intellrise is an AI data analyst for anyone pasting CSV exports into a chat: it connects to six sources including Postgres, MySQL and Redshift, joins across them, and runs on your own OpenAI key, with no credit meter and no message cap. Free is $0 with one live database source, unlimited Google Sheets and all five export formats; Pro is $29 a month and is where file upload lives; every new account starts on 14 days of Pro with no card.
Where your data lives decides whether you can skip that export at all. Checked 15 August 2026, ChatGPT's app directory at chatgpt.com/plugins lists Neon Postgres, Supabase and BigQuery; Snowflake, MySQL and Databricks were not in it that day, and anything it does not cover means building your own Model Context Protocol app. Intellrise connects Postgres, MySQL and Redshift directly whoever hosts them, and a question you save re-runs the SQL stored with it instead of having it written again next month.
ChatGPT vs Intellrise for data analysis, at a glance
- What it is — ChatGPT: a general assistant that analyses files you upload. Intellrise: a data analyst built to answer recurring questions on live databases and warehouses.
- Data connection — ChatGPT: analyses CSV/Excel files you upload, documents connected sources including Google Drive, OneDrive, SharePoint, Gmail, Outlook, Teams, GitHub, Dropbox and Box, and — checked 15 August 2026 — lists Neon Postgres, Supabase and BigQuery in its app directory at chatgpt.com/plugins. Snowflake, MySQL and Databricks were not in that directory on that date, and anything it does not cover means building your own Model Context Protocol app. Intellrise: connects six sources (PostgreSQL, MySQL, Redshift, Google Sheets, CSV, Excel) and joins across them; the three databases connect directly whoever hosts them.
- Reproducibility — ChatGPT: answers by writing and running Python in what OpenAI describes as a stateful Jupyter environment, so the artifact is generated code rather than a stored query definition. Intellrise: schema-aware SQL with a readable query plan and a read-only guard, so a saved question re-runs its stored SQL rather than being generated again, and you can audit it.
- Schema memory — ChatGPT: has persistent memory. OpenAI documents saved memories that are "always considered in future responses" and referencing your past conversations — general-purpose memory across chats, files and connected apps. What is not documented is a per-source model of your schema you can inspect or edit. Intellrise: a semantic layer scoped to each data source, which learns your column and table meanings from chat, saves them as definitions you can read and edit, and supports dbt.
- Model choice — ChatGPT: OpenAI's models only. Intellrise: OpenAI's models plus Gemini, Anthropic, DeepSeek, MiniMax or any OpenAI-compatible provider, switched any time from the model picker in chat.
- Output — ChatGPT: charts and analysis inside the chat. Intellrise: persistent dashboards and editable reports you can export to PDF, DOCX, PPTX, Excel and CSV, plus read-only share links on Pro.
- Cost model — ChatGPT: a flat subscription for the assistant. Intellrise: flat plans (Free $0, Pro $29/mo) with no credit meter; your AI provider bills the tokens directly.
- Built for — Intellrise: analysts and teams who need a saved query definition they can re-run and diff on live databases, and a report they can hand to a stakeholder.
| The question buyers actually ask | What OpenAI's own documentation says |
|---|---|
| Does ChatGPT remember things between separate chats? | Yes. OpenAI documents saved memories that are "always considered in future responses", plus referencing your past conversations — and says memory is available on the free plan. |
| Can ChatGPT connect to a SQL database? | No first-party SQL, Postgres, MySQL, BigQuery or Snowflake connector is listed on OpenAI's connector or data-analysis pages. Building a custom app with the Model Context Protocol is documented as a route to "your own tools and internal data". |
| Which data sources are listed? | File upload, plus Google Drive, OneDrive, SharePoint, Gmail, Google Calendar, Outlook, Teams, GitHub, Dropbox and Box. |
| How does it actually run an analysis? | It "writes and runs Python code in a stateful Jupyter notebook environment". |
| Can the analysis sandbox reach the internet? | No. "The Python environment used for data analysis cannot make external web requests or API calls." |
| Which plan tiers exist? | Free, Go, Plus, Pro, Business and Enterprise. OpenAI's pricing page shows prices in the visitor's local currency. |
1. Keep the model you like — pointed at your live database
The most common way people use ChatGPT for data analysis is to export a file and upload it. That means the data is a snapshot the moment you download it, and every question you ask again next week starts with another export.
Intellrise connects directly to your database or warehouse with a read-only user, so answers are always against live data. Pick OpenAI as the provider and you keep using the GPT models you already know — the difference is that the model is now working against your real schema, through SQL you can re-run, instead of a pasted file.
2. A stored query definition, not code inside a chat
ChatGPT answers a data question by writing and running Python — OpenAI describes it as writing and running Python in a stateful Jupyter notebook environment. What you are left holding afterwards is generated code inside a conversation rather than a saved query definition attached to a source, so re-establishing how a number was produced means finding the chat it came from. OpenAI's own guidance notes it "may choose an analysis method or chart type that does not match your intent on the first try".
Intellrise generates schema-aware SQL against your connected source and shows a readable query plan in business terms, with the exact SQL one click away and a read-only guard that refuses write statements such as DROP, DELETE, UPDATE and INSERT. A question you save re-runs the SQL stored with it rather than being generated afresh, and you can audit exactly how it was calculated — which is what makes it safe to put in front of a stakeholder or a recurring report.
3. A semantic layer scoped to the data, not to you
ChatGPT's memory is attached to you. OpenAI documents a memory feature that saves details from your chats, files and connected apps, held as a notepad you can view and delete. What is not documented is a per-source model of a schema — definitions bound to a specific database, table and column, that you can read, correct and hand to a colleague. That distinction matters when an ambiguous field is what produces a wrong number.
Intellrise runs a semantic layer that annotates your schema and keeps the corrections you make: it learns your column and table meanings from your chats and keeps them across sessions and schema refreshes, with a dbt mode and a manual editor. The meanings you correct are kept, so you build up an asset instead of starting over. And the output goes from the live question to a persistent dashboard to an exportable report, not just an in-chat chart.
Who Intellrise is for
Intellrise is the one to buy when your data lives in a database or warehouse, when a recurring question needs to become a saved query you can re-run and diff rather than code inside a chat, and when the answer has to end up in a report rather than a chat window. Analysts, analytics engineers, dbt users, data consultants and technical founders tend to feel the difference first: reproducible SQL they can audit, a semantic layer that learns their schema, a flat price with no credit meter or message cap, and reports they can actually hand to a stakeholder.
Try it on your own data
One question of your own, against your own data, settles this faster than any comparison page. Intellrise has a free tier — one database source plus unlimited Google Sheets, three dashboards, and unlimited reports and exports — so you can connect a read-only source, add a free Gemini key (how), and see the query plan for yourself before deciding. The 14 days of Pro every new account starts on add the AI semantic layer and file upload.
Frequently asked questions
For working with live databases, yes. Intellrise connects directly to your database or warehouse, generates reproducible schema-aware SQL you can audit, keeps a semantic layer that annotates your schema and keeps the corrections you make, and exports real reports. Free is $0, Pro is $29 a month, and every new account starts on 14 days of Pro with no card.
Yes, for some databases. Checked 15 August 2026, ChatGPT's app directory at chatgpt.com/plugins lists Neon Postgres, Supabase and BigQuery. Snowflake, MySQL and Databricks were not in that directory on the same day, and OpenAI still documents building your own Model Context Protocol app for anything it does not cover. So the rule is not "it cannot reach a database" but "it reaches the ones whose vendor built and shipped an app": if your Postgres is hosted by Neon or Supabase there is a first-party path, and if a contractor set it up on a VPS there is not one you can click. Intellrise treats six sources and cross-source joins as first-class regardless of who hosts them, with a persistent semantic layer on top.
No. Intellrise supports OpenAI, so you can keep using GPT models with an OpenAI key — Intellrise points them at your live database through reviewable SQL. You can also use Gemini, Anthropic, DeepSeek, MiniMax or any OpenAI-compatible provider.
For anything you pin, the query stays the same. A chart pinned to a dashboard re-runs the SQL that was saved with it, so the number moves only when your data moves, or when the query itself has a rolling window or a top-N with no tiebreak. A new question in chat is written fresh by the model, so read the SQL in the query plan before a number goes somewhere that matters, and pin it once it is right.
Yes, about you. OpenAI documents a memory feature that saves useful context from your chats, files and connected apps, and states that saved memories are "always considered in future responses". What OpenAI does not document is a per-source model of a database schema: definitions bound to a specific table and column that you can read, correct and share. That is the distinction to test, not whether memory exists.
No, according to OpenAI: "The Python environment used for data analysis cannot make external web requests or API calls." That sandbox boundary means the data has to come to the sandbox as an upload or through a connected source, rather than the analysis reaching out to a live database. Intellrise takes the other approach: every question runs against your live source — PostgreSQL, MySQL and Redshift are attached read-only and queried in place.
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The one-stop AI data analyst that knows your schema.
One place from question to finished work: the SQL, the chart, a dashboard your whole team can open, and a report that emails itself every morning. It learns what your columns mean, so nobody explains a field twice. Start free with one database plus Google Sheets, or take 14 days of Pro with no card.
Every new account starts on a 14-day Pro trial with no card. After it lapses, the free tier stays free.