altor-vec vs qdrant

altor-vec vs Qdrant — Browser WASM vs Rust Vector Database

Qdrant is a high-performance vector database written in Rust, designed for server-side retrieval with payload filtering and production-grade reliability. altor-vec is a 54KB WASM library that runs HNSW vector search entirely in the browser. Both use HNSW indexing — but they serve fundamentally different deployment contexts.

Feature comparison

Code comparison

altor-vec (browser, no server)

import init, { WasmSearchEngine } from 'altor-vec';

await init();
const engine = WasmSearchEngine.from_vectors(embeddedVectors, 384, 16, 200, 50);
const hits = JSON.parse(engine.search(queryEmbedding, 5));
// Results in <1ms, no network

Qdrant (Node.js, server required)

import { QdrantClient } from '@qdrant/js-client-rest';

const client = new QdrantClient({ url: 'http://localhost:6333' });
const results = await client.search('my_collection', {
  vector: queryEmbedding,
  limit: 5,
  filter: { must: [{ key: 'category', match: { value: 'docs' } }] },
});

Both use HNSW under the hood, but the deployment model is completely different. altor-vec bundles the index as a WASM asset that ships with your frontend. Qdrant runs as a persistent service that your application calls over HTTP or gRPC. For search that needs to work offline, stay in the browser, or avoid backend infrastructure costs, altor-vec is the right architecture. For search that needs to be shared across services, requires complex filtering, or must handle continuous writes, Qdrant fits better.

The HNSW connection

Both altor-vec and Qdrant use HNSW (Hierarchical Navigable Small World) indexing, which is why they appear together in searches for "best vector search library." HNSW gives near-perfect recall with sub-linear query time. The difference is where the graph lives: in your browser's WASM runtime with altor-vec, or in a Rust process on your server with Qdrant.

When to choose each

Choose altor-vec when: your search is a frontend feature, data is public or device-local, you want offline capability, or you need zero-cost vector search without infrastructure management.

Choose Qdrant when: you need server-side vector retrieval, rich payload filtering (category, date range, metadata), billions of vectors, or search as shared infrastructure across multiple services.