Frequently Asked Questions

Features & Capabilities

Why is graph storage better for security data than relational databases?

Graph storage natively represents the interconnected nature of security data, such as users, configurations, findings, services, roles, assets, and events. This allows you to ask complex questions like, "What's the path from this leaked token to a production database?" without the performance penalties of relational JOINs. The result is shorter time to detect lateral movement, better root-cause analysis, and no context gaps between domains. (Source: FalkorDB Blog)

How does FalkorDB handle schema changes in security datasets?

FalkorDB's flexible data model allows you to introduce new data types and update metadata without downtime or rework. This means you avoid migration-induced slowdowns and can stay responsive to changes in your attack surface. (Source: FalkorDB Blog)

What performance advantages does FalkorDB offer for security analytics?

FalkorDB delivers up to 496x faster latency compared to competitors like Neo4j, with consistent low-latency performance under load for aggregate traversal queries common in security analytics. For example, P50 latency is 36ms (FalkorDB) vs. 469ms (competition), and P99 is 83ms vs. 41,157ms. (Source: Official Benchmarks)

How does FalkorDB optimize memory usage and reduce costs?

FalkorDB's unique sparse matrix core uses fewer resources to represent and traverse graphs, resulting in 6x better memory efficiency compared to competitors. For example, FalkorDB uses 100MB of memory versus 600MB for competitors, lowering your cloud spend. (Source: FalkorDB Blog)

Does FalkorDB support multi-tenancy for security platforms?

Yes, FalkorDB can manage 10,000+ isolated graph tenants per database, each with a private namespace and query surface. This ensures zero tenant data commingling and minimal DevOps overhead, making it ideal for SaaS security platforms. (Source: FalkorDB Blog)

How does FalkorDB help with alert fatigue and threat prioritization?

FalkorDB enables end-to-end context resolution, allowing you to correlate IAM misconfigurations with workload vulnerabilities and data exposure paths. This helps you fix the most important incidents first, reduces false positives, and improves threat model fidelity. (Source: FalkorDB Blog)

What is the maximum query throughput FalkorDB can achieve?

Clustered FalkorDB scales from 20,000 QPS (1 node) to 120,000 QPS (6 nodes) while spreading multi-graph workloads across multiple nodes, supporting high-throughput security analytics. (Source: FalkorDB Blog)

How does FalkorDB support real-time threat modeling and attack path analysis?

FalkorDB is engineered for real-time threat modeling, attack path analysis, and multi-tenant security workloads. It delivers sub-millisecond query performance across billions of edges, making it suitable for interactive security applications. (Source: FalkorDB Blog)

How does FalkorDB handle multi-hop security queries?

FalkorDB represents graphs using sparse matrices and performs efficient graph traversals using linear algebra operations. This enables consistent millisecond query times for multi-hop security queries, supporting real-time detection and investigation. (Source: FalkorDB Blog)

What integrations does FalkorDB offer for security and AI workflows?

FalkorDB integrates with frameworks such as Graphiti (by ZEP) for AI agent memory, g.v() for knowledge graph visualization, Cognee for mapping knowledge graphs, LangChain and LlamaIndex for LLM integration, and more. (Source: FalkorDB Integrations)

Does FalkorDB provide an API and technical documentation?

Yes, FalkorDB provides a comprehensive API and technical documentation, including guides for setup, advanced configurations, and integration. Access the documentation at docs.falkordb.com. (Source: FalkorDB Docs)

What is the primary purpose of FalkorDB?

FalkorDB is a high-performance graph database designed to manage complex relationships and enable advanced AI applications, especially for security, fraud detection, and agentic AI use cases. (Source: FalkorDB Website)

What are the key capabilities of FalkorDB?

Key capabilities include support for 10,000+ multi-graphs (tenants), open-source licensing, linear scalability, ultra-low latency, GraphRAG and agent memory optimization, and flexible cloud/on-prem deployment. (Source: FalkorDB Website)

How does FalkorDB support regulatory compliance in security applications?

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

What security and compliance certifications does FalkorDB have?

FalkorDB is SOC 2 Type II compliant, ensuring rigorous standards for security, availability, processing integrity, confidentiality, and privacy. (Source: FalkorDB Demo Page)

How does FalkorDB ensure tenant data isolation?

FalkorDB provides each tenant with a private namespace and query surface, ensuring zero data commingling and robust isolation for multi-tenant security platforms. (Source: FalkorDB Blog)

How does FalkorDB compare to Neo4j for security workloads?

FalkorDB offers up to 496x faster latency and 6x better memory efficiency than Neo4j, includes multi-tenancy in all plans, and supports flexible horizontal scaling. (Source: FalkorDB vs. Neo4j)

How does FalkorDB compare to AWS Neptune for security analytics?

FalkorDB is open source, supports multi-tenancy, and delivers better latency performance than AWS Neptune, which is proprietary and lacks multi-tenancy support. (Source: FalkorDB vs. AWS Neptune)

How does FalkorDB compare to TigerGraph and ArangoDB?

FalkorDB provides faster latency, more efficient memory usage, and flexible horizontal scaling compared to TigerGraph and ArangoDB, making it a strong choice for performance-critical security applications. (Source: FalkorDB Website)

What pain points does FalkorDB address for security teams?

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

Who can benefit from using FalkorDB for security data?

Developers, data scientists, engineers, and security analysts at enterprises, SaaS providers, and organizations managing complex, interconnected security data in real-time or interactive environments can benefit from FalkorDB. (Source: FalkorDB Demo Page)

What is the business impact of using FalkorDB for security analytics?

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: FalkorDB Website)

Can you share customer success stories using FalkorDB for security data?

Yes. For example, AdaptX uses FalkorDB to analyze high-dimensional medical and clinical patient data, XR.Voyage overcame scalability challenges in immersive platforms, and Virtuous AI created a high-performance, multi-modal data store for ethical AI development. (Source: FalkorDB Case Studies)

What industries use FalkorDB for security and analytics?

Industries include healthcare (AdaptX), media and entertainment (XR.Voyage), and artificial intelligence/ethical AI development (Virtuous AI). (Source: FalkorDB Case Studies)

How easy is it to implement FalkorDB for security data?

FalkorDB is built for rapid deployment, enabling teams to go from concept to enterprise-grade solutions in weeks, not months. You can sign up for FalkorDB Cloud, try it for free, or run it locally using Docker. (Source: FalkorDB Demo Page)

What support and resources are available for FalkorDB users?

FalkorDB offers comprehensive documentation, community support via Discord and GitHub Discussions, solution architects for tailored advice, and free trial/demo options. (Source: FalkorDB Website)

What pricing plans does FalkorDB offer?

FalkorDB offers a FREE plan for MVPs, a STARTUP plan starting from /1GB/month (includes TLS and automated backups), a PRO plan from 0/8GB/month (includes cluster deployment and high availability), and an ENTERPRISE plan with tailored pricing and features like VPC and 24/7 support. (Source: FalkorDB Website)

What features are included in the PRO plan?

The PRO plan starts from 0/8GB/month and includes advanced features such as cluster deployment and high availability, making it suitable for production-grade security analytics. (Source: FalkorDB Website)

How does FalkorDB's open-source model benefit users?

FalkorDB's open-source licensing encourages community collaboration and transparency, allowing users to contribute, audit, and extend the platform for their security and analytics needs. (Source: FalkorDB Website)

What customer feedback has FalkorDB received regarding 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, highlighting its frictionless user experience. (Source: FalkorDB Case Studies)

How can I get started with FalkorDB for security analytics?

You can sign up for FalkorDB Cloud, try a free instance, run it locally with Docker, or schedule a demo for a personalized walkthrough. Comprehensive guides and community support are available to help you get started quickly. (Source: FalkorDB Demo Page)

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6 Reasons you need a graph to store your security data

6 Reasons you need graphs to store security data

TL;DR: Graph Storage for Security Data Architecture

  • Performance gap you can’t ignore: Your multi-hop security queries are degrading exponentially with relational JOINs.
  • Multi-tenancy without the headache: Run 10,000+ isolated tenant graphs in a single database instance with zero data commingling.

The Reality of Building Cloud Security at Scale

Cloud security teams face a fundamental data architecture problem. You’re correlating identity permissions, network topology, runtime signals, and vulnerability data across thousands of ephemeral workloads. Conventional relational approaches break down when you need sub-second response times for multi-hop security queries at scale.

Let’s examine why graph-based storage architectures solve core performance and scalability constraints in production security systems, and how you can implement these patterns in your current infrastructure.

1. Security Data Is Inherently Graph-Shaped

The Challenge
Security incidents span identity, workload, cloud, and network layers. The threat landscape is highly-interconnected by nature.
How it affects you
You waste time and context stitching data across SIEMs, IAM, vulnerability scanners, and network telemetry. Critical findings may go undetected.
Why choose graph
Graphs represent users, configurations, findings, services, roles, assets, and events as first-class objects and link them natively. You can ask, "What's the path from this leaked token to a production database?"
Business impact
Shorter time to detect lateral movement. Better root-cause analysis. No context gaps between domains.

2. Rigid Schemas Don’t Work in Cyber

The Challenge
Security datasets are fluid. You need the freedom to introduce new data types as well as to update the meta data of existing ones.
How it affects you
You waste time and efforts modifying rigid schemas, running migrations, and extensive testing.
Why choose graph
Data model can evolve per business needs without downtime or rework, solidifying your solution for future scaling.
Business impact
No migration-induced slowdowns. You stay responsive to changes in your attack surface.

496× Faster Than Neo4j at Peak Load

These results represent aggregate traversal queries common in security analytics workloads. They confirm FalkorDB can deliver consistent low-latency performance under load across real-world graph workloads.

FalkorDB Performance Cards

LATENCY

(Lower is Better)

Superior Latency: 496x faster

FalkorDB
Competition
36ms
469ms
P50
74ms
13969ms
P95
83ms
41157ms
P99

MEMORY USAGE

(Lower is Better)

6x Better performance, Lower overall costs

FalkorDB
Competition
100MB
FalkorDB
600MB
Competition

3. JOINs Kill Performance at Scale

The Challenge
Security queries are multi-hop by design. "Where do we have the combination of misconfiguration + high-severity CVE + public exposure across accounts or environments?" requires traversing multiple hops across different data entities.
How it affects you
In relational systems, multiple JOINs are unavoidable and are the reason for poor query performance time.
Why choose graph
FalkorDB represents graph using sparse matrices and performing efficient graph traversals on native graph data using linear algebra operations.
Business impact
Consistent milliseconds query time. Real-time detection and investigation. Smooth user experience for user-interactive applications.

4. Multi-Tenancy Without Isolation Breaks Trust

The Challenge
You run a multi-tenant security platform and need to ensure full tenant isolation, avoiding customers' data commingling in the same database or needing to spin up a dedicated database for every new customer.
How it affects you
You either introduce risk of data leakage or waste infrastructure resources on isolated stacks.
Why choose graph
FalkorDB can manage 10,000+ isolated graph tenants per database. Each tenant gets a private namespace and query surface.
Business impact
Zero tenant data commingling. Minimal DevOps overhead. Efficient scaling of your infrastructure as you grow.

Linear Scale to 120,000 QPS+ Across Tenants

Clustered FalkorDB scales from 20k QPS (1 node) to 120k QPS (6 nodes) while spreading multi-graph workload across multiple nodes.

FalkorDB QPS Linear Scalability line-chart

5. Memory and Compute Costs Spiral Out

The Challenge
Security telemetry grows non-linearly. You need to process growing volumes without ballooning infrastructure.
How it affects you
You burn cycles and budget on vCPUs and RAM just to stay ahead of ingestion and query backlogs.
Why choose graph
FalkorDB's unique sparse matrix core uses fewer resources to represent and traverse graphs. There are other built-in features that can reduce your memory footprint to lower your costs.
Business impact
Lower memory footprint and better query performance will lower your cloud spend.

6. Incomplete Context = Missed Threats

The Challenge
You receive thousands of alerts daily. Prioritization and correlation are impossible without context.
How it affects you
Analysts burn time on low-value triage. Real threats blend in with noise.
Why choose graph
Graphs enable end-to-end context resolution. For example, you can correlate IAM misconfigurations with workload vulnerabilities and data exposure paths.
Business impact
Fix the most important incidents first. Fewer false positives. Higher fidelity threat models.

Why FalkorDB?

FalkorDB is purpose-built for real-time threat modeling, attack path analysis, and multi-tenant security workloads. Engineered to model complex, evolving relationships and deliver sub-millisecond query performance across billions of edges.

Ready to validate your worst-case query?

Run it locally, populate it with your real production data, and measure the latency yourself.

Schedule a private demo
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Guy Korland

Guy Korland serves as CEO at FalkorDB, where he drives graph database architecture for generative AI and retrieval-augmented generation workflows. He holds a PhD in Computer Science from Tel Aviv University and brings over 20 years of experience in database engineering. He previously led Redis’ incubation arm as SVP & CTO, oversaw platform architecture as GM & CTO at Stor.ai (Self-Point), co-founded and served as CTO of Shopetti, and directed R&D as VP at GigaSpaces.