benchmark comparison
altor-vec vs ChromaDB
Application database for embeddings versus static browser retrieval.
ChromaDB is often chosen as an application-side store for embeddings and metadata, while altor-vec is closer to a shipped frontend primitive. Comparing them usefully means being honest about that mismatch.
Comparison table
| Category | altor-vec | ChromaDB |
|---|---|---|
| Runtime model | Local browser ANN runtime. | Server-side application database or local dev database workflow. |
| Bundle size / delivery | ~54KB gzipped plus index asset. | No browser ANN payload, but the app depends on a running service or server environment. |
| Query latency | Sub-millisecond local lookup after load. | Server or local process latency plus request overhead depending on deployment model. |
| Memory usage | Client memory consumed by shipped vectors. | Memory and storage handled in the application backend or local environment. |
| Features | ANN retrieval and serialization. | Collections, metadata, persistence, and app-database style workflows. |
| Dataset sweet spot | Best for static corpora that belong in the shipped frontend. | Better for mutable app datasets and server-managed retrieval. |
Where altor-vec wins
- No search server to run or pay for.
- Excellent fit for embedded widgets, docs, and offline experiences.
- Queries can stay fully local.
Where ChromaDB wins
- Much better fit for mutable datasets and application storage patterns.
- More natural for backend RAG stacks.
- Easier to centralize data instead of shipping it.
Honest decision guide
ChromaDB is stronger as an application data layer. altor-vec is stronger as a browser-delivered retrieval primitive.
The honest pattern across all of these benchmark pages is simple: if the search corpus should stay on the server, choose server-oriented infrastructure. If the search corpus is intentionally shipped with the product and the UX benefit of local retrieval matters more than backend scale, altor-vec is usually the more natural fit.
FAQ
Can altor-vec replace ChromaDB in a backend RAG system?
Usually no. ChromaDB is better suited to server-managed mutable datasets and backend workflows.
When is altor-vec clearly better?
When the corpus is safe to ship and the retrieval experience belongs directly in the frontend.
Why compare them at all?
Because teams sometimes conflate 'vector search tool' categories even though the operational models are very different.
Get started: npm install altor-vec · GitHub