altor-vec vs weaviate

altor-vec vs Weaviate — When You Don't Need a Server

A fair comparison between altor-vec and Weaviate starts with deployment boundaries, not hype. altor-vec is built for browser-native HNSW retrieval with almost no operational overhead. Weaviate assumes a server, service, or native runtime and gives you the controls that environment usually needs. If your product team confuses those boundaries, it will either overbuild for a simple public search surface or underbuild for a private, business-critical retrieval workflow.

Feature comparison table

The table shows why these tools often appear in the same shortlist even though they are not direct drop-in substitutes. altor-vec is strongest when search should be bundled into the application and shipped like any other static asset. Weaviate is strongest when search is shared infrastructure with its own mutation path, observability, and security rules. Teams usually get the best outcome when they admit that those are materially different jobs.

Code comparison

altor-vec

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

await init();
const dim = 4;
const vectors = new Float32Array([
  1, 0, 0, 0,
  0, 1, 0, 0,
  0, 0, 1, 0,
]);
const engine = WasmSearchEngine.from_vectors(vectors, dim, 16, 200, 50);
const hits = JSON.parse(engine.search(new Float32Array([0.95, 0.05, 0, 0]), 3));

Weaviate

import weaviate from 'weaviate-client';

const client = await weaviate.connectToLocal();
const response = await client.graphql.get()
  .withClassName('Doc')
  .withFields('title body _additional { distance }')
  .withNearVector({ vector: queryVector })
  .withLimit(3)
  .do();

The syntax difference mirrors the architecture. With altor-vec, you initialize WASM, create or load a local index, and search with a Float32Array. With Weaviate, you usually authenticate to a service or rely on a backend process, then route your query through that environment. That adds network or runtime boundaries, but it also enables central governance and shared datasets. The “better” option depends on whether your search feature is fundamentally a frontend capability or a backend platform concern.

When to choose each

Choose altor-vec when:

• Your search feature is a frontend concern — public documentation, product catalogs, knowledge bases, or changelogs that ship as static assets alongside your JavaScript bundle.
• You need zero infrastructure: no Docker, no cloud accounts, no API keys. altor-vec works immediately after npm install with no ongoing operational overhead.
• Data privacy on the client side matters — all vectors and documents stay in the user's browser memory and never touch a server.

Choose Weaviate when:

• You need a centralized AI database with GraphQL APIs, auto-vectorization modules, hybrid BM25 + vector search, and multi-modal retrieval across text, images, and audio.
• Your data is private, sensitive, or enterprise-grade — Weaviate provides access control, role-based permissions, and audit logging that browser-local search cannot offer.
• You require continuous writes, schema enforcement, and shared vector infrastructure consumed by multiple backend services or AI agents simultaneously.

A hybrid model is common and healthy. Many teams keep browser-local semantic search for public docs, changelogs, release notes, or lightweight catalogs while using Weaviate for protected corpora, shared AI services, or complex operational search. That split respects the strengths of both systems instead of forcing everything into one stack just for conceptual purity.

Performance characteristics

altor-vec and Weaviate have fundamentally different performance profiles shaped by their deployment models. altor-vec runs HNSW in WebAssembly inside the browser's JS runtime, delivering sub-millisecond query latency because there is no network round-trip. Index construction is done once at build time and shipped as a binary asset. The practical corpus limit is roughly 50K–100K vectors depending on available browser memory and embedding dimensionality. Resource consumption is entirely client-side — compute and memory costs are paid by the user's device, not your infrastructure.

Weaviate is engineered for server-scale performance. A single Weaviate node handles millions of vectors with HNSW or flat index options, and a distributed Weaviate cluster scales to billions. Query latency is typically 1–20ms server-side, plus network round-trip overhead. Weaviate also supports on-disk indexes to handle datasets that exceed available RAM, and its ACORN hybrid search implementation combines BM25 keyword scoring with vector similarity in a single low-latency pass. For write-heavy workloads with continuous ingestion, Weaviate's server architecture handles concurrent writes that browser-local indexes simply cannot support without a full page reload to rebuild the index from a new asset.

Frequently asked questions

What is Weaviate and how does it differ from altor-vec?

Weaviate is an open-source, cloud-native AI vector database with a GraphQL API, auto-vectorization modules, and support for multi-modal data including text, images, and audio. It requires Docker, Kubernetes, or Weaviate Cloud to run and is designed for production AI backends. altor-vec is a 54KB WebAssembly library that runs HNSW vector search entirely inside the browser with no server, no config, and no deployment overhead.

Can altor-vec replace Weaviate for production AI applications?

It depends on the use case. altor-vec is production-ready for browser-based semantic search over public datasets up to roughly 100K vectors — documentation search, product catalogs, knowledge bases. Weaviate is purpose-built for production AI backends that require centralized vector storage, access control, continuous writes, and shared retrieval across multiple services. The two tools occupy different layers of the stack.

Does altor-vec support multi-modal search like Weaviate?

Weaviate supports multi-modal search natively through its vectorizer modules, enabling retrieval across text, images, and other modalities in a single query pipeline. altor-vec works with any embedding vector you provide, so multi-modal search is technically possible if your embedding model produces a unified vector space — but altor-vec itself ships no built-in vectorizer modules or image encoders.

Does altor-vec support hybrid search like Weaviate?

Weaviate has built-in hybrid search combining dense vector retrieval with BM25 keyword scoring. altor-vec focuses on pure HNSW vector search; hybrid behaviour can be composed in application code by merging keyword and vector results client-side, but this is a manual implementation compared to Weaviate's first-class hybrid search support.

Which tool is easier to deploy?

altor-vec requires only npm install altor-vec and ships as a standard WASM asset alongside your frontend — zero additional infrastructure. Weaviate requires Docker or Weaviate Cloud, schema configuration, and ongoing service management. For teams without dedicated DevOps resources, altor-vec's zero-ops model is significantly simpler and cheaper to maintain.

Is Weaviate open source?

Yes, Weaviate is open source under the BSD-3-Clause license. You can self-host it using Docker Compose or Kubernetes. Weaviate also offers Weaviate Cloud, a managed SaaS version with a free sandbox tier and paid plans for production use. altor-vec is also open source and free to use with no cloud dependency whatsoever.

Operational notes

• Index updates: client-side indexes are best when updates happen on deploys or controlled sync jobs.
• Observability: backend systems centralize logs naturally; browser search needs deliberate product instrumentation.
• Security boundary: if the browser should not know the data, browser-local search is not the source of truth.
• Cost model: local search shifts cost into build-time assets and client compute, while backend systems shift cost into infrastructure and query volume.

Another practical difference is ownership. Frontend teams can usually ship altor-vec with existing static deployment infrastructure. Weaviate often pulls search into platform, DevOps, or backend ownership. That is not a downside when the product genuinely needs central control, but it is unnecessary drag when all you wanted was better semantic retrieval over public content.

Bottom line

Use altor-vec when semantic retrieval belongs inside the interface and the browser is allowed to hold the index. Use Weaviate when search is a centralized system with private data, fast-changing writes, or operational requirements that the browser should not carry. That is the honest comparison axis, and it is the one that usually leads to the right architecture.