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. Learn more.

What is GraphRAG and why is it important for AI agents?

GraphRAG (Graph Retrieval-Augmented Generation) is a system that explicitly maps data relationships, providing structured retrieval, tenant isolation, and explainable outputs for AI agents. According to the KPMG AI Report, GraphRAG addresses top enterprise concerns like data quality, privacy, and trust in AI by ensuring transparent, accurate, and secure AI decision-making. Learn more about GraphRAG.

How does FalkorDB support GraphRAG for enterprise AI?

FalkorDB offers an accurate, multi-tenant RAG solution based on low-latency, scalable graph database technology. It is optimized for highly technical teams handling complex, interconnected data in real-time, resulting in fewer hallucinations and more accurate responses from LLMs. Explore the GraphRAG-SDK.

What are the main use cases for FalkorDB?

FalkorDB is used for Text2SQL (natural language to SQL on complex schemas), security graphs (for CNAPP, CSPM & CIEM), GraphRAG, agentic AI and chatbots, fraud detection, and as a high-performance graph database for complex relationships. See all use cases.

Who is FalkorDB designed for?

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. Get a demo.

What is the primary purpose of FalkorDB?

FalkorDB's primary purpose is to provide an accurate, multi-tenant RAG solution powered by a low-latency, scalable graph database. It enables development teams to achieve accurate GraphRAG for enterprise GenAI and address trust, scalability, and compliance needs. Learn more.

What technical documentation is available for FalkorDB?

FalkorDB provides comprehensive technical documentation and API references, including setup guides, advanced configurations, and integration instructions. Access the documentation at docs.falkordb.com and the latest releases at GitHub Releases.

Does FalkorDB offer an API?

Yes, FalkorDB provides an API with complete references and guides for developers, data scientists, and engineers. The API documentation is available at docs.falkordb.com.

What integrations does FalkorDB support?

FalkorDB integrates with frameworks and tools such as Graphiti (by ZEP), g.v() for visualization, Cognee, LangChain, and LlamaIndex for LLM integration. For more details, see the integrations page.

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 10,000+ multi-graphs (multi-tenancy), open-source licensing, linear scalability, advanced AI integration (GraphRAG, agent memory), and flexible deployment (cloud & on-prem). See all features.

How does FalkorDB ensure data privacy and tenant isolation?

FalkorDB integrates security directly into its architecture through native tenant isolation, ensuring data privacy and robust compliance. This design prevents organizational data leakage and supports secure AI agent implementations. Learn more.

How does FalkorDB support explainable AI and transparency?

FalkorDB, through GraphRAG, provides explicit, traceable data retrieval pathways, enabling clear and defensible AI decisions. This transparency is essential for enterprise trust and regulatory compliance, as highlighted in the KPMG AI Report.

What performance advantages does FalkorDB offer?

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 flexible horizontal scaling, making it ideal for real-time, large-scale AI and analytics workloads. See benchmarks.

How does FalkorDB handle scalability?

FalkorDB supports linear, flexible horizontal scaling and can manage over 10,000 multi-graphs, making it suitable for enterprises and SaaS providers with large, complex datasets.

Is FalkorDB open source?

Yes, FalkorDB is open source, encouraging community collaboration and transparency. This differentiates it from proprietary solutions like AWS Neptune.

Does FalkorDB support multi-tenancy?

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

What compliance certifications does FalkorDB have?

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

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 pricing plans does FalkorDB offer?

FalkorDB offers four main 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 with VPC, custom backups, and 24/7 support). See pricing details.

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, ideal for mission-critical applications.

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 for large organizations with advanced requirements.

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. Neo4j uses an on-disk storage model, is written in Java, and offers multi-tenancy only in premium plans. See detailed comparison.

How does FalkorDB compare to AWS Neptune?

FalkorDB is open source, supports multi-tenancy, and delivers better latency performance and efficient vector search. AWS Neptune is proprietary, closed-source, and lacks multi-tenancy support. See detailed 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 efficiency and limited scaling.

What makes FalkorDB different from other graph databases?

FalkorDB stands out with its in-memory storage model (written in C and Rust), open-source licensing, built-in multi-tenancy, ultra-low latency, and advanced AI integration. These features provide significant advantages for real-time, scalable, and AI-driven applications.

What problems does FalkorDB solve for enterprises?

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 development. Learn more.

What business impact can customers expect from FalkorDB?

Customers can expect improved scalability, enhanced trust and reliability, reduced alert fatigue, faster time-to-market, better user experience, regulatory compliance, and support for advanced AI applications. These outcomes empower organizations to unlock the full potential of their data. See business impact.

What pain points does FalkorDB address?

FalkorDB helps with trust and reliability in LLM-based applications, managing large-scale data, reducing alert fatigue in cybersecurity, overcoming performance limitations of competitors, enabling interactive data analysis, supporting regulatory compliance, and powering agentic AI and chatbots.

Who can benefit from using FalkorDB?

Enterprises, SaaS providers, and organizations in industries like healthcare, media & entertainment, and AI/ethical AI development can benefit from FalkorDB's advanced graph database capabilities. See case studies.

What industries use FalkorDB?

Industries represented in FalkorDB case studies include healthcare (AdaptX), media and entertainment (XR.Voyage), and artificial intelligence/ethical AI development (Virtuous AI). Explore industry use cases.

Can you share customer success stories with FalkorDB?

Yes. AdaptX used FalkorDB to analyze clinical data, XR.Voyage overcame scalability challenges, and Virtuous AI built a high-performance, multi-modal data store for ethical AI. Read their stories.

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

Customers like AdaptX and 2Arrows have praised FalkorDB for its rapid access to insights, ease of running non-traversal queries, and user-friendly dashboards. These testimonials highlight FalkorDB's intuitive design and high-speed capabilities. See testimonials.

How easy is it to get started with FalkorDB?

FalkorDB is built for rapid deployment, allowing teams to go from concept to enterprise-grade solutions in weeks. Users can sign up for FalkorDB Cloud, try a free instance, run locally with Docker, or schedule a demo. Comprehensive documentation and community support are available. Get started.

What support options are available for FalkorDB users?

Support options include comprehensive documentation, community support via Discord and GitHub Discussions, access to solution architects, and demo/onboarding sessions. See support resources.

How long does it take to implement FalkorDB?

FalkorDB enables rapid implementation, allowing development teams to deploy enterprise-grade solutions in weeks, not months. This accelerates time-to-market for AI and data-driven applications.

Where can I find FalkorDB's latest updates and release notes?

The latest updates and release notes are available on the FalkorDB GitHub Releases page.

How can I contact FalkorDB for a demo or sales inquiry?

You can schedule a demo or contact sales directly via the FalkorDB demo page or the contact page.

Why the KPMG AI Report Demands GraphRAG for Enterprise AI

Gal Shubeli
Date Published: March 20, 2025
Date Updated: March 25, 2025

Highlights

KPMG AI Report cites data quality (85%) and privacy (71%) as top enterprise AI concerns; GraphRAG addresses both through structured retrieval.
Enterprise trust in AI requires explainable outputs; GraphRAG uniquely provides transparent reasoning beyond basic embedding models.
GraphRAG ensures tenant isolation, enabling secure AI agent implementations without risking organizational data leakage.

Enterprises are pushing AI agents from experimental stages into full production under intense pressures to deliver measurable value.

Yet, a key hurdle remains: traditional database architectures. These architectures obscure critical data relationships, undermining AI accuracy, privacy, and scalability. The KPMG AI Report confirms this, identifying data quality as the dominant challenge faced by 85% of enterprise leaders.

Vector databases—tabular schemas and document stores—seem logical but secretly mask complex data interdependencies. These structures fail to clearly represent the intricate relationships within modern, complex datasets.

As AI scales, hidden inconsistencies in traditional databases begin to surface, resulting in unreliable AI performance and unmanageable complexity.

Scaling AI from Pilot to Production

Despite significant experimentation, 51% of organizations exploring AI agents and 37% piloting—only 12% achieve full operational deployment. Traditional database architectures struggle with real-time integration, flexible scalability, and operational integration, creating barriers to demonstrating ROI.

How GraphRAG Reframes the Data Quality Challenge

GraphRAG provides a new perspective by explicitly mapping data relationships, directly addressing the structural limitations inherent in conventional databases. This explicit representation dramatically enhances data clarity and integrity, which is foundational for reliable AI outcomes. By eliminating hidden inconsistencies, GraphRAG ensures enterprises can confidently scale their AI initiatives.

Correcting Misconceptions Directly

Misconception: Incremental database improvements suffice.

Reality: Structural change through explicit relationship mapping provided by GraphRAG is required.

Misconception: External governance sufficiently protects data privacy.

Reality: Effective privacy requires integration directly within the data architecture—GraphRAG delivers this capability.

Misconception: Embedding-based AI decisions offer adequate explainability.

Reality: Transparent, explicit retrieval methods used by GraphRAG are necessary for enterprise trust and compliance.

Building Trust Through Explainability

AI adoption often stalls due to stakeholders’ skepticism about AI decision-making processes. Transparency and explainability are not just desirable—they’re essential. GraphRAG addresses this critical demand by providing explicit, traceable data retrieval pathways. Unlike traditional AI systems that rely on opaque embeddings, GraphRAG enables clear, defensible AI decisions, significantly boosting stakeholder confidence.

Privacy and cybersecurity remain top concerns for enterprises adopting AI, as highlighted by 71% of leaders in the KPMG AI Report. GraphRAG integrates security directly into its architecture through native tenant isolation. This design ensures data privacy and robust compliance, reducing reliance on external governance systems and minimizing vulnerabilities.

Enterprises continue to struggle with moving AI agents from pilot stages to fully operational deployments. The transition demands flexibility, seamless integration, and scalability—capabilities often missing in traditional databases. GraphRAG’s architecture inherently supports real-time data streams and horizontal scalability, simplifying the deployment process and bridging the critical gap from pilot to production.

If you’re still relying on traditional databases, you’re treating symptoms—not the root issue. Is your approach to AI adoption treating symptoms rather than underlying data complexity?

What key challenges does the KPMG AI Report highlight

Data quality (85%), data privacy (71%), and trust in AI accuracy top the list.

Why is GraphRAG critical according to the KPMG AI Report findings?

GraphRAG ensures enterprise data accuracy, tenant isolation, and explainable AI outputs.

How does GraphRAG differ from vector databases?

Unlike vectors, GraphRAG offers structured retrieval, tenant isolation, and explicit data context for accurate AI.

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.

Gal Shubeli

Gal is a Software and AI Engineer, leading the development of GraphRAG-SDK, a specialized toolkit for building Graph Retrieval-Augmented Generation (GraphRAG) systems. It integrates knowledge graphs, ontology management, and state-of-the-art LLMs to deliver accurate, efficient, and customizable RAG workflows.

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