Quadratic
Summary
The VLOOKUP that spans three sheets and breaks when someone adds a column is a rite of passage — Quadratic replaces it with AI-generated Python that reads like a comment, runs live, and doesn't shatter when the data changes.
Quadratic is a spreadsheet environment where cells can hold Python, SQL, or JavaScript instead of formulas, and an AI agent writes that code from plain-English prompts. You connect live sources — Postgres, Snowflake, QuickBooks, Plaid, Mixpanel — and the sheet stays in sync without CSV exports. The AI handles joins, forecasts, and charts; you review the generated code before it runs, so there is an audit trail. The ceiling appears when your analysis requires orchestration across multiple agents with complex branching — the spreadsheet model stops fitting the logic. Teams at that point reach for a dedicated workflow tool and keep Quadratic for the output layer.
Bottom line: Pick Quadratic when you need a finance or BI analyst to stop exporting CSVs and start querying live databases in a shareable sheet — but plan a different architecture the moment your workflow requires conditional branching that a spreadsheet canvas cannot express.
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Pros
Sign in to edit- AI writes Python and SQL from plain-English prompts, so analysts who know what they want but not the syntax stop being blocked — and the generated code is visible in the cell, which means a reviewer can verify the logic instead of trusting a black box.
- Live connections to Postgres, Snowflake, BigQuery, QuickBooks, Plaid, and Mixpanel mean the sheet refreshes from source data, so you stop chasing down who last exported the CSV and whether it was before or after month-end close.
- MCP support lets external agents write to and read from the spreadsheet as a tool, so Quadratic can sit inside a larger agent pipeline rather than requiring you to rebuild your entire workflow inside one product.
- Output lives in a familiar spreadsheet format, so sharing results with a finance director or product manager who will not open a Jupyter notebook is not a conversation you have to have.
- Replacing VLOOKUP stacks with readable Python reduces the 'who wrote this and why does it break' debugging cycle — the logic is explicit, versioned, and survives column-order changes.
Cons
Sign in to edit- Multi-step conditional logic — branching on what a previous query returned, then routing to a different data source based on the result — does not fit the spreadsheet execution model. Teams building that kind of workflow hit this ceiling on the second or third agent and add a separate orchestration layer, at which point they are maintaining two systems.
- No self-hosted deployment option means every live database connection and every piece of data processed by the AI agent transits Quadratic's cloud. Teams under strict data residency requirements or with security policies prohibiting third-party cloud access cannot use the product and move to a self-hostable alternative.
- The API and scheduled tasks are paid-only features, so teams evaluating the free tier for automated, recurring reports will find those capabilities gated — the evaluation environment does not reflect what production actually requires.
- The product targets analysts in a spreadsheet paradigm; engineers building data pipelines or transformation logic that belongs in dbt, Airflow, or a dedicated ETL tool will find the canvas constraining and the collaboration model mismatched to a code-review workflow.
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About
- Platforms
- Web
- API Available
- Yes
- Self-Hosted
- No
- Last Updated
- 2026-07-14T16:23:28.693Z
Best For
Who it's for
- Data analysts and BI teams
- Finance and product teams needing live data
- Users replacing complex VLOOKUP formulas with readable code
- Teams requiring auditable AI-assisted analysis
What it does well
- Exploratory data analysis across multiple live sources
- Financial modeling with QuickBooks and Plaid data
- Revenue forecasting and segment analysis
- Database analytics combining SQL and Python
- Creating shareable, repeatable reports with AI-generated code
Integrations
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Frequently Asked Questions
- Is Quadratic free?
- Quadratic has a permanent free tier alongside paid upgrades. You can keep using a baseline version indefinitely without paying.
- Is Quadratic open source?
- No — Quadratic is a closed-source tool. Source code is not publicly available.
- Does Quadratic have an API?
- Yes. Quadratic exposes a developer API. See the official documentation at https://quadratichq.com for details.
- What platforms does Quadratic support?
- Quadratic is available on: Web.
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Curated lists that include this category
Quadratic reimagines the spreadsheet as a live code environment: cells execute Python, SQL, or JavaScript, and an AI agent writes that code from plain-English questions. The core workflow is connect a data source, ask a question, review the generated code, and share the result as a spreadsheet your stakeholders already know how to read. Sources range from flat files and PDFs to live databases like Postgres, BigQuery, and Snowflake, plus SaaS connectors for QuickBooks, Google Analytics, Mixpanel, and Plaid.
The differentiating feature is that AI-generated code replaces formula nesting — not just as a convenience, but as a structural shift. A lookup that previously required an `IFERROR(VLOOKUP(…))` stack that breaks on whitespace becomes a readable pandas operation that anyone can audit. Because the code is visible in the cell, teams can verify what the AI produced before the result propagates through the sheet. This is the audit trail that finance and BI teams need when a number is going into a board deck.
Quadratic fits teams doing exploratory analysis, financial modeling, and repeatable reporting where the primary challenge is connecting scattered sources and making the logic readable. It struggles when the work requires agents running conditional task chains — the spreadsheet model is not designed for multi-step branching logic, and that wall appears early if your workflow involves more than fetch-transform-display. Teams building that kind of automation will need a separate orchestration layer and may use Quadratic only for the final presentation sheet.
The vendor page describes Model Context Protocol (MCP) support, which means any MCP-compatible agent can interact with the spreadsheet as a tool — a meaningful integration point for teams already building agent pipelines. Scheduled tasks and an API are also described, though the API and scheduled tasks are paid-only features. Self-hosting is not available, so data leaving your environment through live database connections is routed through Quadratic’s cloud infrastructure.