Vector databases and RAG: AI tools compared (7)

What vector databases and RAG tools are for

Vector database and RAG tools help retrieve useful material from documents so an AI application can use it as context. RAG combines document retrieval with response generation. This category serves teams building internal search, document assistants or guided access to a knowledge base. Previously, someone might open several procedure files and search manually for the relevant instructions. With a suitable workflow, they ask a question, the system retrieves passages and an answer can be prepared using that material. Agentset, for example, supports document imports and answers with sources. A vector database covers a different part of the workflow by storing and searching representations of content. It does not necessarily provide a complete assistant. The final result also depends on extraction, access permissions, document updates and how the generated answer uses the retrieved evidence.

Example uses:

• You retrieve passages from an internal procedure when a question uses different wording from the original document.
• You build a document assistant that presents answers alongside sources readers can inspect to check important claims.
• You combine keyword and semantic search to find exact references within material containing specialized terminology and abbreviations.
• You filter retrieval results using metadata to restrict the search to documents relevant to the requested context.
• You index updated documents and verify that superseded versions no longer appear where they would mislead readers.

Our picks by need

These picks follow simple rules applied to the verified data, with no partnership or affiliate link:

• Best scored : LlamaIndex. 95/100 on our access and transparency grid.
• Cheapest paid option : Pinecone. First plan at $20 per month.

The main tools at a glance

LlamaIndex 95/100

LlamaIndex lets teams processing documents extract content, structure tables, and index documents for search. It also connects model responses to source documents and supports building agents that process documents. Features: Extract content from complex documents; Convert tables into structured data; Index documents for search. Entry price: $50 per month, with a free tier. Runs on: web browser and API. Data hosting: Several regions.

Pinecone 78/100

Pinecone helps people build vector searches and document assistants. It stores and searches vectors, combines semantic and keyword search, filters results by metadata, and creates vector representations of texts. Features: Store and search vectors; Combine semantic search and keyword search; Filter results by their metadata. Entry price: $20 per month, with a free tier. Runs on: web browser and API. Data hosting: Several regions.

Weaviate 75/100

Weaviate lets teams developing RAG applications store documents and their vector representations. It supports semantic and keyword search, property filters, and queries through a console for accessing stored data. Features: Store documents and their vector representations; Search by meaning and keywords; Filter results by properties. Entry price: $45 per month, with a free tier. Runs on: web browser and API. Data hosting: Several regions.

Chroma 68/100

Chroma is for people indexing documents and their vectors to search for passages by similarity. It combines text and vector search and allows results to be filtered using metadata. Features: Index documents and their vectors; Search for passages by similarity; Combine text and vector search. Entry price: $2.5 pay as you go, with a free tier. Runs on: web browser and API. Data hosting: Several regions.

Agentset 66/100

Agentset lets teams querying documents import and index them, search passages by meaning, and obtain answers with sources. It also supports querying document images and tables and filtering with metadata. Features: Import and index documents; Search passages by meaning; Answer questions with sources. Entry price: $49 per month, with a free tier. Runs on: web browser and API.

Qdrant 65/100

Qdrant lets teams working with vector databases store and index vectors. It combines semantic and keyword search, filters results using metadata, and provides collection management through a web interface. Features: Store and index vectors; Search for content by similarity; Combine semantic search and keyword search. Free tier available. Runs on: web browser and API. Data hosting: Several regions.

Gestell 48/100

Gestell lets users import documents and media to extract and structure information. It supports extraction categories and lets users search data collections or query them through an API. Features: Import documents and media; Extract and structure information; Define extraction categories. Entry price: $212.5 per month, with a free tier. Runs on: web browser and API.

The category in figures

Of the 7 tools in the category, 7 (100%) offer a free tier, 100% have a public API and 0 have a French interface. The median entry price, per month, is $49 (across 5 tools with a published price). 5 disclose where data is hosted, 0 of them in Europe or on device.

Also useful

These tools belong to another category but also cover this need:

• Firecrawl Firecrawl lets developers extract web page content and crawl websites. It converts pages to Markdown, extracts
• Wren AI Wren AI serves teams querying databases in plain language. It produces SQL queries, creates charts and dashboa
• Dify Dify assembles workflows in a visual editor and creates agents that use multiple tools. It serves people who w
• Jina Reader Jina Reader extracts main web page content, converts URLs or PDFs to Markdown and returns structured JSON cont
• BigModel BigModel lets users call language models through an API, analyze text and images, and generate images and vide
• Nomic Atlas Nomic Atlas lets people analyzing datasets explore them on an interactive map. It groups similar text and imag

Choosing well and avoiding pitfalls

Separate vector storage requirements from the need for a complete document workflow. Test your own files, including tables, scans and lengthy documents. Evaluate French retrieval using real questions and expected passages. Review filters, permissions, updates and effective deletion. Examine hybrid search options, exports and integration with your application. Establish where documents, vectors and queries are processed and stored. Compare billing conditions for storage, indexing and search. If the workflow calls separate models for document representations or answer generation, include those dependencies when assessing operational requirements and the overall cost of the proposed setup.

A passage similar to a question can still be irrelevant or outdated. Evaluate retrieval separately from answer generation. Displayed sources do not prove that every claim is supported. Preserve access rules during indexing and queries. Treat instructions embedded in documents as untrusted content. Test updates and deletion throughout the workflow, including copies held by connected components. Protect vectors and logs as well as original files, since they can reveal information about your material. Check whether the system can acknowledge that available evidence is insufficient.

To compare with the cost of using an AI model directly, see the API price calculator.

Free or paid?

Free plans can support indexing a trial collection and testing retrieval within defined restrictions. They help assess extraction and relevance before a lasting integration. Paying becomes useful when document volume, query activity, availability or permission requirements exceed those conditions. Check separately for charges from models used to represent documents or generate answers from the retrieved material.

Tools in the category with a free tier: LlamaIndex, Pinecone, Weaviate, Chroma, Agentset, Qdrant, and Gestell.

For which jobs?

The profiles that use these tools most, based on the use cases documented by publishers: developers, data analysts, and researchers.