Both are credible ways to sync a server database down to local stores on your clients. But if there is no server database — no Postgres, no backend to route writes through — you're comparing on the wrong axis. Here's the one that's missing.
ElectricSQL syncs subsets of a Postgres database down to clients — you define what each client sees, reads stay fresh, and writes go through your own API. If Postgres is your system of record, it's a strong read-path answer.
PowerSync runs as a sync service between your backend database (Postgres, MongoDB, or MySQL) and SQLite on the client, with rules for what syncs where and a write path through your backend. Same shape: a server database is the source of truth.
The GF Cloud DB answers a different question: what if there is no server database at all — just your app's own database on every node, all of them writable? That's the local-first case for desktop apps, field deployments, edge nodes, and apps where the data layer IS the product. It's the case neither tool targets, and the case the benchmark measures.
| Dimension | ElectricSQL | PowerSync | GF Cloud DB |
|---|---|---|---|
| Server source of truth | Postgres | Your backend database (Postgres, MongoDB, MySQL) | None required — the GF Cloud DB is the source of truth on every node |
| Where writes land first | Your API writes to Postgres; changes sync down | Clients upload writes to your backend, which applies them | Every node writes locally; all converge (multi-master) |
| Works without a server database | No — Postgres is the source of truth | No — your backend database is the source of truth | Yes — an existing database becomes fully managed in one call (1M rows in 5.5s, measured) |
| Reports without a warehouse | Your reporting stack to build | Your reporting stack to build | Built in — refreshed in 0.30s under a 10,000-write storm on 1M rows, measured |
| Conflict story | Write path is yours to design | Write path is yours to design | Deterministic resolution, every losing write preserved and restorable — verified in 152/152 scenario checks |
| Published, reproducible correctness benchmark | — | — | Yes — conflicts, kill-mid-sync, offline catch-up, fuzz parity |
| Schema documents itself after onboarding | — | — | 100% machine-readable intent coverage on the Northwind exemplar |
| Typical sync payload | Depends on your shapes/rules | Depends on your sync rules | 10K changed rows = 128–143 KB gzipped (531–1,448× smaller than the database), measured |
ElectricSQL and PowerSync characterizations are based on each vendor's public documentation as of July 2026 — verify current capabilities with each vendor. All product names are trademarks of their respective owners. GF Cloud DB numbers are measured results from the GF Cloud DB Benchmark.
Postgres is your system of record and you want fresh, partial replicas of it on every client, with writes flowing through your existing API.
You run Postgres, MongoDB, or MySQL on the server and want a managed sync layer keeping client-side SQLite stores current.
There is no server database — every node writes locally, you want reports fresh without a warehouse, and you need measured guarantees that no change is ever lost plus a schema that documents itself for your AI.
Every GF Cloud DB number above is a measured result with a scenario ID behind it. Re-run exactly what we measured.
