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 such as Text2SQL, Security Graphs, GraphRAG, agentic AI, chatbots, and fraud detection. Learn more.

What is the GraphRAG-SDK and what are its latest updates?

The GraphRAG-SDK is a toolkit for building graph-based Retrieval Augmented Generation (RAG) systems. As of version 0.2.0, it supports Ollama (for local LLM deployments) and Azure OpenAI (for enterprise-grade cloud LLMs), expanding deployment options for graph-based RAG applications. See release notes.

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.

What are the main use cases for FalkorDB?

Main use cases include Text2SQL (natural language to SQL on complex schemas), Security Graphs (for CNAPP, CSPM, CIEM), GraphRAG (advanced graph-based retrieval), agentic AI and chatbots, fraud detection, and high-performance graph storage for complex relationships.

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). See case studies.

Who are some of FalkorDB's customers?

Notable customers include AdaptX, XR.Voyage, and Virtuous AI, each leveraging FalkorDB for advanced data management and AI-driven solutions. Read their stories.

Features & Capabilities

What are the key features of FalkorDB?

Key features include ultra-low latency (up to 496x faster than Neo4j), 6x better memory efficiency, support for 10,000+ multi-graphs (multi-tenancy), open-source licensing, linear scalability, advanced AI integration (GraphRAG, agent memory), and flexible cloud/on-prem deployment.

Does FalkorDB support multi-tenancy?

Yes, FalkorDB supports multi-tenancy in all plans, enabling management of over 10,000 multi-graphs. This is especially valuable for SaaS providers and organizations with diverse user bases.

What integrations does FalkorDB offer?

FalkorDB integrates with frameworks such as Graphiti (for AI agent memory), g.v() (for knowledge graph visualization), Cognee (for AI agent memory), LangChain and LlamaIndex (for LLM integration), and is open to new integrations. Learn more.

Does FalkorDB provide an API?

Yes, FalkorDB provides a comprehensive API with references and guides available in the official documentation, supporting integration into developer workflows.

What technical documentation is available for FalkorDB?

FalkorDB offers extensive technical documentation and API references at docs.falkordb.com and release notes on GitHub. These resources cover setup, advanced configurations, and integration guides.

How does the GraphRAG-SDK help with knowledge management?

The GraphRAG-SDK enables conversion of enterprise documentation into queryable knowledge graphs, supports complex queries across multiple documents, and reduces token usage by 26-97% compared to traditional RAG approaches. See details.

What data sources can be ingested with the GraphRAG-SDK?

The SDK can ingest data from URLs, CSV files, and JSON sources, and can automatically generate ontologies for different domains, supporting comprehensive data integration projects.

How does the GraphRAG-SDK improve RAG applications?

It captures complex relationships between entities, provides explainable and verifiable responses, and enables visual debugging of query results, making it ideal for scenarios requiring complex reasoning across large datasets.

What deployment options are available for the GraphRAG-SDK?

The SDK supports both cloud and on-premises deployments, including local LLM support via Ollama and enterprise integration with Azure OpenAI.

Performance & Scalability

How does FalkorDB perform compared to competitors?

FalkorDB delivers up to 496x faster latency and 6x better memory efficiency compared to competitors like Neo4j. It supports over 10,000 multi-graphs and offers flexible horizontal scaling for large-scale, high-dimensional data. See benchmarks.

What makes FalkorDB suitable for AI and agentic applications?

FalkorDB is optimized for AI use cases such as GraphRAG and agent memory, combining graph traversal with vector search for real-time adaptability and personalized user experiences in intelligent agents and chatbots.

How scalable is FalkorDB?

FalkorDB supports linear scalability and flexible horizontal scaling, making it ideal for enterprises and SaaS providers managing complex, high-dimensional datasets.

How does FalkorDB enhance user experience for data analysis?

FalkorDB enables fast, interactive analysis of complex data through dashboards and custom views, providing a frictionless user experience for exploring and filtering data.

Security & Compliance

What security and compliance certifications does FalkorDB have?

FalkorDB is SOC 2 Type II compliant, meeting rigorous standards for security, availability, processing integrity, confidentiality, and privacy. Learn more.

How does FalkorDB ensure data privacy and protection?

FalkorDB's SOC 2 Type II compliance ensures protection against unauthorized access, operational availability, accurate data processing, confidentiality of sensitive information, and privacy of personal data.

Pricing & Plans

What pricing plans does FalkorDB offer?

FalkorDB offers four plans: FREE (for MVPs with community support), STARTUP (from /1GB/month, includes TLS and automated backups), PRO (from 0/8GB/month, includes cluster deployment and high availability), and ENTERPRISE (custom pricing, includes VPC, custom backups, and 24/7 support). See pricing.

What features are included in the FREE plan?

The FREE plan is designed for building a powerful MVP and includes community support. It is ideal for early-stage projects and experimentation.

What features are included in the STARTUP plan?

The STARTUP plan starts at /1GB/month and includes TLS encryption and automated backups, making it suitable for growing teams and production workloads.

What features are included in the PRO plan?

The PRO plan starts at 0/8GB/month and includes advanced features such as cluster deployment and high availability, targeting organizations with mission-critical requirements.

What features are included in the ENTERPRISE plan?

The ENTERPRISE plan offers tailored pricing and includes enterprise-grade features such as VPC, custom backups, and 24/7 support, suitable for large organizations with advanced needs.

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, whereas Neo4j's multi-tenancy is only in premium tiers. FalkorDB is open source and supports full on-prem deployment. See detailed comparison.

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 lacks multi-tenancy. See comparison.

How does FalkorDB compare to TigerGraph?

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

How does FalkorDB compare to ArangoDB?

FalkorDB demonstrates superior latency and memory efficiency, with flexible horizontal scaling, making it a better choice for performance-critical applications compared to ArangoDB's moderate memory efficiency and limited scaling.

Use Cases & Benefits

What business impact can customers expect from using FalkorDB?

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

What pain points does FalkorDB address?

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.

Can you share specific customer success stories with FalkorDB?

Yes. AdaptX used FalkorDB to provide clinicians with rapid access to SPC charts and uncover hidden insights in clinical data. XR.Voyage overcame scalability challenges in immersive experiences, and Virtuous AI built a high-performance, multi-modal data store for ethical AI. Read case studies.

How easy is it to implement FalkorDB?

FalkorDB is built for rapid deployment, enabling teams to go from concept to enterprise-grade solutions in weeks, not months. Getting started is straightforward with cloud sign-up, free trials, Docker support, and comprehensive documentation. Get started.

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

Customers like AdaptX and 2Arrows have praised FalkorDB for its user-friendly design and high-speed performance, highlighting its ability to handle complex data and deliver rapid insights. See feedback.

How does FalkorDB help with regulatory compliance?

FalkorDB's GraphRAG-SDK helps organizations stay ahead of financial regulations by mapping regulations to workflows, identifying compliance gaps, and providing actionable recommendations.

What support and training options are available for FalkorDB?

FalkorDB offers comprehensive documentation, community support via Discord and GitHub, access to solution architects, free trials, and demo options for onboarding. See documentation.

FalkorDB GraphRAG-SDK Adds Ollama and Azure OpenAI Support

FalkorDB GraphRAG-SDK Adds Ollama and Azure OpenAI Support

Our GraphRAG-SDK version 0.2.0 now supports Ollama and Azure OpenAI, expanding deployment options for graph-based RAG applications. 

This integration enables developers to leverage local LLM deployments through Ollama or enterprise-grade Azure OpenAI services while maintaining FalkorDB’s efficient graph storage and retrieval capabilities.

TL;DR

Local LLM Support

  • Run GraphRAG workloads entirely on-premises using Ollama
  • Reduce latency and maintain data privacy
				
					```python
from graphrag_sdk.models.ollama import OllamaGenerativeModel

# Initialize your Ollama preferred model
model = OllamaGenerativeModel(model_name="preferred_model")
```
				
			

Enterprise Integration

  • Seamless connection to Azure OpenAI services
  • Production-ready deployment options for enterprise environments
				
					```python
from graphrag_sdk.models.azure_openai import OpenAiGenerativeModel

# Initialize your Azure OpenAI preferred model
model = AzureOpenAiGenerativeModel(model_name="preferred_model")
```
				
			

The update streamlines the implementation of graph-based retrieval augmented generation (RAG) systems, particularly beneficial for AI developers working with large document collections requiring sophisticated knowledge extraction and querying capabilities.

How to use our GraphRAG-SDK

For Knowledge Management Systems

Document Processing

  • Convert enterprise documentation into queryable knowledge graphs
  • Enable complex queries across multiple documents while maintaining relationships between concepts
  • Reduce token usage by 26-97% compared to traditional RAG approaches

 

How to improve enterprise documentation processing

For Data Integration Projects

Flexible Source Processing

  • Ingest data from URLs, CSV files, and JSON sources
  • Automatically generate ontologies for different domains
  • Connect multiple knowledge graphs for comprehensive analysis

 

For Enhanced RAG Applications

Improved Query Understanding

  • Capture complex relationships between entities
  • Provide explainable and verifiable responses
  • Enable visual debugging of query results

The SDK is particularly suitable in scenarios requiring complex reasoning across large datasets while maintaining high performance and accuracy. Its multi-model support and flexible deployment options make it suitable for both cloud and on-premises implementations.

 

Check it out here, happy coding: https://github.com/FalkorDB/GraphRAG-SDK/releases