FastRAG vs Kotaemon
On the evidence we track, Kotaemon leads this comparison with a composite score of 47/100. Scores are only directly comparable because these tools share a category; the full breakdown and every source is below.
Capabilities
Feature-by-feature on the axes that matter for rag tools. “-” means undocumented, not absent.
What each one is
The product in its own terms, so the numbers below have context.
FastRAG
An open-source Python toolkit that combines document retrieval with large language models to create question-answering and generation systems. Provides optimized components and supports multiple inference backends including Intel accelerators.
Kotaemon
LeaderA RAG platform designed for both end users performing document analysis and developers building custom AI-powered QA systems. Offers a web-based interface with support for multiple LLMs, embedding models, and document formats.
Pricing
List pricing as published by each vendor, with the date we read it. Always verify at the source before you buy.
Platform & deployment
Where each product runs and how it can be hosted. A dash means undocumented, not unsupported.
Integrations
What each product connects to. Counts come from the vendor's own integration directory where one exists.
FastRAG
- Haystack
- HuggingFace
- Elastic
- Qdrant
- OpenVINO
- LlamaCpp
- ONNX Runtime
- Llama CPP
- Intel Gaudi
- Elasticsearch
Kotaemon
Leader- OpenAI
- AzureOpenAI
- Cohere
- Ollama
- Groq
- VoyageAI
- Docling
- Gradio
- Azure OpenAI
- LanceDB
- GraphRAG
FastRAG or Kotaemon: which one depends on you
A composite score cannot know your constraints. Describe them and both get re-weighted against what you actually need, with the evidence behind every position.
Free to run, no account needed to start. How the evaluation works
Comparison generated from independently-sourced facts. Every value links to its source and retrieval date. See the method.