TableFlow alternative that runs entirely in the browser
Updog does the upload, column matching, validation and editing on the user's machine. Your app receives the clean rows through a callback.
100% client-sideWhite-labelFlat $19/domain/month
Updog and TableFlow at a glance
| Dimension | Updog | TableFlow |
|---|---|---|
| Data privacy | Files are parsed, validated, and edited in the browser. Row data never reaches Updog's servers, so there is no data processor, DPA, or residency to manage. | TableFlow's platform is server-side: documents are uploaded to its cloud and processed by third-party AI models, including Google's Gemini, so file contents leave your infrastructure. It claims SOC 2 Type II, TLS 1.2+ in transit, AES-256 at rest, third-party penetration testing, logical data separation, and secure deletion with audit logs. Its privacy policy addresses CCPA and commits to deleting accessible personal data within seven business days of an account-deletion request; no GDPR statement, DPA, or data-residency region was found. |
| White-label | Full styling through CSS variables and class overrides. No Updog logos or "powered by" on any plan, including the free one. | There is little to white-label. TableFlow is API-first with an internal review UI for your team, not a branded importer you drop into your users' flow, so no theming, logo, or branding controls apply. No embeddable component was found. |
| Pricing | Free for development, and a 14-day trial with no card when you go live. In production, a flat $19 per domain per month, the same at any volume, and your first paid domain includes a free one for staging. | Sales-led, with no public pricing: the site offers a demo and a contact form rather than plans or rates. TableFlow is venture-backed (Y Combinator and others). |
| Data mapping | Schema in code. Fuzzy column matching with a built-in synonym dictionary you can extend, or connect the AI your organization already approves through a hook, so it runs on infrastructure you control with no new AI vendor to clear. Updog keeps no mapping history. Your app owns that state and can supply saved selections on later imports. Maps incoming values to your options. Auto-detects number and date formats. Combines files by upserting on a key. | Extraction templates act as blueprints that define the fields, tables, and validation rules to pull from a document. AI handles field mapping and normalization, classifies document types such as invoices and receipts, detects nested and merged-cell tables, and picks the correct sheet from a multi-sheet Excel file. This is AI extraction from unstructured or semi-structured documents, not header-to-column matching of a clean file. |
| Data cleaning | Cells edit inline through a typed editor per column, text, number, date, select, or multi-select, with clipboard, fill handle, and full undo and redo. Values pass a transform on the way in, then validation runs at three levels, built-in rules, your own checks, and asynchronous ones. Sorting, full-text search, filters on the columns you enable, and formulas. Invalid rows can block submit or come through flagged. Any view also exports in any supported format. | Two validation layers: configurable rule-based checks (email, phone, regex, number ranges, decimal places, required fields, custom lists, with error, warning, and info severity) and AI verification that compares extracted data against the source document to catch missing rows, OCR errors, and wrong values. A side-by-side human review UI shows the original document next to the extracted data with bounding-box attribution, inline editing, notes, full edit history, row-level status, and audit logs; low-confidence extractions can be routed to a review queue. |
| Scale & performance | About one million rows at around 20 columns in the browser, with a filter on every column. No fixed row cap, the browser tab's memory is the ceiling, and fewer columns reach further. Filtering and sorting run in a worker, measured at 30 to 250 milliseconds over a million rows. | Server-side, and built for documents rather than large flat files: it processes multi-page PDFs and multi-tab Excel with merged cells, and reports volumes and processing times on analytics dashboards. No row, column, or file-size limit is published, and throughput depends on TableFlow's cloud rather than the browser. |
| Integration | React component, plus a Web Component for Vue, Angular, Svelte, and vanilla JS. Renders inline in your page's DOM. Imports CSV, TSV, Excel, JSON, and XML, plus any other format through a parser you register yourself. | API-first. A REST API returns structured data as JSON or CSV, webhooks fire when documents finish processing, and pre-built connectors cover ERPs plus n8n, Make, and Zapier. There is no frontend SDK or embeddable UI component for the platform; delivery is backend to backend. Multiple AI providers can be selected per document type. |
| Languages & RTL | English by default, with every interface string overridable, so you can localize into any language. Right-to-left is first-class. It flips layout, text alignment, scrollbars, and column pinning, and carries through to export. | No multi-language interface is documented for the platform. Its open-source importer ships English, Spanish, and French, and takes your own translations for other languages. No right-to-left support was found for either. |
| Accessibility | An ARIA grid with full keyboard navigation and screen-reader support. WCAG 2.2 AA is the level Updog builds to, with automated rule checks over every component and every step of the importer on each test run. Updog holds no independent audit or VPAT. | No accessibility statement was found for the platform, and its public pages describe no keyboard or screen-reader support for the review UI. Its open-source importer carries table roles, labels, and some keyboard handling, without grid semantics. |
This page compares Updog with TableFlow's current product, a server-side AI document-extraction and automation platform (its V2 "AI teammates" launched in September 2025). TableFlow also still ships an open-source, MIT-licensed, client-side CSV importer on GitHub, which is closer to Updog's category but is no longer the marketed product. Facts checked against TableFlow's public pages in July 2026.
Which one fits your team
TableFlow may fit better if
- You need to extract structured data from unstructured documents like PDFs, scans, invoices, or images, not just import clean CSV or Excel files.
- You want AI to classify documents and match extracted data against your ERP, purchase orders, or packing lists.
- You want a server-side pipeline that delivers via API, webhooks, and ERP connectors, with a back-office review queue rather than an embedded, self-serve importer.
- You are automating document workflows and are comfortable with cloud processing and sales-led pricing.
Updog may fit better if
- You want an importer and spreadsheet editor embedded in your own app.
- You want to match your app's design exactly, styling the editor with your own CSS through variables and class overrides.
- You want to handle large files in the browser, with nothing stored on a server.
- You want flat, public pricing with no per-import fees.
- You want to use the AI your organization already approves, with no new vendor to clear.
Questions people ask
Is Updog a drop-in replacement for TableFlow?
No. Updog is a browser-based importer and spreadsheet editor you embed in your app, while TableFlow is a server-side AI platform that turns messy documents such as PDFs and scans into structured data.
How do the two handle data privacy?
Updog processes everything in the browser with no server component, so file data never leaves the user's machine. TableFlow uploads documents to its cloud and passes them to third-party AI models, including Gemini.
How does Updog pricing compare?
Updog is free in development and a flat $19 per production domain per month. TableFlow does not publish pricing; it is sales-led, with a demo and a contact form.
Can Updog handle large files like TableFlow?
Yes, about 1 million rows in the browser, bound by the machine's memory. TableFlow works at the document level rather than the row level, and publishes no row or file-size limit.
Can Updog be used to view or edit existing data, without an import?
Yes. Load existing data straight into Updog's editor to view and edit it, with a read-only mode. TableFlow's review UI is built for correcting data extracted from a document, not general spreadsheet editing.
How do the two handle accessibility?
Updog's grid uses ARIA semantics with full keyboard navigation and screen-reader support, and the importer is checked against WCAG 2.2 A and AA rules on each test run. Updog holds no independent audit or VPAT. For TableFlow, no accessibility statement was found, and its public pages describe no keyboard or screen-reader support for the review UI. Its open-source importer carries table roles, labels, and some keyboard handling, without grid semantics.
Can the interface be translated, and does right-to-left work?
Updog ships in English with every interface string overridable, so you can localize into any language, and it renders right-to-left layouts natively. TableFlow documents no multi-language interface for its platform. Its open-source importer ships English, Spanish, and French and takes your own translations for other languages, and no right-to-left support was found for either.
See how Updog compares to other CSV importers
Try Updog for free
Install the package, add your columns, render the component. Free on localhost and preview domains. Every feature included.