FalkorDB%2FFalkorDB | Trendshift

Ultra-fast, Multi-tenant
Graph Database Powering GenAI

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FalkorDB graph visualization

Available On All Major Platforms

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10K+ MULTI-GRAPHS
(TENANTS)

OPEN SOURCE

LINEAR SCALABILITY

ULTRA-LOW LATENCY

GRAPHRAG & AGENT
MEMORY OPTIMIZED

REDIS-BASED

Purpose-built to Your Use Case

AI Retrieval

GraphRAG

Combine LLMs with domain-specific knowledge graphs to reduce hallucinations and enrich AI responses. Enable natural language queries, traceable retrieval logic, and hidden insight discovery for smarter decision-making and faster AI deployment.

Work with structured and unstructured data
Ontology auto-detection
Built-in agent orchestration

Personalized Systems

Agentic AI

Combine graph traversal with vector search to create personalized agentic AI applications. Connect user profiles, preferences, and activities to deliver accurate, explainable recommendations with near real-time adaptability to changing contexts.

Real-time applications
Rich context and memory
Persistent conversation history

Conversational Apps

Chatbots

Build context-aware chatbots by integrating knowledge graphs for entity extraction, fact linking, and relationship mapping. Enable real-time recommendations by correlating user behavior, product data, and session activity for dynamic responses.

Long-term memory context
Advanced retrieval augmentation
Smooth user experience

Investigations

Fraud Detection

Detect fraud rings by analyzing relationships between entities such as IPs, devices, and transactions. Use real-time graph analytics to uncover anomalies, track patterns across accounts, and adapt dynamically to evolving fraudulent behavior.

Real-time detection
Claims fraud analysis
Account fraud correlation

Threat Intelligence

Security Graph

Store security data in a flexible, schemaless form and query findings, vulnerabilities, assets, and related entities in near real time. With FalkorDB's scalable infrastructure, cyber and cloud security vendors can support multi-tenant SaaS and on-prem deployments for threat surfacing and analytics.

Interconnected security domains
Explainable graph traversals
Scalable data structures
Not seeing your use case? Let’s talk.

Compare FalkorDB Across Parameters That Matter.

Feature
FalkorDB
Neo4j
Multi-tenancy
Latency
Fast
Poor
Horizontal Scaling
Flexible
Limited
Vector Search
Limited
Cypher Support
Memory Efficiency
Highly Efficient
Baseline
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

Easily Migrate from Neo4j

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.

Migration docs
Step-by-step Guide
Benchmarks

Join Our Community

Engage with our community on GitHub for feedback and collaboration opportunities. Our comprehensive documentation covers everything from basic setup to advanced configurations, ensuring a smooth integration with your existing data architecture.

Join Our Discord
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Graph enthusiasts now online

To learn more about how to get started see FalkorDB documentation

Major Version 4.14.10 Released 🎉
FalkorDB Features
Multi-Graph / Multi-Tenancy
✓
Graph Access Control
✓
TLS
✓
VPC
✓
Cluster Deployment
✓
High Availability
✓
Multi-zone Deployment
✓
Scalability
✓
Continuous Persistence
✓
Automated Backups
Every 12 Hours
Cloud Providers
GCP, AWS, Azure
Support
24/7

Materials to Get You Started

FalkorDB CTA with FAQ

Stop 'deCyphering' Which
Graph Database is Better.

FalkorDB represents the first queryable property graph database using sparse matrices for adjacency matrix representation and linear algebra for graph queries. It leverages AVX acceleration for performance optimization and eliminates complex batch processing requirements.

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Frequently Asked Questions

FalkorDB offers horizontal scalability designed for distributed systems. Unlike Neo4j or other graph databases, which may require specific configurations for scaling, FalkorDB supports a pay-as-you-grow model that simplifies resource allocation as workloads increase.
GraphRAG-SDK offers robust ontology management features, including automated ontology generation from unstructured data. This capability allows for the automatic creation of structured representations of knowledge, facilitating more efficient data retrieval and integration. Automated ontology generation minimizes manual schema creation, streamlining development and keeping knowledge graphs up-to-date.
FalkorDB natively supports multi-tenant and multigraph environments with zero overhead, eliminating the need to manage multiple instances. This reduces DevOps complexity and associated licensing costs while maintaining full isolation and security.
Yes, FalkorDB integrates seamlessly with AI/ML pipelines by supporting GraphRAG workflows. This improves model accuracy and reduces inference latency without disrupting existing architectures.
FalkorDB supports industry-standard Cypher query language.