Pylon vs. Convex
Pylon and Convex are the two TypeScript-first reactive backends. Both ship reactive queries, schema-as-code, and a managed cloud. The honest differences are licensing, deployment shape, and what's in the box.
You want the most polished pure-TS dev loop, you'll stay on Convex's cloud (or accept their FSL-licensed self-host), and you don't need native SSR or faceted search.
You want a single-binary self-host, a permissive license, native faceted search, or server-rendered React in the same process.
Architecture at a glance
| Pylon | Convex | |
|---|---|---|
| Process model | One service (Rust + Bun) | Hosted service / multi-service self-host |
| Default store | SQLite (Postgres optional) | Custom Convex DB |
| License | MIT OR Apache-2.0 | FSL — converts to Apache after 2 yrs |
| Self-host on day 1 | Yes — one binary | Yes — multi-service |
| Faceted search | Built-in (full-text + facets) | Roll your own queries |
| Native SSR | Yes — React in the same binary | Pair with Next.js |
| Native mobile sync | Swift engine at parity | React Native |
What both ship
- Reactive queries that auto-update the UI on writes
- TypeScript-first query / mutation / action server functions
- Schema as code with entities and types end to end
- Real-time WebSocket sync, optimistic mutations
- Built-in auth (magic links, OAuth), file storage
- React and React Native SDKs
- Self-host + managed cloud options
Where Convex wins
Convex has invested heavily in DX polish. Type inference flows end-to-end without a codegen step. Pylon's TS flow is tight, but Convex's is a touch tighter out of the box.
Well-funded company, more docs, more examples, more StackOverflow answers, more job postings.
Convex's vector indexes are approximate and built for large tables. Pylon's built-in vector search (field.vector + ctx.llm.embed + ctx.db.vectorSearch) is exact k-NN — perfect recall to roughly 100k rows per entity; past that, Convex's ANN wins.
Where Pylon wins
Install the binary and Bun on a VPS, or run `pylon deploy`. Convex's self-host uses multiple services and a custom database.
Add search: to an entity and get full-text hits and live facet counts in one call. Convex requires custom queries on top of full-text search.
Pylon server-renders your frontend from the same binary, including file routing, <Link>, <Image>, metadata, and streaming. Convex pairs with a separate Next.js host.
MIT OR Apache-2.0. Convex's FSL bars you from running a competing managed Convex for two years. An edge case for most, but meaningful for devtools companies.
The full sync engine ported to Swift and kept at parity, for native iOS and Mac. Convex's mobile story is React Native.
Moving from Convex
| Convex | Pylon |
|---|---|
| defineSchema(...) | buildManifest({ entities: [...] }) |
| query / mutation / action | Same names, identical mental model |
| useQuery(api.tasks.list) | db.useQuery("Task") |
| ctx.db.insert("tasks", {...}) | ctx.db.insert("Task", {...}) |
| Convex auth | Magic-link / OAuth / OIDC |
| Convex file storage | presigned uploads (S3 / R2 / local) |
| Convex scheduled functions | ctx.scheduler + durable ctx.workflows |
| Convex search index | Per-entity search config |
| Convex vector search | field.vector + ctx.db.vectorSearch |
Convex has more developer mindshare today and more polish in reactive query batching, type-inference depth, and IDE integration. If you want the most polished pure-TS reactive backend and do not need single-process self-hosting, faceted search, or native SSR, Convex is a strong choice.
Try Pylon for yourself.
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