Frequently Asked Questions

Product Information & Multigraph Topology

What is FalkorDB and what makes it unique as a graph database?

FalkorDB is a high-performance, open-source graph database designed for managing complex, interconnected data in real-time or interactive environments. It stands out for its ultra-low latency, linear scalability, and robust multi-tenancy, supporting over 10,000 isolated graphs per deployment. FalkorDB is optimized for advanced AI applications, such as GraphRAG and agent memory, and is trusted by organizations in healthcare, media, and AI development. Learn more.

How does FalkorDB's multigraph topology support scalability and isolation?

FalkorDB's multigraph topology enables each graph to be fully isolated, ensuring that queries target a single graph key. This design eliminates the need for second-label filters and reduces the risk of data leakage. The architecture allows for linear throughput growth as compute resources are added, with minimal coordination overhead across shards. This makes FalkorDB ideal for multi-tenant SaaS applications and large-scale enterprise deployments. Source

Is the multigraph feature available in the open-source version of FalkorDB?

Yes, the multigraph feature is fully available in the open-source version of FalkorDB, with no distinction from the managed service. This ensures all users can benefit from robust multi-tenancy and isolation capabilities. Source

How does FalkorDB handle query isolation and prevent data leakage?

FalkorDB ensures query isolation by targeting a single graph key for each query. This approach removes the need for second-label filters and significantly reduces the risk of data leakage between tenants or graphs. Source

Can I run queries across multiple graphs in FalkorDB?

No, queries in FalkorDB target a single graph. Cross-graph queries are not supported within one query execution, ensuring strong isolation and performance. Source

Is there any overhead when adding more graphs in FalkorDB?

Adding more graphs increases memory usage and snapshot duration, but query latency remains unaffected since each query targets a single graph. This allows for efficient scaling without performance degradation. Source

Are multi-tenant graphs a good mechanism for sharding in FalkorDB?

No, multi-tenancy in FalkorDB manages multiple graphs on a single instance, while sharding refers to splitting key-space across machines for horizontal scaling. Both can be used together for optimal scalability. Source

How does FalkorDB scale read operations?

You can add multiple replicas per master in FalkorDB to scale read operations. Each replica increases read throughput proportionally, supporting high QPS workloads. Source

Is there full isolation between masters and replicas in FalkorDB?

Yes, queries run locally on the target master or replica. Read queries do not affect other nodes; only writes trigger replication to replicas, ensuring strong isolation and consistency. Source

What are the observed performance metrics for FalkorDB's multigraph topology?

In a real-world test, a single instance (16 cores, 629 isolated graphs) achieved ≈25,000 QPS. A 3-master cluster (48 cores) reached ≈60,000 QPS, and with 3 masters + 3 replicas (96 cores), throughput doubled to ≈120,000 QPS, demonstrating near-linear scaling. Source

How does FalkorDB handle replica lag for read operations?

Replica lag in FalkorDB is negligible for reads, with only a brief propagation delay from master to replica. Write integrity remains master-bound, ensuring data consistency. Source

What is the primary purpose of FalkorDB's multigraph topology?

The primary purpose is to provide scalable, isolated graph workloads for multi-tenant SaaS and enterprise environments, enabling high throughput and strong data isolation for each tenant or application. Source

Who is FalkorDB designed for?

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

What are some real-world use cases for FalkorDB?

FalkorDB is used for Text2SQL (natural language to SQL on complex schemas), security graphs (CNAPP, CSPM, CIEM), GraphRAG (advanced graph-based retrieval), agentic AI and chatbots, and real-time fraud detection. Source

How does FalkorDB support AI and GenAI applications?

FalkorDB is optimized for AI use cases such as GraphRAG and agent memory, enabling intelligent agents and chatbots with real-time adaptability and accurate, multi-tenant RAG solutions. Source

What technical documentation is available for FalkorDB?

FalkorDB provides comprehensive technical documentation and API references at docs.falkordb.com, including setup guides, advanced configurations, and integration instructions. Source

Does FalkorDB offer an API?

Yes, FalkorDB provides a robust API with detailed references and guides available in the official documentation. This enables seamless integration into developer workflows. 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. Source

How does FalkorDB ensure security and compliance?

FalkorDB is SOC 2 Type II compliant, meeting rigorous standards for security, availability, processing integrity, confidentiality, and privacy. This ensures enterprise-grade protection for sensitive data. Source

What support and onboarding resources are available for FalkorDB?

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

Performance & Competitive Comparison

How does FalkorDB's performance compare to Neo4j?

FalkorDB offers up to 496x faster latency and 6x better memory efficiency compared to Neo4j. It also supports flexible horizontal scaling and includes multi-tenancy in all plans. Source

How does FalkorDB compare to AWS Neptune?

FalkorDB is open source, supports multi-tenancy, and provides better latency performance and memory efficiency than AWS Neptune, which is proprietary and lacks multi-tenancy support. Source

What are the advantages of FalkorDB over TigerGraph and ArangoDB?

FalkorDB delivers faster latency, more efficient memory usage, and flexible horizontal scaling compared to TigerGraph and ArangoDB, making it ideal for performance-critical and large-scale applications. Source

What pain points does FalkorDB solve for its users?

FalkorDB addresses trust and reliability in LLM-based applications, scalability and data management challenges, alert fatigue in cybersecurity, performance limitations of competitors, and regulatory compliance needs. 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, better user experience, regulatory compliance, and support for advanced AI applications. Source

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 frictionless user experience. AdaptX Case Study, 2Arrows Feedback

Who are some of FalkorDB's customers?

Notable customers include AdaptX (healthcare analytics), XR.Voyage (media/entertainment), and Virtuous AI (ethical AI development). Case Studies

What industries are represented in FalkorDB's case studies?

Industries include healthcare, media and entertainment, and artificial intelligence/ethical AI development. Source

How long does it take to implement FalkorDB and how easy is it to start?

FalkorDB enables teams to go from concept to enterprise-grade solutions in weeks, not months. Users can sign up for FalkorDB Cloud, try a free instance, or run locally with Docker. Comprehensive documentation and community support are available for onboarding. Source

What pricing plans does FalkorDB offer?

FalkorDB offers a FREE plan for MVPs, a STARTUP plan 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 custom pricing and features like VPC and 24/7 support. Source

What compliance certifications does FalkorDB have?

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

What are the key capabilities and benefits of FalkorDB?

Key capabilities include support for 10,000+ multi-graphs, open-source licensing, linear scalability, ultra-low latency, GraphRAG and agent memory optimization, and flexible deployment (cloud/on-prem). Benefits include trust, reliability, scalability, enhanced user experience, and regulatory compliance. Source

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. Source

What customer success stories are available for FalkorDB?

Case studies include AdaptX (healthcare analytics), XR.Voyage (media/entertainment), and Virtuous AI (ethical AI development), each leveraging FalkorDB for high-performance, scalable, and reliable data solutions. Case Studies

Multigraph Topology for Scalable, Isolated Graph Workloads

Multigraph Topology for Scalable, Isolated Graph Workloads

Recap

FalcorDB’s team assessed how a multi-tenant graph database handles an access-permission workload and whether throughput scales when moving from a single server to a clustered deployment. The workshop used a real-world pattern—“Does user X have permission to file Y?”—and measured query-per-second (QPS) under three hardware configurations.

Test setupCoresGraph layoutQPS (mean)Scaling reasoning
Single instance16629 isolated graphs on one node≈ 25 kBaseline
3-master cluster48Graphs distributed by key-space slots across three masters≈ 60 kDeductive: tripling compute raised throughput ~2.4×, close to linear
3 masters + 3 replicas96Masters handle writes; replicas added as read targets≈ 120 kInductive: doubling compute again doubled read throughput, sustaining linear trend

Key Observations

  • Linear throughput growth. Each 32-core increment raised capacity roughly in proportion to added compute, indicating minimal coordination overhead across shards.

  • Graph isolation at query time. Every query targets a single graph key, removing the need for second-label filters and reducing risk of data leakage.

  • Replica lag is negligible for reads. The team noted only a brief propagation delay from master to replica; write integrity remains master-bound.

Workshop Q&A

Can I add more replicas to achieve more operations per second?

Yes. You can add multiple replicas per master to scale read operations. Each replica increases read throughput proportionally.

Is the multigraph functionality available in the open-source version of FalkorDB?

Yes. The multigraph feature is fully available in the open-source version without distinction from the managed service.

Is there full isolation between masters and replicas?

Yes. Queries run locally on the target master or replica. Read queries don’t affect other nodes; only writes trigger replication to replicas.

Are multi-tenant graphs a good mechanism for sharding?

No. Multi-tenancy manages multiple graphs on a single instance. Sharding refers to splitting key-space across machines for horizontal scaling.

Can I run a query distributed between multiple graphs and return one result set?

No. Queries in FalkorDB target a single graph. Cross-graph queries are not supported within one query execution.

Is there any overhead when adding more graphs?

Memory usage increases and snapshots take longer, but query latency remains unaffected since each query targets a single graph.

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.