Vector Search Comparison · 2026

altor-vec vs Pinecone: Client-Side vs Cloud Vector Search

The choice between altor-vec and Pinecone is not about which is better — it is about deployment boundary. altor-vec runs in the browser with no server required. Pinecone assumes a server and gives you the controls that environment needs. Confusing those boundaries leads to either overbuilding a simple public search surface or underbuilding a private, business-critical retrieval pipeline.

When to Use altor-vec

altor-vec wins when these conditions are true:

• Public or semi-public corpus — documentation, blog posts, product catalog, FAQ content that does not require access control
• Corpus size under ~200K vectors — HNSW in the browser scales well up to this range; above it, browser memory limits become a constraint
• Static or batch-updated index — the index is rebuilt on deployment or on a schedule, not updated continuously with individual writes
• Zero backend budget or complexity — no API key management, no per-query billing, no server to maintain
• Offline or edge use cases — PWAs, offline-first apps, or edge functions where a network call to a vector DB adds unacceptable latency
• Privacy-first requirements — user queries never leave the browser; no server logs the search

When to Use Pinecone

Pinecone wins when these conditions are true:

• Large private corpus — proprietary documents, customer data, or internal knowledge bases that should never be bundled into a client asset
• Corpus over ~200K vectors — or rapidly growing, where rebuilding and redeploying a full index on every update is impractical
• Continuous writes — new vectors are added frequently and search results must reflect the latest state without a full rebuild
• Multi-user shared index — many users querying the same vector store simultaneously, with server-side access control
• Strong access control required — role-based permissions, audit logs, SOC 2 compliance

Feature Comparison

Code Comparison

altor-vec (browser, zero backend)

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

await init();
const engine = new WasmSearchEngine(384, 'cosine');

// Build index from your embeddings (Float32Array per vector)
for (const [id, embedding] of vectors) {
  engine.add(id, new Float32Array(embedding));
}

// Query — runs locally, sub-millisecond
const results = engine.search(new Float32Array(queryEmbedding), 5);
// results: [{ id, score }, ...]

Pinecone (cloud, server-side)

import { Pinecone } from '@pinecone-database/pinecone';

const pc = new Pinecone({ apiKey: process.env.PINECONE_API_KEY });
const index = pc.index('my-index');

// Upsert vectors (server-side only)
await index.upsert([{ id: 'doc1', values: embedding, metadata: { text } }]);

// Query (requires network round-trip)
const result = await index.query({
  vector: queryEmbedding,
  topK: 5,
  includeMetadata: true,
});

Migration: Pinecone → altor-vec

Migrating makes sense when your corpus is static, public, and under ~200K vectors. The steps:

1. Export vectors from Pinecone using index.fetch(ids) or a full export script
2. Convert each vector to a Float32Array and store alongside your document metadata as JSON
3. Build the altor-vec HNSW index at build time (or in a Web Worker at first load)
4. Serialize the index to a binary file and serve it as a static asset
5. Load and query client-side — no API key, no network call

// Build script (Node.js, runs at deploy time)
import init, { WasmSearchEngine } from 'altor-vec';
import fs from 'fs';

await init();
const engine = new WasmSearchEngine(384, 'cosine');

const corpus = JSON.parse(fs.readFileSync('./vectors.json', 'utf8'));
for (const { id, embedding } of corpus) {
  engine.add(id, new Float32Array(embedding));
}

// Serialize and write to public/
const bytes = engine.serialize();
fs.writeFileSync('./public/search-index.bin', Buffer.from(bytes));

Can They Coexist?

Yes — and this is a common production pattern. Teams use altor-vec for public-facing documentation or product search (zero cost, offline capable, privacy-preserving) and Pinecone for internal RAG pipelines over private documents (continuous updates, access control, large scale).

Frequently Asked Questions

Is altor-vec better than Pinecone?

altor-vec is better for browser-native, zero-infra search over public corpora under ~200K vectors. Pinecone is better for large private corpora with continuous writes and multi-user access control. Neither is universally better.

Can altor-vec replace Pinecone?

For browser-native and edge use cases with public data, yes. For large-scale private retrieval with continuous writes and access control, no. Many teams use both in the same product.

How do I migrate from Pinecone to altor-vec?

Export your Pinecone vectors, convert to Float32Array per vector, build an altor-vec HNSW index at build time, serialize to a binary file, and serve as a static asset. Full example above.