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What is Graph Story?

Organizations opting for a do-it-yourself approach to implement a graph database should expect a preparation period of approximately 2 to 3 months before reaching a production-ready status. On the other hand, utilizing Graph Story’s managed services can enable you to set up your operational database in mere minutes. You can uncover a range of graph use cases while also examining a comparative analysis between self-hosting and managed services. We support deployments within your current infrastructure, whether it is hosted on AWS, Azure, or Google Compute Engine, and we can accommodate any geographical area. If you need VPC peering or specific IP access restrictions, we can swiftly tailor our services to meet those requirements. For those interested in developing a proof of concept, launching a single enterprise graph instance can be accomplished in just a few clicks. If your needs shift towards scaling up to a high-availability production cluster, we are ready to provide support! Our graph database management tools are crafted to enhance your experience, allowing for easy monitoring of CPU, memory, and disk usage. Furthermore, you will have access to crucial configurations, logs, and the ability to back up your database and restore snapshots as needed. This degree of adaptability guarantees that your graph database management meets your operational needs seamlessly. Additionally, our support team is always available to help troubleshoot any issues, ensuring a smooth experience throughout your database management journey.

What is FalkorDB?

FalkorDB stands out as a remarkably fast, multi-tenant graph database specifically optimized for GraphRAG, delivering precise and relevant AI/ML results while effectively reducing hallucinations and enhancing overall efficiency. Utilizing sparse matrix representations in conjunction with linear algebra, it skillfully manages complex, interconnected datasets in real-time, which not only lowers the incidence of hallucinations but also improves the accuracy of responses generated by large language models. This database supports the OpenCypher query language, augmented by unique features that promote both expressive and efficient querying of graph data. Moreover, it includes integrated vector indexing and full-text search capabilities, enabling detailed search functions and similarity evaluations within a cohesive database environment. FalkorDB's architecture allows for multiple graphs to coexist within a single instance, thereby increasing security and performance for various tenants. Additionally, it ensures high availability through live replication, making certain that data remains consistently accessible, even during peak demand periods. This array of capabilities positions FalkorDB as an effective solution for organizations aiming to handle intricate graph data efficiently and reliably, making it an essential tool for data-driven decision-making.

Media

Media

Integrations Supported

Amazon Web Services (AWS)
Google Cloud Platform
Microsoft Azure
Google Compute Engine

Integrations Supported

Amazon Web Services (AWS)
Google Cloud Platform
Microsoft Azure
ChatGPT
Cognee
DSPy
Docker
Gemini
Gemini Enterprise
LangChain
Llama
Mem0
Netlify
OpenAI
Vercel

API Availability

Has API

API Availability

Pricing Information

$299 per month

Pricing Information

Pricing not provided

Supported Platforms

SaaS
On-Prem

Supported Platforms

SaaS

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Customer Service / Support

Standard Support
Web-Based Support

Training Options

Documentation Hub

Training Options

Documentation Hub
Online Training
On-Site Training

Company Facts

Organization Name

Graph Story

Date Founded

2014

Company Location

United States

Company Website

www.graphstory.com

Company Facts

Organization Name

FalkorDB

Date Founded

2023

Company Location

Israel

Company Website

www.falkordb.com

Categories and Features

Graph Databases

Not specified

Categories and Features

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