Convex: The Reactive Backend Platform for TypeScript Apps

Convex: The Reactive Backend Platform for TypeScript Apps

Quick Answer

Convex is a software-defined reactive backend platform that combines database, API layer, cache, and state manager into a unified TypeScript system. It automatically keeps your app in sync through real-time subscriptions, includes built-in vector search for AI applications, and offers ACID transactions with optimistic concurrency control.

What is Convex?

Convex represents a paradigm shift from traditional backend architectures. Instead of maintaining separate layers—a relational database, an in-memory cache, a search engine, and a WebSocket server—Convex consolidates all of these into a single platform managed through TypeScript code.

Software-Defined Database Philosophy

Convex inverts the traditional application architecture by treating infrastructure decisions as application code:

• Isomorphic TypeScript: Backend logic written in TypeScript with full type inference
• Transactional Integrity: ACID-compliant transactions with optimistic concurrency
• Eliminated ORM Layer: Direct database access without translation overhead
• Reactive by Default: Every query is implicitly a live subscription

Deterministic Execution Model

Convex functions run in a custom V8-based JavaScript runtime that ensures determinism:

• Frozen Time: Multiple calls to Date.now() return the same timestamp within a transaction
• Seeded Randomness: Math.random() uses a seeded PRNG for retry-safe execution
• V8 Isolates: Lightweight execution contexts eliminate cold start latency

Key Features

Reactive Queries & Subscriptions

Convex’s reactivity is automatic. When using useQuery in React:

function UserProfile({ userId }) {
  const user = useQuery(api.users.get, { userId });
  // UI updates automatically when database changes
}

The system tracks dependencies (“read set”) and pushes updates via WebSocket whenever relevant data changes.

Vector Search for AI

Native Vector Indexing:

• HNSW algorithm for Approximate Nearest Neighbor search
• 2 to 4096 dimensions supported
• Cosine similarity metric
• Metadata filtering for multi-tenant search

RAG Workflow Integration:

// ctx.vectorSearch available in Actions
const results = await ctx.vectorSearch("documents", {
  vector: embedding,
  filter: q.eq("userId", user.id),
  limit: 10
});

Transaction Model

Function Type	Purpose	Timeout
Queries	Pure read functions, reactive	~1 second CPU
Mutations	ACID write operations	~1 second CPU
Actions	Side effects & external APIs	Up to 10 minutes

Data Types & Validation

Convex supports a rich type system with runtime validation:

Type	Validator	JavaScript Equivalent
ID	v.id("tableName")	Id<"tableName"> (String)
String	v.string()	string (UTF-8)
Float64	v.number()	number (IEEE 754)
Int64	v.int64()	bigint
Boolean	v.boolean()	true/false
Bytes	v.bytes()	ArrayBuffer
Array	v.array(T)	Array<T>
Object	v.object({...})	Record<string, any>

Convex vs Competitors

Convex vs Supabase

Feature	Convex	Supabase
Core Engine	Custom Reactive Database (Rust/V8)	PostgreSQL
Query Language	TypeScript / JavaScript	SQL / PostgREST
Reactivity	Ubiquitous (default)	Opt-in (Postgres replication)
Logic Layer	Integrated “Software-Defined” Backend	Edge Functions / DB Functions
Migration	Schema-as-Code (TypeScript)	SQL Migrations (Flyway/CLI)

Convex vs Firebase

Feature	Convex	Firebase
Type Safety	TypeScript-native	Manual type casting required
Query Power	Full JavaScript logic	Restricted OR queries, rigid sorting
Consistency	ACID transactions with OCC	Eventual consistency issues
Cost Predictability	Function calls based	Reads-based billing

Convex vs Traditional Stack (Prisma/Drizzle)

Feature	Convex	Prisma/Drizzle + PostgreSQL
Maintenance	Single platform	DB + API + WebSocket server
Type Safety	End-to-end inferred	Generated types (Prisma) or inferred (Drizzle)
Real-time	Built-in subscriptions	Requires separate implementation
Migration	Harder from coupled logic	Easier (own the SQL schema)

Client Ecosystem

Web (React, Next.js, TanStack Start)

• React Hooks: useQuery, useMutation with automatic connection management
• Next.js: Supports SSR preloading with client-side live updates
• TanStack Start: Deep integration for React Router ecosystem

Mobile Support

iOS (Swift):

• Official convex-swift library with native concurrency
• SwiftUI integration via .task modifier
• AsyncSequence for reactive updates

Android (Kotlin):

• Official android-convexmobile library
• Kotlin Flow integration with Jetpack Compose

Flutter Gap:

• No official Flutter SDK as of 2026
• Community packages available via FFI wrapper
• Consider alternatives like Appwrite or Firebase for Flutter-first teams

Deployment Models

Convex Cloud:

• Fully managed PaaS with auto-scaling
• 11 9’s durability via AWS RDS
• Global distribution and dashboard

Self-Hosted (Open Source):

• Apache 2.0 licensed backend
• Docker deployment for data sovereignty
• Requires manual scaling and monitoring

Use Cases: Best For

• Collaborative applications: Real-time sync with automatic conflict resolution
• AI-native apps: Built-in vector search with RAG workflow support
• Rapid prototyping: Zero backend boilerplate, schema-as-code
• TypeScript teams: End-to-end type safety across the stack
• Multi-player games: Low-latency state synchronization

Use Cases: Avoid For

• Non-TypeScript projects: TypeScript is required for the backend logic
• SQL-heavy analytics: Not optimized for complex analytical queries
• Legacy integrations: Direct SQL access not available
• Flutter-first projects: No official SDK (consider Supabase instead)

Pricing Structure (2026)

Tier	Monthly Cost	Included Function Calls	Storage	Overage
Starter	Free	1 Million	0.5 GB	N/A (Hard stop)
Professional	$25/seat	25 Million	50 GB	$2 per 1M calls
Overages	-	-	-	$0.20 per GB

Cost Considerations

• Fan-out multiplier: Each reactive update to 1,000 users = 1,000 function calls
• Vector storage: More expensive than standard storage ($0.50/GB)
• Action compute: Charged separately per GB-hour of execution

Operational Limits

Limit	Value
Transaction size	16 MiB
Document size	1 MiB
Max array elements	8,192 per document
Nesting depth	16 levels
Max vector indexes	4 per table
Max indexes per table	32

Development Workflow

Schema Definition

import { defineSchema, defineTable } from "convex/server";

export default defineSchema({
  users: defineTable({
    name: v.string(),
    email: v.string(),
    active: v.boolean().default(true),
  })
    .index("by_email", ["email"])
    .index("by_active", ["active"]),
});

Query Function

import { query } from "./_generated/server";
import { mutation } from "./_generated/server";

export const getUser = query({
  args: { id: v.id("users") },
  handler: async (ctx, args) => {
    return await ctx.db.get(args.id);
  },
});

Mutation Function

export const updateUser = mutation({
  args: { id: v.id("users"), name: v.string() },
  handler: async (ctx, args) => {
    return await ctx.db.patch(args.id, { name: args.name });
  },
});

FAQ

Is Convex open source?

Yes, the Convex backend is open-source under the Apache 2.0 license. The managed cloud service offers additional features like dashboard and auto-scaling.

Does Convex work with Flutter?

There is no official Flutter SDK. Community packages exist via Rust FFI but lack official support. Consider Supabase or Firebase for Flutter projects.

Can I use Convex for mobile apps?

Yes, Convex has official SDKs for iOS (Swift) and Android (Kotlin). Mobile apps connect via HTTP/WebSocket, maintaining real-time sync.

How does Convex handle offline mode?

Convex clients support optimistic updates for immediate UI feedback. When the device reconnects, changes are synchronized and conflicts are resolved automatically.

Does Convex support SQL?

No, Convex uses TypeScript functions for data access rather than SQL. This provides type safety but requires learning the Convex query API.

---

Related Technologies: Supabase, Firebase, Prisma, Drizzle ORM, PlanetScale, Pinecone