FalkorDB delivers an accurate, multi-tenant RAG solution powered by a low-latency, scalable graph database technology. Our solution is purpose-built for development teams working with complex, interconnected data—whether structured or unstructured—in real-time or interactive user environments.
A startup from Israel that is founded by Guy Korland, Roi Lipman, Avi Avni.
Multi-Tenancy
10K+ In a single instance
Low-Latency
500x faster than Neo4j
C, Rust, Next.js
An ultra-low latency Graph Database
x100 faster than the leading solutions
Developers, Architects, Data scientists, CTOs
An ultra-low latency Graph Database that perfects the Knowledge Graph for KG-RAG. Effectively overcoming the existing limitations of RAG for Large Language Models (LLM).
FalkorDB is the first queryable Property Graph database to use sparse matrices to represent the adjacency matrix in graphs and linear algebra to query the graph.
We have collected here some useful links to help you find out if FalkorDB is good.
Check the traffic stats of FalkorDB on SimilarWeb. The key metrics to look for are: monthly visits, average visit duration, pages per visit, and traffic by country. Moreoever, check the traffic sources. For example "Direct" traffic is a good sign.
Check the "Domain Rating" of FalkorDB on Ahrefs. The domain rating is a measure of the strength of a website's backlink profile on a scale from 0 to 100. It shows the strength of FalkorDB's backlink profile compared to the other websites. In most cases a domain rating of 60+ is considered good and 70+ is considered very good.
Check the "Domain Authority" of FalkorDB on MOZ. A website's domain authority (DA) is a search engine ranking score that predicts how well a website will rank on search engine result pages (SERPs). It is based on a 100-point logarithmic scale, with higher scores corresponding to a greater likelihood of ranking. This is another useful metric to check if a website is good.
The latest comments about FalkorDB on Reddit. This can help you find out how popualr the product is and what people think about it.
Use a low-latency graph database: Integrate FalkorDB for its sparse matrix representation and optimized linear algebra-based traversals. Queries execute in milliseconds—critical for real-time AI interactions. - Source: dev.to / 7 months ago
In vector databases, data is stored as high-dimensional vector embeddings, which are numerical representations generated by machine learning models to capture the features of data. When querying, the input is converted into a vector embedding, and similarity searches are performed between the query vector and stored embeddings using distance metrics like cosine similarity or Euclidean distance to retrieve the most... - Source: dev.to / 7 months ago
For AI architects, integrating graph-native storage with LLMs isn’t optional—it’s imperative for building systems capable of robust, multi-hop reasoning at scale. - Source: dev.to / 7 months ago
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