It keeps its own reports fast, documents itself, heals itself, and never loses a change — so your team just builds. No warehouse, no ETL, no schema docs, no backup anxiety.
Most data layers quietly hand your team a second job. The GF Cloud DB exists to make these jobs disappear.
Fresh reports shouldn't require a second database, a pipeline tool, and a nightly job that breaks on Saturdays.
Most databases get slower as they grow — until someone is paid to watch, tune, and rescue them. Speed shouldn't be a staffed position.
Every team promises to document the database. Nobody does — and six months later, no one remembers why a column exists.
The quiet fear that a conflict, a crash, or a bad sync silently overwrote something you can never get back.
Six capabilities, one data layer — and every performance claim below links to a measured result.
Reporting rollups keep themselves fresh as data changes. First build in 0.04 seconds; refreshed in 0.30 seconds under a 10,000-write storm on a million-row database — no ETL, no pipeline, no second system.
See the performance numbersMeasured at 100K, 1M, and 3M rows: onboarding in 0.6s, 5.5s, and 16.7s, update payloads nearly flat, reports sub-second under a write storm at a million rows. No DBA on speed dial.
The scaling measurementsEvery table, column, and relationship carries its purpose — machine-readable, always current. Your AI knows why every column exists, so it answers about your business, not your column names.
A live entity-relationship map of everything — schemas, tables, columns, and how they connect. Click anything to drill in. It's never out of date, because it's generated from the database itself.
A grid workspace on your real data: search, filter, sort, and edit with spreadsheet ease — on an actual database with real relationships underneath, not a toy that caps out.
Offline included: changes ship as kilobyte deltas, conflicts resolve deterministically, and the losing version is always preserved and recoverable. Kill it mid-transfer — it rolls back atomically.
The correctness proofOpen the schema explorer and see everything — every schema, table, column, and relationship, laid out as a living map. Click any box to drill from schemas to tables to columns.
And it isn't just shapes: every object carries its purpose. Why the column exists, what the table means to the business, how deletes behave. New engineers stop guessing. Your AI stops hallucinating about your data.
On the benchmark's exemplar database, 100% of the schema carries machine-readable intent after onboarding — the whole thing fits an AI context in ~4.8K tokens, with a ~400-token digest for everyday sessions.
Every object carries its purpose
| Order | Customer | Amount | Status |
|---|---|---|---|
| #10248 | Alpine Trading | $1,814 | Open |
| #10249 | Harborview Co. | $2,329 | Open |
| #10250 | Meridian Ltd. | $1,552 | Shipped |
| #10251 | Northgate Inc. | $654 | Shipped |
Illustrative data — search, filter, edit, export on your real database
If you've outgrown hosted spreadsheet-database tools like Airtable, this is the other side of the trade: the same friendly grid — search, filter, sort, edit, export — sitting on a genuine database you own.
152/152 scenario checks across 14 real-use-case scenarios, 205/205 regression checks, and a load/chaos/fuzz suite — one command, one verdict, all green. We published the whole thing as a reproducible benchmark.
See the GF Cloud DB BenchmarkIt's the data layer of the GritFlow Framework — the platform for building enterprise vertical AI that compounds into a moat.
