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Ivandt vs Dromo
Comparison • Updated December 2025

Dromo vs Ivandt

Which importer actually lets you stop writing custom scripts?

If you've tried both, you already know this: on the surface Dromo and Ivandt look similar. Both let your users upload CSV/Excel, see a spreadsheet-like UI, fix errors, and get JSON back. Where they really diverge is how you integrate them, how much custom code they force you to write, and how far they scale before things start to hurt.

At a glance

Max rows (comfortable)

Dromo

10k rows

Ivandt

500k rows

Built-in validators

Dromo

5+

Ivandt

18+

Framework support

Dromo

3

Ivandt

5

Custom code required

Dromo

80%

Ivandt

10%

Key differences

Native integration

Dromo

SDK widget with config props

Ivandt

Web component that lives in your DOM

Cross-field validation

Dromo

Write custom hooks

Ivandt

Declarative JSON queries

Data transformation

Dromo

Build your own functions

Ivandt

Built-in transformer library

Remote lookups

Dromo

DIY fetch, cache, retry

Ivandt

First-class DataSources

Smart Fix

Dromo

Not available

Ivandt

One-click corrections

Large files

Dromo

Main thread, ~10k rows

Ivandt

Web workers, 500k+ rows

Real-world comparison (developer-to-developer)

Integration: React SDK vs Web Component

Dromo

Integrate via JavaScript/React SDK (DromoUploader component). Pre-built UI that drops into your app. Layout and behavior controlled by Dromo. Styling via configuration options and theme props.

Ivandt

Framework-agnostic web component with thin wrappers for React, Vue, Angular, and Svelte. Native element in your DOM - render anywhere: full page, modal, sidebars, split views. CSS variables + visual Theme Designer for pixel-perfect integration.

Dromo feels like "dropping someone else's app into yours". Ivandt feels like "another component in your app" that just happens to do the hard stuff.

Validation Logic: Custom Hooks vs JSON Rules

Dromo

Define fields with types (email, url, number, date). Field validators: required, unique, unique_with. Dynamic or cross-field logic requires custom hooks. Country/State cascades solved with imperative hook code.

Ivandt

Full validator engine with basic and advanced validators. DataSources: reusable in-memory or remote data sets. Queries: JSON rules that filter based on other cells. Country/State cascade is declarative JSON, not hooks. Rules run in web workers, in parallel.

With Dromo, complex business rules eventually pull you back into custom JavaScript. With Ivandt, those same rules live in a schema, are reusable, testable, and naturally parallelizable.

Data Transformation: Manual Functions vs Transformer Library

Dromo

"Hooks everywhere": column hooks and row hooks. Every cleanup rule becomes a function you own. Uppercase, phone normalization, country codes = custom code you deploy and maintain.

Ivandt

Large built-in transformer library: trimming, case changes, currency, dates, ID generation. Defined in your schema, not in code. Run automatically during validation or via Smart Fix. Declarative = runs in web workers without UI freeze.

Your "uppercase first name" example is configuration in Ivandt, but code in Dromo.

Remote Data & Lookups

Dromo

API lookups and enrichments via hooks. You write fetch logic, concurrency, retry, caching. Error handling is your responsibility.

Ivandt

Remote data is a first-class concept. DataSources backed by static JSON or remote APIs. Dropdowns wired directly to sources with JSON rules. Google Places, IATA airports, internal product search. Bulk/batch calls, caching, retry & backoff built-in.

Your "Excel column of addresses validated against Google" story is a first-class Ivandt feature, while in Dromo it's a custom hook pipeline.

Performance Comparison: Main Thread vs Web Workers

Dromo

Parses files in-browser, good for typical SaaS use cases. Struggles around ~10k rows: UI lag, parsing slowdowns. Very large files require Headless API (server-side, premium).

Ivandt

Built from day one around web workers + concurrent processing. 1M+ cells/sec in the browser as a core promise. Parsing, validation, transformation all parallelized. UI stays responsive even with large files.

Dromo is fine until your customers hit "big CSVs". Ivandt is designed specifically for big, ugly files without forcing you into a separate server-side product.

Schema-less Import Capabilities

Dromo

Always needs a schema. Either saved in Schema Studio or fields array in code. Must define at least a minimal schema first.

Ivandt

Dedicated schema-less flow. User uploads any file, Ivandt parses it. Assign field types per column (text, numeric, date, phone). AI suggests field types from header names. Validation, cleaning, Smart Fix all still work.

No schema, no time, just upload, clean, and go.

Smart Fix UX: Manual Correction vs One-Click Fixes

Dromo

Solid inline error reporting. AI suggestions to help correct invalid data. Automatic fixes still live inside your hooks - you decide how to clean and re-emit corrected values.

Ivandt

Smart Fix baked into the core import flow. Each field type surfaces one-click suggestions: "Fix to E.164 phone", "Trim whitespace", "Normalize currency". Powered by the same transformer library. Every error has "what's wrong" + "click to fix it".

This is the difference between a generic error table and something that actually guides non-technical users through cleanup.

Pricing Comparison: Dromo vs Ivandt

Dromo

Fully hosted SaaS. Pricing tiered, often tied to imports/volume. Enterprise features require talking to sales. Headless API and advanced quotas are premium.

Ivandt

Client-side processing, zero data storage by default. All parsing, validation, transformation in the browser. Flat, subscription-based pricing. Unlimited imports per account (no per-import trap). Optional hosted features on top.

We're not here to meter every import. We're here to solve the problem.

The verdict

Dromo is a solid product that works well for straightforward imports. But the moment you need cross-field validation, cascading dropdowns, remote lookups, or large file handling, you're writing custom hooks and maintaining imperative code.

Ivandt takes a different approach: declarative schemas, DataSources with JSON queries, a built-in transformer library, and web worker parallelization. The result is less code to maintain, better performance at scale, and a UX that actually guides users through fixing their data.

If you're tired of writing hooks for every business rule, Ivandt is the importer that actually lets you stop.

Frequently asked questions

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Last updated: December 2025 • Have feedback? Let us know