Reactive TypeScript Backends: Convex and Pylon Are Changing the Game

In short: As of September 5, 2026, choose Convex for a mature reactive TypeScript experience and managed cloud, or Pylon to prioritize self-hosting, SQLite or PostgreSQL, and permissive licensing. My default recommendation is Convex for shipping a product quickly; choose Pylon when infrastructure and licensing control matter most.

A traditional collaborative application often combines a database, an API server, and a real-time layer. Convex and Pylon bring these responsibilities together around TypeScript queries that re-run when the data they read changes. The main benefit is not speed alone: it is the reduction in synchronization code you must write and maintain.

What problem does a reactive backend solve?

Consider a CRM, shared document editor, or Kanban board. A conventional architecture requires several separate mechanisms:

// Classic REST API
// 1. Define the SQL schema
// 2. Create migrations
// 3. Write CRUD endpoints
// 4. Manage authentication
// 5. Add real-time updates
// 6. Invalidate caches
// 7. Synchronize frontend and backend types

Every connection can drift out of sync. A mutation succeeds, but the right event never reaches the client. A server type changes without an interface update. A cache entry becomes stale.

In a reactive backend, the server remembers the data read by a query. When a write touches those dependencies, it recomputes the result and pushes it to subscribers. Both the Convex and Pylon documentation describe this model.

How does Convex work?

Convex combines a reactive database, TypeScript server functions, and client libraries. The schema stays in the project:

// convex/schema.ts
import { defineSchema, defineTable } from 'convex/server';
import { v } from 'convex/values';

export default defineSchema({
  tasks: defineTable({
    text: v.string(),
    isCompleted: v.boolean(),
    authorId: v.string(),
  }).index('by_author', ['authorId']),
});

Reads use queries, while transactional writes use mutations:

// convex/tasks.ts
import { query, mutation } from './_generated/server';
import { v } from 'convex/values';

export const list = query({
  args: { authorId: v.string() },
  handler: async (ctx, args) =>
    ctx.db
      .query('tasks')
      .withIndex('by_author', (q) => q.eq('authorId', args.authorId))
      .order('desc')
      .collect(),
});

export const add = mutation({
  args: { text: v.string(), authorId: v.string() },
  handler: async (ctx, args) => {
    await ctx.db.insert('tasks', {
      text: args.text,
      isCompleted: false,
      authorId: args.authorId,
    });
  },
});

On the React side, useQuery subscribes the component to the result. Convex tracks dependencies, reruns the affected query, and sends the new value over WebSocket:

import { useQuery } from 'convex/react';
import { api } from './_generated/api';

function TaskList({ userId }) {
  const tasks = useQuery(api.tasks.list, { authorId: userId });

  if (tasks === undefined) return <Spinner />;
  return (
    <ul>
      {tasks.map((task) => (
        <li key={task._id}>{task.text}</li>
      ))}
    </ul>
  );
}

What are Convex’s strengths and limitations?

• End-to-end typing across schema, functions, and client.
• Transactional mutations with serializable isolation and optimistic concurrency control.
• Reactive queries, full-text search, vector search, scheduling, and server actions.
• Managed cloud or self-hosting with the open-source backend.
• FSL Apache 2.0 license: it permits many uses but prohibits building a product that competes with Convex Cloud before the code automatically converts to Apache 2.0.

Official self-hosting is available, but the managed service remains the simplest path. Your application code also depends on Convex primitives, which makes moving to a conventional database and API more expensive than merely exporting the data.

How does Pylon work?

Pylon puts the database, API, authentication, real-time connections, and TypeScript functions behind a Rust server. It uses SQLite by default or PostgreSQL through DATABASE_URL. Bun runs TypeScript functions and React server rendering.

// apps/api/app.ts
import { entity, field, policy, buildManifest } from '@pylonsync/sdk';

const Note = entity(
  'Note',
  {
    title: field.string(),
    body: field.richtext(),
    authorId: field.string(),
    updatedAt: field.datetime(),
  },
  {
    indexes: [{ name: 'by_author', fields: ['authorId'], unique: false }],
  },
);

const notePolicy = policy({
  name: 'note_public',
  entity: 'Note',
  allowRead: 'true',
  allowInsert: 'auth.userId == data.authorId',
  allowUpdate: 'auth.userId == data.authorId',
  allowDelete: 'auth.userId == data.authorId',
});

export default buildManifest({
  name: 'notes',
  version: '0.1.0',
  entities: [Note],
  policies: [notePolicy],
  queries: [],
  actions: [],
  routes: [],
});

A server function lives in functions/:

// apps/api/functions/createNote.ts
import { mutation, v } from '@pylonsync/functions';

export default mutation({
  args: { title: v.string(), body: v.string() },
  async handler(ctx, args) {
    const id = await ctx.db.insert('Note', {
      title: args.title,
      body: args.body,
      authorId: ctx.auth.userId,
      updatedAt: new Date().toISOString(),
    });
    return { id };
  },
});

The React client can then subscribe to a reactive function:

import { db } from '@pylonsync/react';

function Feed({ userId }) {
  const { data: notes, loading, error } = db.useReactiveQuery('getNotes', { userId });

  if (loading) return <Spinner />;
  if (error) return <ErrorBanner error={error} />;
  return notes.map((note) => <NoteCard key={note.id} note={note} />);
}

What are Pylon’s strengths and limitations?

• Unified Rust server with Bun for TypeScript functions and SSR.
• SQLite or PostgreSQL, without a required proprietary persistence engine.
• Row-level access policies declared near the schema.
• Reactive queries, real-time synchronization, authentication, and file storage built in.
• MIT or Apache 2.0 licenses for the main repository.

Pylon gives you more infrastructure control, but its project and ecosystem are younger. You should therefore validate operations, integrations, and reactive-query limits carefully against your real workload.

Convex or Pylon: what are the differences?

Criterion	Convex	Pylon
Primary experience	Reactive backend with managed cloud	Self-hostable full-stack framework
Persistence	Convex service; SQLite, PostgreSQL, or MySQL in open source	SQLite or PostgreSQL
Real-time model	Reactive queries and synchronized clients	Reactive queries, live queries, and synchronization
Deployment	Convex Cloud or self-hosting	Binary or project dependency, Docker, or cloud
Backend license	FSL Apache 2.0, then Apache 2.0 after its specified delay	MIT or Apache 2.0
Maturity	More established ecosystem and documentation	Newer project with a broader full-stack scope

Convex is the safer choice if you primarily want an integrated developer experience and a service operated for you. Pylon becomes compelling when you want to control the database, server, and deployment with standard components.

Does a reactive backend create vendor lock-in?

Yes, partially. Lock-in does not come from storage alone. It mostly comes from each platform’s server functions, client hooks, access rules, and query model.

Convex reduces operational risk through ZIP or continuous data exports and its self-hostable backend. Pylon limits persistence lock-in by using SQLite or PostgreSQL and publishing its server under the MIT or Apache 2.0 licenses.

Before choosing, check three exit paths:

1. Can you export data and files in a usable format?
2. Can you run the backend without the vendor’s cloud?
3. How much logic would you have to rewrite to return to a standard API?

When should you choose Convex or Pylon?

Choose Convex if:

• you want to ship quickly with a managed cloud;
• typing and React integration matter most;
• you need vector search or scheduled functions;
• the FSL restriction does not conflict with your business model.

Choose Pylon if:

• you want control over hosting and the database;
• SQLite or PostgreSQL already fit your operations;
• an MIT or Apache 2.0 license is decisive;
• you accept evaluating a newer ecosystem.

For a mostly transactional and minimally interactive public API, keep a conventional backend as an option. Reactivity provides the most value when several clients must display the same state almost immediately.

Conclusion

Convex and Pylon make real-time updates more declarative: you write the read operation, and the system keeps subscribers up to date. This abstraction removes a significant amount of synchronization code, but it also moves more responsibility into the framework.

I recommend Convex by default for launching a reactive product, thanks to its integrated experience and managed option. I would choose Pylon for a project where self-hosting, SQLite or PostgreSQL, and licensing freedom are non-negotiable. In either case, test one representative feature first and document your exit strategy.

Sources

• Convex overview
• Convex self-hosting and license
• Convex data export
• Introduction to Pylon
• Pylon reactive queries
• Official Pylon repository