ParqDB
ParqDB is a vector search engine that operates entirely within the browser using WebAssembly. It eliminates the need for a dedicated query server by leveraging HTTP range requests to read only the necessary byte ranges from Parquet files stored in object storage. The tool builds IVF-LVQ8 indexes over embeddings, such as MiniLM, and publishes them as immutable Parquet files. Queries are embedded locally in the browser, and results are ranked using LVQ8 distance and bounded top-k. This architecture ensures that query text never leaves the user's tab, providing privacy and reducing infrastructure costs. ParqDB is open-source and available on GitHub, making it suitable for developers and agencies looking to integrate semantic search into their applications without backend overhead.
ParqDB is a vector search engine, integrating with GitHub, ONNX, WebAssembly, and Parquet. InnovaAI scores it 3.8/10 for agency resale.
Agency Audit
ParqDB offers a novel approach to vector search by running entirely in the browser, which can significantly reduce infrastructure costs and improve privacy. It is particularly valuable for agencies building search features for clients who prioritize data privacy or have limited backend resources. However, it requires technical expertise to set up and may not be suitable for all use cases.
Team size not published
Team size not published
Team size not published
Not published
Illustrative scenario. Not a guarantee. Net capacity needs a verified paid base plan, and none is published for this service, so it is not modeled. Hours saved come from the service estimate; implementation, taxes, and unprovided usage charges are excluded.
Adoption signals available after Phase 2
- You need real-time index updates
- You require complex query features like filtering or aggregations
- You lack technical expertise in vector databases or WASM
Internal Adoption Path
No paid plan published
Team size not published
Team size not published
Team size not published
Illustrative scenario. Not a guarantee. No verified paid base plan is published for this service, so subscription cost and net capacity are not modeled. Implementation, taxes, and unprovided usage charges are excluded.
AI Tool Overview
- You need to provide privacy-preserving search to clients
- You want to reduce infrastructure costs for search features
- You are building a search application for large document corpora
- You need real-time index updates
- You require complex query features like filtering or aggregations
- You lack technical expertise in vector databases or WASM
Platform Features
Core capabilities of ParqDB
Browser-based vector search
UniqueRuns semantic search entirely in the browser using WebAssembly, eliminating the need for a query server.
HTTP range querying
UniqueReads only the required byte ranges from Parquet files in object storage, reducing data transfer.
IVF-LVQ8 indexing
UniqueBuilds efficient IVF-LVQ8 indexes over embeddings for fast approximate nearest neighbor search.
On-device embedding
UniqueEmbeds query text into 384-dimensional vectors using ONNX/WASM, keeping data local.
Query profiler
Provides detailed metrics on requests, memory hits, transferred bytes, and query time.
What Makes ParqDB Different
Unique advantages vs similar tools in this niche
Eliminates query server infrastructure
vs Traditional vector databases like Pinecone or WeaviateParqDB runs entirely in the browser, so there is no server to maintain or pay for.
Privacy-preserving search
vs Cloud-based vector search servicesQuery text never leaves the user's tab, ensuring data privacy.
Cost-effective scaling
vs Server-based vector databasesOnly object storage is needed, reducing costs for large-scale deployments.
Frequently Asked Questions
Answers about setup, alternatives
ParqDB integrates natively with GitHub, ONNX, WebAssembly, Parquet, HTTP Range, MiniLM. These integrations enable agencies to connect ParqDB into existing client workflows without custom development.
ParqDB's most distinctive features include: Browser-based vector search (Runs semantic search entirely in the browser using WebAssembly, eliminating the need for a query server.); HTTP range querying (Reads only the required byte ranges from Parquet files in object storage, reducing data transfer.); IVF-LVQ8 indexing (Builds efficient IVF-LVQ8 indexes over embeddings for fast approximate nearest neighbor search.). These capabilities differentiate ParqDB from alternatives and create unique value for agency clients.
ParqDB's key advantage over Traditional vector databases like Pinecone or Weaviate: Eliminates query server infrastructure. ParqDB runs entirely in the browser, so there is no server to maintain or pay for. Overall: ParqDB offers a novel approach to vector search by running entirely in the browser, which can significantly reduce infrastructure costs and improve privacy. It is particularly valuable for agencies building search features for clients who prioritize data privacy or have limited backend resources. However, it requires technical expertise to set up and may not be suitable for all use cases.
ParqDB is a strong fit if: You need to provide privacy-preserving search to clients; You want to reduce infrastructure costs for search features; You are building a search application for large document corpora. Consider alternatives if: You need real-time index updates; You require complex query features like filtering or aggregations; You lack technical expertise in vector databases or WASM. Key trade-off: The main trade-off is the complexity of building and publishing the index, which requires technical knowledge of vector databases and WASM, versus the benefit of eliminating server costs.
Pricing
Pricing data not yet available for ParqDB.