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Up to 4 tools, same rows for each: what it is, what it really costs, the trap, and how to wire it up. The URL carries your picks — send it to anyone.
QdrantFuse.jsMiniSearch
Quick read
- Free with no card: Fuse.js, MiniSearch.
| Field | Qdrantqdrant.tech | Fuse.jsfusejs.io | MiniSearchgithub.com |
|---|---|---|---|
| What it is | A Rust vector database with the strongest payload filtering in the category — combine vector similarity with keyword, full-text, numeric-range, geo and boolean conditions in one query. Supports hybrid search with reciprocal rank fusion, sharding, multitenancy, GPU indexing, and quantization that cuts RAM by up to 97%. | A zero-dependency fuzzy-search library using the Bitap algorithm — roughly 6.8kB gzipped for the basic build, 8.6kB for the full one. Searches arrays of JS objects in memory with typo tolerance, field weighting, nested keys and extended query operators. Runs in browsers, Node and Deno. | A dependency-free JavaScript full-text search engine that builds a real inverted index in memory, in the browser or in Node. Supports prefix search, fuzzy matching, field boosting, modern relevance ranking, auto-suggestion, and adding or removing documents at any time. The index serializes to JSON so it can be prebuilt. |
| Category | Search, vector & RAG | Search, vector & RAG | Search, vector & RAG |
| Cost tier | mixed | free | free |
| Pricing | OSS free (Apache 2.0). Cloud free tier forever: 1GB RAM, 0.5 vCPU, 4GB disk, single node. Standard is usage-based hourly for compute, memory, storage and backup. Premium has a minimum spend. Hybrid and Private Cloud are custom. | Free, Apache-2.0. No service, no account, no limits, no bill. | Free, MIT-licensed. No service or account. |
| Why builders pick it | The best free-forever managed tier of any serious vector DB: 1GB RAM with no clock running is enough for a real side project, and docker run qdrant/qdrant is identical locally. | When a few hundred to a few thousand items already sit in your app state — a command palette, a settings list, a filterable table — this is ten lines of code and no infrastructure. | The step up from Fuse.js when you have tens of thousands of records — a proper index instead of rescoring every item per keystroke, still with no backend and no bill. |
| Watch out for | The free cluster is a single node with no backups and no HA — a prototype home, not a production one. Filtered search over high-cardinality payloads needs index planning. | It scores every item on every keystroke, so it degrades noticeably past roughly 10K records. Use MiniSearch or a real index above that. | The whole index ships to the client, so a large corpus means a large download. Prebuild and cache the serialized JSON rather than indexing on every page load. |
| How to wire it up | docker run -p 6333:6333 qdrant/qdrant | npm i fuse.js | npm i minisearch |
| Editor's pick | No | No | No |
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# Tool comparison — Qdrant vs Fuse.js vs MiniSearch Source: Kbaise, a directory of tools that work with Lovable projects. Pick one and tell me why before writing any integration code. ## Qdrant (qdrant) - URL: https://qdrant.tech - Category: Search, vector & RAG - Cost: mixed — OSS free (Apache 2.0). Cloud free tier forever: 1GB RAM, 0.5 vCPU, 4GB disk, single node. Standard is usage-based hourly for compute, memory, storage and backup. Premium has a minimum spend. Hybrid and Private Cloud are custom. - What it is: A Rust vector database with the strongest payload filtering in the category — combine vector similarity with keyword, full-text, numeric-range, geo and boolean conditions in one query. Supports hybrid search with reciprocal rank fusion, sharding, multitenancy, GPU indexing, and quantization that cuts RAM by up to 97%. - Why builders pick it: The best free-forever managed tier of any serious vector DB: 1GB RAM with no clock running is enough for a real side project, and docker run qdrant/qdrant is identical locally. - Trap: The free cluster is a single node with no backups and no HA — a prototype home, not a production one. Filtered search over high-cardinality payloads needs index planning. - Wiring: docker run -p 6333:6333 qdrant/qdrant - Full dossier: /api/public/tools/qdrant ## Fuse.js (fuse-js) - URL: https://www.fusejs.io - Category: Search, vector & RAG - Cost: free — Free, Apache-2.0. No service, no account, no limits, no bill. - What it is: A zero-dependency fuzzy-search library using the Bitap algorithm — roughly 6.8kB gzipped for the basic build, 8.6kB for the full one. Searches arrays of JS objects in memory with typo tolerance, field weighting, nested keys and extended query operators. Runs in browsers, Node and Deno. - Why builders pick it: When a few hundred to a few thousand items already sit in your app state — a command palette, a settings list, a filterable table — this is ten lines of code and no infrastructure. - Trap: It scores every item on every keystroke, so it degrades noticeably past roughly 10K records. Use MiniSearch or a real index above that. - Wiring: npm i fuse.js - Full dossier: /api/public/tools/fuse-js ## MiniSearch (minisearch) - URL: https://github.com/lucaong/minisearch - Category: Search, vector & RAG - Cost: free — Free, MIT-licensed. No service or account. - What it is: A dependency-free JavaScript full-text search engine that builds a real inverted index in memory, in the browser or in Node. Supports prefix search, fuzzy matching, field boosting, modern relevance ranking, auto-suggestion, and adding or removing documents at any time. The index serializes to JSON so it can be prebuilt. - Why builders pick it: The step up from Fuse.js when you have tens of thousands of records — a proper index instead of rescoring every item per keystroke, still with no backend and no bill. - Trap: The whole index ships to the client, so a large corpus means a large download. Prebuild and cache the serialized JSON rather than indexing on every page load. - Wiring: npm i minisearch - Full dossier: /api/public/tools/minisearch ## Quick read - Free with no card: Fuse.js, MiniSearch. ## Rules 1. Prefer the free tier when no budget was stated, and say what the ceiling is. 2. Read the full dossier before integrating. 3. Fetch /api/public/models before writing any AI model ID.
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