cloud vs browser
altor-vec vs Algolia
Free semantic browser search vs $1/1K operations SaaS. When keyword+neural cloud search justifies the cost.
vector search comparison guide 2026
altor-vec vs All Alternatives
altor-vec is a JavaScript library for semantic vector search that runs entirely in the browser via a 54KB WebAssembly module — no server, no API keys, no per-query cost. This hub compares it honestly against every major vector search alternative.
The core question: Does your search need to run in a browser with no backend, or do you have server infrastructure and need billion-scale retrieval? That single decision determines the right tool.
Master comparison table
| Tool | Runs in browser | Cost model | Max scale | Search type | Language |
|---|---|---|---|---|---|
| altor-vec this | ✅ Yes (54KB WASM) | Free forever | ~100K vectors | Semantic (HNSW) | JavaScript/TypeScript |
| Pinecone | ❌ Cloud only | $0.096/1M reads | Billions | Vector ANN | Python, JS, Go |
| Algolia | ❌ SaaS API | ~$1/1K operations | Billions | Keyword + neural | Any (REST) |
| Weaviate | ❌ Server/cloud | OSS + Cloud pricing | Billions | Vector + hybrid | Python, Go, JS |
| ChromaDB | ❌ Server required | Free (self-host) | Millions | Vector + metadata | Python-first |
| FAISS | ❌ Python/C++ only | Free (self-host) | Billions | Vector ANN | Python, C++ |
| Typesense | ❌ Server required | Free + Typesense Cloud | Millions | Keyword + vector | Any (REST) |
| Meilisearch | ❌ Server required | Free + Meilisearch Cloud | Millions | Full-text + semantic | Any (REST) |
| Qdrant | ❌ Server required | Free + Qdrant Cloud | Billions | Vector + filtering | Python, JS, Rust |
| Milvus | ❌ Kubernetes | Free (self-host) | Billions | Vector ANN | Python, Java, Go |
| HNSWlib | ❌ Python/C++ only | Free (self-host) | Millions | HNSW ANN | Python, C++ |
| Fuse.js | ✅ Browser-native | Free (MIT) | ~100K | Fuzzy text (Bitap) | JavaScript |
When altor-vec wins
altor-vec is the clear choice when:
- Search must run in the browser — your app is a static site, PWA, or you want zero server costs for search
- Privacy is a requirement — the data being searched must never leave the user's device (HIPAA apps, personal documents, private notes)
- You need offline-first search — altor-vec indexes can be serialized and cached in localStorage or IndexedDB
- You want zero billing — no per-query cost, no API key management, no infrastructure to maintain
- Your dataset is under ~100K documents — documentation sites, product catalogs, knowledge bases, chat history
- Fast iteration matters — ship search as a 54KB npm package with no backend to deploy or manage
When cloud databases win
Choose a cloud vector database when:
- Scale exceeds 100K vectors — Pinecone, Weaviate, and Milvus are purpose-built for billion-scale retrieval
- Multiple users share one index — a centralized server is the right architecture for shared data
- You need real-time writes — cloud databases handle concurrent writes natively; browser indexes work best with batch updates on deploy
- Complex metadata filtering is required — Qdrant and Weaviate have sophisticated filter query languages
- Server-side RAG pipelines — if your LLM and retrieval both run server-side, a server-side vector database is the right tool
Individual comparison pages
cloud vs browser
altor-vec vs Pinecone
Zero-cost browser HNSW vs $0.096/1M read managed cloud. The definitive client-side vs cloud comparison.
server vs browser
altor-vec vs Weaviate
54KB WASM vs cloud-native AI database. Multi-modal, GraphQL, Docker — when you need more than altor-vec.
python vs javascript
altor-vec vs ChromaDB
Browser-native JS vs Python-first AI vector DB. Which fits your RAG application architecture?
python vs javascript
altor-vec vs FAISS
54KB browser WASM vs C++/Python FAISS. Can JavaScript vector search match FAISS performance?
semantic vs full-text
altor-vec vs Meilisearch
Embedding-based semantic search vs full-text typo-tolerant search. Different problems, different tools.
server vs browser
altor-vec vs Typesense
No-server browser search vs self-hosted search server. The server requirement is the key differentiator.
production filtering
altor-vec vs Qdrant
Lightweight browser HNSW vs Rust-powered vector DB with advanced payload filtering for production.
billion scale
altor-vec vs Milvus
Browser-scale search vs billion-scale distributed vector engine. Right tool for each magnitude of data.
same algorithm
altor-vec vs HNSWlib
Both use HNSW. altor-vec ships as browser WASM. HNSWlib requires Python/C++ bindings. Why it matters.
altor-vec vs Fuse.js
Fuse.js does fuzzy text matching (42M downloads/month). altor-vec does semantic vector similarity — "cancel subscription" finds "end your plan". Different tools for different jobs.
Quick decision guide
| Your situation | Best choice |
|---|---|
| Static docs site or PWA needing semantic search | altor-vec |
| Privacy: data must never leave the device | altor-vec |
| Under 100K documents, no server budget | altor-vec |
| Need keyword + typo tolerance + faceting for ecommerce | Algolia or Typesense |
| Server-side RAG pipeline in Python | ChromaDB or Qdrant |
| Billion-scale retrieval with Kubernetes budget | Milvus or Pinecone |
| Multi-modal search with auto-vectorization | Weaviate |
| Need FAISS performance in Python/C++ | FAISS or HNSWlib |
Try altor-vec:
npm install altor-vec — MIT licensed, 54KB WASM, works in any browser. See docs and live demo →