Software Alternatives, Accelerators & Startups

Azure Cosmos DB VS FalkorDB

Compare Azure Cosmos DB VS FalkorDB and see what are their differences

Azure Cosmos DB logo Azure Cosmos DB

NoSQL JSON database for rapid, iterative app development.

FalkorDB logo FalkorDB

Build Fast and Accurate GenAI Apps with GraphRAG at Scale
  • Azure Cosmos DB Landing page
    Landing page //
    2023-03-16
  • FalkorDB
    Image date //
    2025-01-27

FalkorDB delivers an accurate, multi-tenant RAG solution powered by a low-latency, scalable graph database technology. Our solution is purpose-built for development teams working with complex, interconnected data—whether structured or unstructured—in real-time or interactive user environments.

FalkorDB

$ Details
freemium
Release Date
2023 December
Startup details
Country
Israel
Founder(s)
Guy Korland, Roi Lipman, Avi Avni
Employees
10 - 19

Azure Cosmos DB features and specs

  • Global Distribution
    Azure Cosmos DB allows for the distribution of data across multiple global regions, enhancing availability and delivering low-latency access to data for users around the world.
  • Multi-Model Support
    It supports multiple data models including document, graph, key-value, and column-family APIs, making it versatile for a variety of applications and use cases.
  • Automatic Scaling
    The database automatically scales up and down to meet the demands of application traffic, helping to manage workloads efficiently without manual intervention.
  • High Throughput and Low Latency
    Cosmos DB offers high performance with single-digit millisecond read and write latencies, ensuring fast access to data for applications.
  • Comprehensive SLAs
    Azure Cosmos DB provides industry-leading SLAs covering availability, throughput, consistency, and latency, offering strong guarantees for customers.
  • Integrated Security
    It includes robust security features such as SSL/TLS encryption, role-based access control, and integration with Azure Active Directory for secure data management.

Possible disadvantages of Azure Cosmos DB

  • Cost
    Azure Cosmos DB can be expensive, especially for high-throughput workloads and global distribution scenarios. Its pricing model based on provisioned throughput (RU/s) can add up quickly.
  • Complexity
    Managing and optimizing Cosmos DB can be complex, requiring a deep understanding of its configuration settings, partitioning strategies, and indexing to achieve optimal performance.
  • Vendor Lock-In
    As a proprietary service, using Cosmos DB tightly couples your application to Azure. This can make it difficult to migrate to other database solutions or cloud providers in the future.
  • Consistency Models
    Azure Cosmos DB supports multiple consistency levels which can introduce complexity in designing applications. Developers need to understand and choose the appropriate consistency level for their specific use case.
  • Limited Native Analytics
    Cosmos DB does not have built-in advanced analytics capabilities. Integrating with other services like Azure Synapse or Databricks may be necessary for sophisticated data analytics and reporting.

FalkorDB features and specs

  • Multi-Tenancy
    10K+ In a single instance
  • Low-Latency
    500x faster than Neo4j

Azure Cosmos DB videos

Azure Cosmos DB: Comprehensive Overview

More videos:

  • Review - Azure Friday | Azure Cosmos DB with Scott Hanselman
  • Tutorial - Azure Cosmos DB Tutorial | Globally distributed NoSQL database

FalkorDB videos

Auto generating of Knowledge Graph with MindGraph, FalkorDB & OpenAI

More videos:

  • Tutorial - Getting started with FalkorDB SaaS

Category Popularity

0-100% (relative to Azure Cosmos DB and FalkorDB)
Databases
86 86%
14% 14
NoSQL Databases
86 86%
14% 14
Graph Databases
75 75%
25% 25
Developer Tools
0 0%
100% 100

Questions and Answers

As answered by people managing Azure Cosmos DB and FalkorDB.

Which are the primary technologies used for building your product?

FalkorDB's answer:

C, Rust, Next.js

What makes your product unique?

FalkorDB's answer:

An ultra-low latency Graph Database

Why should a person choose your product over its competitors?

FalkorDB's answer:

x100 faster than the leading solutions

How would you describe your primary audience?

FalkorDB's answer:

Developers, Architects, Data scientists, CTOs

What's the story behind your product?

FalkorDB's answer:

An ultra-low latency Graph Database that perfects the Knowledge Graph for KG-RAG. Effectively overcoming the existing limitations of RAG for Large Language Models (LLM).

FalkorDB is the first queryable Property Graph database to use sparse matrices to represent the adjacency matrix in graphs and linear algebra to query the graph.

User comments

Share your experience with using Azure Cosmos DB and FalkorDB. For example, how are they different and which one is better?
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Social recommendations and mentions

Based on our record, Azure Cosmos DB should be more popular than FalkorDB. It has been mentiond 9 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

Azure Cosmos DB mentions (9)

  • Blazor server app, deployment options
    If you are writing the code maybe consider learning Cosmos DB it’s pretty easy to work with and there is a free tier. Also in my experience it’s much faster than a SQL database. Source: almost 2 years ago
  • Infrastructure as code (IaC) for Java-based apps on Azure
    Sometimes you don’t need an entire Java-based microservice. You can build serverless APIs with the help of Azure Functions. For example, Azure functions have a bunch of built-in connectors like Azure Event Hubs to process event-driven Java code and send the data to Azure Cosmos DB in real-time. FedEx and UBS projects are great examples of real-time, event-driven Java. I also recommend you to go through 👉 Code,... - Source: dev.to / over 2 years ago
  • Deploying a Mostly Serverless Website on GCP
    When debating the database solution for our application we were really seeking for a scalable serverless database that wouldn’t bill us for idle time. Options like AWS Athena, AWS Aurora Serverless, and Azure Cosmos DB immediately came to mind. We believed that GCP would have a comparable service, yet we could not find one. Even after consulting the GCP cloud service comparison documentation we were still unable... - Source: dev.to / almost 3 years ago
  • Which DB to use for API published on Azure?
    If you are looking for one to start with; you can try Cosmos: https://azure.microsoft.com/en-us/services/cosmos-db/. Source: about 3 years ago
  • Basic Setup for Azure Cosmos DB and Example Node App
    I have had an opportunity to work on a project that uses Azure Cosmos DB with the MongDB API as the backend database. I wanted to spend a little more time on my own understanding how to perform basic setup and a simple set of CRUD operations from a Node application, as well as construct an easy-to-follow procedure for other developers. - Source: dev.to / about 3 years ago
View more

FalkorDB mentions (3)

  • Semantic search alone won't solve relational queries in your LLM retrieval pipeline.
    Use a low-latency graph database: Integrate FalkorDB for its sparse matrix representation and optimized linear algebra-based traversals. Queries execute in milliseconds—critical for real-time AI interactions. - Source: dev.to / about 2 months ago
  • Graph database vs relational vs vector vs NoSQL
    In vector databases, data is stored as high-dimensional vector embeddings, which are numerical representations generated by machine learning models to capture the features of data. When querying, the input is converted into a vector embedding, and similarity searches are performed between the query vector and stored embeddings using distance metrics like cosine similarity or Euclidean distance to retrieve the most... - Source: dev.to / about 2 months ago
  • NoLiMA: GPT-4o achieve 99.3% accuracy in short contexts (<1K tokens), performance degrades to 69.7% at 32K tokens.
    For AI architects, integrating graph-native storage with LLMs isn’t optional—it’s imperative for building systems capable of robust, multi-hop reasoning at scale. - Source: dev.to / 3 months ago

What are some alternatives?

When comparing Azure Cosmos DB and FalkorDB, you can also consider the following products

ArangoDB - A distributed open-source database with a flexible data model for documents, graphs, and key-values.

neo4j - Meet Neo4j: The graph database platform powering today's mission-critical enterprise applications, including artificial intelligence, fraud detection and recommendations.

Redis - Redis is an open source in-memory data structure project implementing a distributed, in-memory key-value database with optional durability.

Memgraph - Memgraph is an open source graph database built for real-time streaming and compatible with Neo4j. Whether you're a developer or a data scientist with interconnected data, Memgraph will get you the immediate actionable insights fast.

MongoDB - MongoDB (from "humongous") is a scalable, high-performance NoSQL database.

TigerGraph DB - Application and Data, Data Stores, and Graph Database as a Service