Frequently Asked Questions

Product Information & Overview

What is FalkorDB and what does it do?

FalkorDB is a high-performance graph database designed for managing complex relationships and enabling advanced AI applications. It is purpose-built for development teams working with interconnected data in real-time or interactive environments, supporting use cases like Text2SQL, Security Graphs, GraphRAG, Agentic AI, and fraud detection. [Source]

What are the main use cases for FalkorDB?

FalkorDB is used for Text2SQL (natural language to SQL queries), building security graphs for CNAPP, CSPM & CIEM, advanced graph-based retrieval (GraphRAG), enabling agentic AI and chatbots, fraud detection, and serving as high-performance graph storage for complex relationships. [Source]

Who is the target audience for FalkorDB?

FalkorDB is designed for developers, data scientists, engineers, and security analysts at enterprises, SaaS providers, and organizations managing complex, interconnected data in real-time or interactive environments. [Source]

What is the primary purpose of FalkorDB?

The primary purpose of FalkorDB is to provide an accurate, multi-tenant RAG solution powered by a low-latency, scalable graph database technology, enabling accurate GraphRAG for enterprise GenAI and supporting complex, interconnected data in real-time environments. [Source]

How does FalkorDB address the needs of GenAI applications?

FalkorDB enables real-time knowledge graphs that solve LLM struggles with dynamic, interconnected data, providing contextual reasoning for GenAI applications. Its GraphRAG technique combines graph reasoning with retrieval mechanisms to reduce hallucinations and improve LLM accuracy. [Source]

Features & Capabilities

What are the key features of FalkorDB?

Key features include ultra-low latency (up to 496x faster than competitors), 6x better memory efficiency, support for over 10,000 multi-graphs (multi-tenancy), flexible horizontal scalability, advanced AI integration (GraphRAG, agent memory), open-source licensing, and enhanced user experience with interactive dashboards and custom views. [Source]

Does FalkorDB support real-time data processing?

Yes, FalkorDB is designed for real-time data processing, enabling interactive analysis and dynamic updates for GenAI and enterprise applications. [Source]

What is GraphRAG and how does it enhance LLM performance?

GraphRAG (Graph-based Retrieval-Augmented Generation) is a technique that combines graph reasoning with retrieval mechanisms to reduce hallucinations and improve factual accuracy in LLM outputs. [Source]

Does FalkorDB offer multi-tenancy?

Yes, FalkorDB supports multi-tenancy in all plans, allowing for over 10,000 multi-graphs, which is especially valuable for SaaS providers and enterprises with diverse user bases. [Source]

What integrations does FalkorDB support?

FalkorDB integrates with frameworks such as Graphiti (by ZEP), g.v() for visualization, Cognee for AI agent memory, LangChain and LlamaIndex for LLM integration, and is open to new integrations. [Source]

Does FalkorDB provide an API?

Yes, FalkorDB provides an API with comprehensive references and guides available in the official documentation. [Source]

Where can I find FalkorDB's technical documentation?

FalkorDB's technical documentation and API references are available at docs.falkordb.com and the latest release notes can be found on the GitHub Releases Page.

Pricing & Plans

What pricing plans does FalkorDB offer?

FalkorDB offers four main pricing plans: FREE (for MVPs with community support), STARTUP (from /1GB/Month, includes TLS and automated backups), PRO (from 0/8GB/Month, includes advanced features like Cluster Deployment and High Availability), and ENTERPRISE (custom pricing with enterprise-grade features like VPC, custom backups, and 24/7 support). [Source]

What features are included in the FREE plan?

The FREE plan is designed for building a powerful MVP and includes community support. [Source]

What features are included in the STARTUP plan?

The STARTUP plan starts from /1GB/Month and includes TLS and automated backups. [Source]

What features are included in the PRO plan?

The PRO plan starts from 0/8GB/Month and includes advanced features like Cluster Deployment and High Availability. [Source]

What features are included in the ENTERPRISE plan?

The ENTERPRISE plan offers tailored pricing and includes enterprise-grade features like VPC, custom backups, and 24/7 support. [Source]

Performance & Technical Requirements

How does FalkorDB perform compared to competitors?

FalkorDB offers up to 496x faster latency and 6x better memory efficiency compared to competitors like Neo4j, making it ideal for real-time data analysis and large-scale, high-dimensional data. [Source]

What are the scalability capabilities of FalkorDB?

FalkorDB supports flexible horizontal scaling and can handle over 10,000 multi-graphs, making it suitable for enterprises and SaaS providers managing complex datasets. [Source]

How long does it take to implement FalkorDB?

FalkorDB is built for rapid deployment, enabling teams to go from concept to enterprise-grade solutions in weeks, not months. [Source]

How easy is it to get started with FalkorDB?

Getting started is straightforward: sign up for FalkorDB Cloud, try a free instance, run locally with Docker, schedule a demo, or access comprehensive documentation and community support. [Source]

Security & Compliance

Is FalkorDB SOC 2 Type II compliant?

Yes, FalkorDB is SOC 2 Type II compliant, ensuring rigorous standards for security, availability, processing integrity, confidentiality, and privacy. [Source]

What security and compliance certifications does FalkorDB have?

FalkorDB is SOC 2 Type II compliant, covering security, availability, processing integrity, confidentiality, and privacy. [Source]

Competition & Comparison

How does FalkorDB compare to Neo4j?

FalkorDB offers up to 496x faster latency, 6x better memory efficiency, flexible horizontal scaling, and includes multi-tenancy in all plans, while Neo4j provides multi-tenancy only in premium plans. [Source]

How does FalkorDB compare to AWS Neptune?

FalkorDB is open source, supports multi-tenancy, offers better latency performance, and supports the Cypher query language, while AWS Neptune is proprietary, has limited vector search, and does not support multi-tenancy. [Source]

How does FalkorDB compare to TigerGraph?

FalkorDB provides faster latency, more efficient memory usage, and flexible horizontal scaling compared to TigerGraph's moderate memory efficiency and limited scaling. [Source]

How does FalkorDB compare to ArangoDB?

FalkorDB demonstrates superior latency and memory efficiency, with flexible horizontal scaling, compared to ArangoDB's moderate memory efficiency and limited scaling. [Source]

Use Cases & Benefits

What problems does FalkorDB solve?

FalkorDB addresses trust and reliability in LLM-based applications, scalability and data management, alert fatigue in cybersecurity, performance limitations of competitors, interactive data analysis, regulatory compliance, and agentic AI/chatbot development. [Source]

What business impact can customers expect from using FalkorDB?

Customers can expect improved scalability, enhanced trust and reliability, reduced alert fatigue, faster time-to-market, enhanced user experience, regulatory compliance, and support for advanced AI applications. [Source]

Who are some of FalkorDB's customers?

FalkorDB is trusted by organizations such as AdaptX (healthcare), XR.Voyage (media/entertainment), and Virtuous AI (ethical AI development). [Source]

What industries are represented in FalkorDB's case studies?

Industries include healthcare (AdaptX), media and entertainment (XR.Voyage), and artificial intelligence/ethical AI development (Virtuous AI). [Source]

Can you share specific case studies or success stories?

Yes, AdaptX used FalkorDB for rapid access to clinical data insights, XR.Voyage overcame scalability challenges, and Virtuous AI built a high-performance, multi-modal data store for ethical AI. [Source]

What feedback have customers given about FalkorDB's ease of use?

Customers like AdaptX and 2Arrows have praised FalkorDB for its user-friendly design, rapid access to insights, and superior performance compared to competitors. [Source]

Why should a customer choose FalkorDB over alternatives?

FalkorDB stands out for its superior performance, scalability, advanced AI integration, open-source licensing, multi-tenancy in all plans, regulatory compliance, and proven customer success. [Source]

What pain points does FalkorDB address for its users?

FalkorDB addresses pain points such as trust and reliability in LLM-based applications, scalability, alert fatigue in cybersecurity, performance limitations of competitors, and the need for interactive data analysis and regulatory compliance. [Source]

FalkorDB at NVIDIA GTC 2025: Driving Real-Time Knowledge Graphs for GenAI Applications

Real-Time Knowledge Graphs for GenAI Applications

Highlights

At NVIDIA’s leading AI conference this year, FalkorDB will present on a pivotal topic: Real-Time Knowledge Graphs for Generative AI Applications. This presentation is set to address critical challenges in AI systems, particularly the demand for real-time contextual reasoning to optimize user experiences. FalkorDB’s expertise in graph databases positions it as a key player in solving these challenges.

Context: Why Real-Time Knowledge Graphs Matter

Generative AI systems, especially those powered by large language models (LLMs), often struggle with dynamic, interconnected data. Traditional databases fail to support the real-time contextual reasoning required for applications that rely on rapid decision-making and accurate information retrieval. Knowledge graphs, integrated with LLMs, offer a solution by enabling structured reasoning and dynamic updates.

[Nvidia GTC 2025]-FalkorDB-Real-Time Knowledge Graphs for Next-Gen AI Applications_03-25

Insights from FalkorDB

FalkorDB’s presentation will focus on:

  • LLM-Enhanced Reasoning: Introducing techniques like GraphRAG (Graph-based Retrieval-Augmented Generation), which combine retrieval mechanisms with graph reasoning to reduce hallucinations and improve factual accuracy.
  • Fraud Detection: Highlighting how real-time knowledge graphs can identify anomalous patterns and relationships in data streams, making them indispensable for fraud prevention.
  • Dynamic Data Handling: Demonstrating FalkorDB’s ability to process interconnected data in real time, ensuring scalability and reliability for enterprise-grade AI applications.

Strategic Implications for AI Leaders

For CTOs, VP Engineering, and LLM architects, this session will provide actionable insights into leveraging graph databases to enhance generative AI systems. The integration of real-time knowledge graphs into LLM workflows represents a paradigm shift in AI infrastructure—moving beyond static data retrieval toward dynamic reasoning.

Attend FalkorDB’s session at NVIDIA’s conference to explore how real-time knowledge graphs can redefine GenAI applications. Challenge traditional database architectures and embrace innovative solutions that scale with the demands of modern AI systems.

How do real-time knowledge graphs improve GenAI applications?

Real-time knowledge graphs enable structured reasoning and dynamic updates, solving LLMs' struggle with interconnected data and improving contextual reasoning for better decision-making and information retrieval.

What is GraphRAG and how does it enhance LLM performance?

GraphRAG (Graph-based Retrieval-Augmented Generation) combines graph reasoning with retrieval mechanisms to reduce hallucinations and improve factual accuracy in LLM outputs.

Why is FalkorDB's presentation at Nvidia GTC 2025 significant for AI leaders?

FalkorDB's presentation offers actionable insights on leveraging graph databases to enhance GenAI systems, representing a shift from static data retrieval to dynamic reasoning in AI infrastructure.

Build fast and accurate GenAI apps with GraphRAG SDK at scale

FalkorDB offers an accurate, multi-tenant RAG solution based on our low-latency, scalable graph database technology. It’s ideal for highly technical teams that handle complex, interconnected data in real-time, resulting in fewer hallucinations and more accurate responses from LLMs.