SQL to Prisma — Schema Converter
Convert SQL CREATE TABLE statements to Prisma schema models with inferred types, @id, @unique, and @default attributes.
How it works
- 1Paste or type your text in the input field
- 2Click "Convert to Prisma" — processing happens in your browser
- 3Copy the result or download as a text file
What to do next
About SQL to Prisma Schema
SQL to Prisma Schema is the kind of utility you bookmark and reach for when you need it. Convert SQL CREATE TABLE statements to Prisma schema models with inferred types, @id, @unique, and @default attributes. It loads quickly, works on any modern browser, and produces a result you can download or copy in a single click.
Typical users of SQL to Prisma Schema include QA engineers writing repro cases, engineers debugging API payloads and backend developers inspecting requests. The thread connecting all of them is the same: a focused developer utility task that fits cleanly into a browser tab and benefits from a tool with sensible defaults and minimal setup.
SQL to Prisma Schema runs the entire transformation inside your browser. The file is read by JavaScript running in the page, processed in-memory by standard browser APIs, and written back as a download. The browser is the runtime; the page is the interface. You can confirm what the tool does by opening the developer-tools Network tab during a run — the only requests are for the page's own static assets.
SQL to Prisma Schema is implemented on top of standard browser APIs. Inputs are read from the file picker or drop zone, decoded in the browser, processed, and re-encoded into the output format. Files up to 0 MB are well within the comfort zone of any modern browser.
Most people land on SQL to Prisma Schema via a search at the moment they actually need the tool. That shapes the design: the page is a single screen with the input on one side, the controls in the middle, and the result on the other, so a first-time visitor can complete the job without reading documentation.
SQL to Prisma Schema fits naturally next to several adjacent tools. Common companions include SQL Query Formatter, SQL Schema Visualizer, JSON to SQL CREATE TABLE, and MySQL to PostgreSQL Converter — combine them when the job needs more than one transformation. After running SQL to Prisma Schema, many users move on to SQL Schema Visualizer and JSON to SQL CREATE TABLE. Each tool is a separate page so you can compose the exact pipeline you need.
SQL to Prisma Schema returns the result as a download. If you are running multiple jobs, the output names will not collide as long as the input names differ. You can re-run with different settings as many times as you like; each run produces a fresh file with no caching trickery in between.
On limits: 0 MB per file is the ceiling. Output formats and quality settings are listed in the controls panel above, and they apply to every run.
The transformation in SQL to Prisma Schema is deterministic — the same input plus the same options produces the same result every run. That predictability matters when the result has to match an upstream specification or be reproducible later.
Some background on the design choices behind SQL to Prisma Schema: every option you see on the page is there because a real workflow needs it, and every option that is not shown has been deliberately omitted to keep the common case fast. The bias is toward minimal-but-complete.
Tips from users who reach for SQL to Prisma Schema regularly: process one input first to confirm the settings produce what you expect before committing to a batch; treat the page as the working surface and avoid leaving large jobs running in a backgrounded tab where the browser may throttle JavaScript; and if a particular file fails, check whether the source is intact by opening it in its native viewer — most "tool errors" are actually input errors.
For most failure modes, refreshing the page and re-running the job is enough — the engine has no persistent state to corrupt. If the same input fails twice in a row, the input itself is most likely the problem (a truncated file, an unexpected variant of the format, or a stream the engine does not recognise).
As a single-page tool, SQL to Prisma Schema stays focused on one developer utility step. Multi-step workflows are composed by chaining adjacent tools — each tool produces a standard file the next one can read directly, so a longer pipeline is just a sequence of short tab-and-tab visits.
Open the workspace above to start using SQL to Prisma Schema. The engine loads on the first interaction so the page itself stays light, and once the tool is warm it processes subsequent jobs quickly. The moment the page is interactive, the tool is ready to do real work on your file.
How it works
- 1Reach the SQL to Prisma Schema page in your browser to begin.
- 2Add your developer input by dropping it onto the page or browsing for it.
- 3Adjust the options to match what you need. Sensible defaults cover the most common case, so you can usually skip this step.
- 4Click to start the job. The engine (standard browser APIs) processes the input in the page; you can watch the progress indicator until it completes.
- 5Save the output when it is ready.
- 6Repeat the process for additional inputs whenever you need to. The page stays loaded, so subsequent runs are quick.
Common use cases
- Generate boilerplate from a single specification line using SQL to Prisma Schema.
- Compare two API responses to spot a regression.
- Decode a token to confirm its claims during a debugging session.
- Hash a string for a quick reproducibility check.
- Convert between data formats while wiring up an integration.
- Format a noisy log line into something a teammate can read.
- Pretty-print a minified blob during incident triage.
- Encode binary content for transport in a JSON body.
FAQ
Which SQL types are mapped?
INT, VARCHAR, TEXT, BOOLEAN, FLOAT, DECIMAL, DATE, TIMESTAMP, JSON, BLOB, UUID, and SERIAL variants are mapped to Prisma equivalents.
Are relations inferred?
Basic foreign key references are noted but full Prisma @relation syntax may need manual adjustment for complex schemas.
Does it handle AUTO_INCREMENT?
Yes — AUTO_INCREMENT and SERIAL types are converted to @default(autoincrement()).
Are model names PascalCase?
Yes — table names are converted to PascalCase and snake_case is converted to camelCase for model names.
Is my schema private?
Yes — conversion happens 100% in your browser. Nothing is sent to any server.
Can I convert multiple tables?
Yes — paste multiple CREATE TABLE statements and all tables will be converted to separate Prisma models.
Do I need to install anything to use SQL to Prisma Schema?
No installation is needed. SQL to Prisma Schema runs as a normal web page, with no browser extension, no native helper, and no separate desktop client to download. That is partly a privacy choice — extensions can request broad permissions, while a regular page is sandboxed by default — and partly a convenience one: you can use SQL to Prisma Schema on any computer you have temporary access to without leaving anything installed on it.
Will SQL to Prisma Schema keep working in a year?
SQL to Prisma Schema is updated whenever the underlying engine releases an improvement or a bug fix. Because the tool is delivered as a static page, every visit fetches the latest version automatically — there is no "version" to manage on your end. If a particular release ever changes default behaviour, the change is documented on Favtoo's changelog so you can confirm what shifted.
Can I use SQL to Prisma Schema on documents that contain personal data?
Your file is processed inside your browser by standard browser APIs. The engine reads the file's bytes from your tab's memory, computes the result, and writes the result back into the tab. You can confirm what the page does by opening developer tools and watching the Network tab during a run — the requests you see are for the tool's static assets only.
Does Favtoo keep a copy of files I process with SQL to Prisma Schema?
Favtoo keeps no copy of your file because Favtoo never receives your file. SQL to Prisma Schema runs entirely in your browser, the input is held only in your tab's memory, and closing the tab discards it. There is no opt-in cloud history, no "recent jobs" panel synced to an account, and no server-side retention to configure — the architecture simply has nowhere for your file to be stored.
How accurate is SQL to Prisma Schema?
SQL to Prisma Schema is built on standard browser APIs, which is the same class of engine used by professional developer utility pipelines. For deterministic operations, the output is byte-identical to what an equivalent CLI run would produce; for operations involving a codec or a model, the result is well within the range of what comparable tools generate. If you have a specific reference output you need to match, run a small test job first to confirm the configuration produces what you expect.
What is the maximum file size for SQL to Prisma Schema?
Inputs are capped at 0 MB per file, which keeps memory usage stable across phones, tablets and older laptops. You can run SQL to Prisma Schema as often as you need; every run produces a full-quality result.
How is SQL to Prisma Schema different from desktop apps that do the same thing?
Desktop apps usually have more advanced features but require installation, maintenance and (often) a licence. Paid online tools are convenient but route your file through their servers and gate downloads behind accounts. SQL to Prisma Schema sits in between: free, instant, and private, but intentionally narrow in scope. For one-off jobs and the common developer utility operations, it is usually the lowest-friction choice; for highly specialised work, a dedicated app is still the right answer.
Does SQL to Prisma Schema reduce quality of the result?
SQL to Prisma Schema is built to preserve quality wherever the underlying developer format allows it. Operations that are mathematically lossless (e.g. structural transformations, lossless re-encoding) round-trip with no perceptible change. Operations that involve a lossy codec inevitably introduce small artefacts at the byte level, but the defaults aim at the sweet spot where output looks or sounds the same to a normal viewer or listener while still being meaningfully smaller or faster than the input.