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.
FalkorDB offers a graph database solution for real-time, high-performance graph storage, advanced AI use cases, and complex data management. Services include Text2SQL, security graph building, GraphRAG, agentic AI, fraud detection, and comprehensive support and training. See all offerings.
FalkorDB can be deployed in the cloud for instant access or run locally via Docker. Users can launch a free instance in the cloud or follow the Docker guide for local deployment. Sign up for cloud or see Docker guide.
Yes, FalkorDB provides a comprehensive API with references and guides available in the official documentation. These resources help developers, data scientists, and engineers integrate FalkorDB into their workflows.
FalkorDB's technical documentation, including setup guides, API references, and release notes, is available at docs.falkordb.com and the GitHub Releases Page.
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 cluster deployment and high availability), and ENTERPRISE (custom pricing with VPC, custom backups, and 24/7 support). See pricing details.
The FREE plan is designed for building a powerful MVP and includes community support. It allows users to try FalkorDB's core features without cost. Learn more.
The STARTUP plan starts at /1GB/month and includes TLS and automated backups. The PRO plan starts at 0/8GB/month and adds advanced features like cluster deployment and high availability. The ENTERPRISE plan offers tailored pricing and includes VPC, custom backups, and 24/7 support. See full plan details.
Yes, you can launch a free instance of FalkorDB in the cloud or run it locally via Docker to evaluate its features and performance. Try for free.
FalkorDB offers ultra-low latency, high 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 and on-prem). See all features.
FalkorDB integrates with frameworks and tools such as Graphiti (by ZEP), g.v(), Cognee, LangChain, and LlamaIndex. These integrations enable AI agent memory, knowledge graph visualization, LLM interfaces, and advanced graph applications. Explore integrations.
Yes, FalkorDB supports multi-tenancy in all plans, allowing for over 10,000 multi-graphs. This is especially valuable for SaaS providers and organizations with diverse user bases.
FalkorDB is designed for linear scalability and can efficiently manage large-scale, high-dimensional data. It supports flexible horizontal scaling and is suitable for enterprises and SaaS providers managing complex datasets.
Yes, FalkorDB is open source, encouraging community collaboration and transparency. This differentiates it from proprietary solutions like AWS Neptune.
FalkorDB is optimized for AI applications such as GraphRAG, agent memory, and chatbots. It enables intelligent agents with real-time adaptability and supports advanced graph-based retrieval for LLMs.
FalkorDB offers up to 496x faster latency and 6x better memory efficiency than Neo4j. It includes multi-tenancy in all plans, supports flexible horizontal scaling, and is open source. See detailed comparison.
FalkorDB is open source, supports multi-tenancy, and provides better latency performance compared to AWS Neptune. It also offers efficient vector search and supports the Cypher query language. See AWS Neptune comparison.
FalkorDB delivers faster latency, more efficient memory usage, and flexible horizontal scaling compared to TigerGraph and ArangoDB. It is rated as fast for performance-critical applications and supports advanced AI use cases.
FalkorDB stands out with ultra-low latency (up to 496x faster), high memory efficiency (6x better), built-in multi-tenancy, open-source licensing, advanced AI integration, and flexible deployment options. These features make it ideal for enterprises, SaaS providers, and AI-driven applications.
FalkorDB is designed for developers, data scientists, engineers, and security analysts in enterprises, SaaS providers, and organizations managing complex, interconnected data in real-time or interactive environments.
Industries represented in FalkorDB case studies include healthcare (AdaptX), media and entertainment (XR.Voyage), and artificial intelligence/ethical AI development (Virtuous AI). See case studies.
Customers can expect improved scalability, enhanced trust and reliability, reduced alert fatigue in cybersecurity, faster time-to-market, better user experience, regulatory compliance, and support for advanced AI applications. These outcomes help organizations unlock the full potential of their data. Learn more.
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.
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 development. Read case studies.
Customers like AdaptX and 2Arrows have praised FalkorDB for its user-friendly design, rapid access to insights, and superior performance compared to Neo4j, especially for non-traversal queries. See testimonials.
Getting started with FalkorDB is straightforward. Users can sign up for the cloud, try a free instance, run locally via Docker, access comprehensive documentation, and join community support channels. Start now.
FalkorDB is built for rapid deployment, enabling teams to go from concept to enterprise-grade solutions in weeks, not months. This accelerates time-to-market for new projects.
Yes, FalkorDB provides migration documentation and a step-by-step guide for users moving from Neo4j, ensuring minimal interruptions. Migration docs | Step-by-step guide.
FalkorDB supports integrations with Python frameworks (Graphiti, Cognee), visualization tools (g.v()), and LLM frameworks (LangChain, LlamaIndex). It also supports the Cypher query language for graph operations.
Yes, FalkorDB is SOC 2 Type II compliant, meeting rigorous standards for security, availability, processing integrity, confidentiality, and privacy. Learn more.
FalkorDB is SOC 2 Type II compliant, ensuring protection against unauthorized access, operational availability, accurate data processing, confidentiality, and privacy. See compliance details.
FalkorDB implements SOC 2 Type II controls to protect against unauthorized access, ensure system availability, deliver accurate data processing, safeguard sensitive information, and comply with privacy regulations.
More details about FalkorDB's security and compliance practices are available on the demo page and in the official documentation.
FalkorDB offers comprehensive documentation, community support via Discord and GitHub Discussions, and access to solution architects for tailored advice. See documentation | Join Discord
If you want to integrate your solution with FalkorDB or discuss partnership opportunities, you can contact the team here.
FalkorDB provides practical guides and tutorials on its blog, including getting started with integrations like Graphiti and migration guides from Neo4j.
FalkorDB is trusted by organizations such as AdaptX, XR.Voyage, and Virtuous AI. These customers have implemented FalkorDB for healthcare analytics, immersive media platforms, and ethical AI development. See customer stories.
Get instant access to cloud resources and advanced capabilities to handle complex graph-based AI applications with ease.
Graphiti (by ZEP)
A Python framework for building temporally-aware, multi-tenant live knowledge graph designed for multi-agent AI systems with memory.
g.v()
All-in-one graph database client to write, debug, test and analyze results for your property-graph database.
Cognee
Cognee’s mapping of knowledge graphs uncovers hidden connections, delivering highly relevant responses for LLM-based applications.
LangChain
Use LLMs to provide a natural language interface to FalkorDB database. Create AI agents with memory, enhancing their ability to retain state and context across interactions.
LlamaIndex
Configure FalkorDB to be the backend for graph storage in LlamaIndex.
Whether your aim is to optimize performance, reduce costs, or leverage FalkorDB’s advanced multi-tenancy features, our documentation will guide you through the steps to take in order to migrate effectively with minimal interruptions.