Pylon vs. Supabase

Supabase is Postgres-as-a-platform — a curated stack of GoTrue, PostgREST, Realtime, Storage, and Studio. Pylon is one binary that does most of those things itself. Both are valid; pick the trade-off.

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TL;DR

Pick the one that fits your shape

Choose Supabaseif

You want the full Postgres SQL surface (CTEs, materialized views, pgvector), you're comfortable operating multi-service deployments, or you need Postgres tools (Metabase, Hex, Retool) hitting your data directly.

Choose Pylonif

You want one binary on a VPS, native faceted search with live counts, in-process functions sharing a transaction with writes, or game shards alongside your app data.

Architecture

Where the two diverge

	Pylon	Supabase
Process count	1	5+ (Postgres, GoTrue, PostgREST, Realtime, Storage, Studio, Edge Functions)
Default DB	SQLite (Postgres optional)	Postgres (required)
Backed by	Rust	Postgres + Go + Elixir + Deno
Self-host	`scp` + `systemctl`	docker-compose with 7+ containers
Schema source of truth	TypeScript (`entity()`)	SQL migrations
Faceted search	Native (FTS5 + roaring-bitmaps)	`tsvector` for FTS, build facets yourself
Function-to-DB latency	`<1ms` (same process)	50–200ms (Edge → Postgres)

Same shape

What both ship

Either one is a real choice for an open-source Firebase-style backend. The differences below are about emphasis and operational shape, not feature presence.

Real-time subscriptions over WebSocket
Built-in auth — magic links, password, OAuth
File storage with signed URLs
Row-level access control (RLS for Supabase, policies for Pylon)
Web + mobile SDKs
Self-hostable, FOSS-licensed
Managed cloud option

Where Pylon wins

What you get with Pylon you don't with Supabase

One binary instead of seven services

One process, one port, one config file. Compare to docker-compose with Postgres + GoTrue + PostgREST + Realtime + Storage + Studio + Edge Functions. Same shape, vastly less ops.

Native faceted search

useSearch("Post", { facets: [...] }) returns hits + live facet counts. Supabase has tsvector for FTS but no native facets — you'd build them with custom queries.

Functions share a transaction with writes

Pylon's mutation runs in-process with ctx.db — throw inside, everything rolls back. Supabase Edge Functions are separate processes round-tripping over HTTP; atomicity needs explicit BEGIN/COMMIT.

Game shards built-in

Shard<S: SimState> for tick-based authoritative game state. Supabase has nothing equivalent — you'd run Colyseus or Nakama alongside.

Loro CRDTs in-process

Collaborative text/lists/maps/trees with conflict-free convergence. Supabase Realtime is broadcast-only — your CRDT logic lives entirely client-side.

32 built-in plugins

TOTP, captcha, rate limit, audit log, Stripe billing, MCP server, SCIM, soft-delete, computed fields, timestamps — flip on in the manifest. Supabase has Postgres extensions, but they're DB-level, not framework-level.

Where Supabase wins

What Supabase does better today

Honest comparison — these are real reasons to pick Supabase. If any of them are dealbreakers, choose accordingly.

Full Postgres SQL surface

Every Postgres feature works: CTEs, window functions, JSONB operators, materialized views, foreign data wrappers, GIST indexes, partitioning. Pylon's query layer is intentionally narrower.

pgvector at million-scale

First-class vector search via Postgres extension. Pylon has the vector_search plugin (in-memory) but Supabase's pgvector wins for production-scale embeddings.

Larger ecosystem

More libraries, more tutorials, more StackOverflow answers, more job postings. Eight years of battle-testing on PostgREST. Mature integrations with Metabase, Hex, Retool, etc.

Database UI for SQL exploration

Supabase Studio is excellent for ad-hoc SQL. Pylon Studio focuses on entity inspection — different shape.

Edge Functions

Globally distributed Deno runtime. Nice for latency-sensitive endpoints near the user. Pylon functions run where your binary runs.

Migration

Coming from Supabase

Most of the dev surface translates one-to-one. The biggest deltas show up as differences in shape, not features missing.

Supabase	Pylon
SQL schema + RLS	`entity()` + `policy()` in TypeScript
`supabase.from('todos').select()`	`useQuery("Todo")`
`supabase.auth.signInWithOtp(...)`	`startMagicCode(email)` → `verifyMagicCode(...)`
Storage buckets	`/api/files/init` → direct PUT (Stack0 / S3 / local)
Edge Functions	`mutation` / `action` in `functions/*.ts`
Realtime subscriptions	`useQuery` (server-authoritative) or `subscribeCrdt` (collaborative)
`pg_net` outbound HTTP	`fetch` inside an action (with `net_guard` plugin for SSRF defense)
`auth.users`	Pylon's `User` entity (you control the shape)

Honest weakness

Where Pylon loses

Supabase's Postgres-native data layer is more flexible than Pylon's for analytics, complex queries, and SQL-tool interop (Metabase, Hex, Mode, Retool). If your data model needs to interop with a wide data ecosystem, "it's just Postgres" is genuinely valuable. Pylon offers Postgres mode but the entity API stays the primary interface.

Both / and

When using both is the right call

Pylon supports Postgres as the backing store via the `postgres-live` feature. Some teams point Pylon at an existing Supabase Postgres to keep Studio for SQL exploration while gaining Pylon's sync engine, facets, and tighter function-data integration.

Try Pylon — free Hobby tier on Cloud

No card, no setup. Run a real Pylon project against managed Postgres in under a minute. Migrate from Supabasewhen you're ready — or run both.

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