Memgraph vs Redis Comparison: Reviews, Features, Pricing & Alternatives in 2026

Detailed side-by-side comparison to help you choose the right solution for your team

Updated Apr 2026 8 min read

Memgraph

0.0 (0 reviews)

Memgraph is a high-performance in-memory graph database that provides real-time data processing and streaming analytics for developers building complex, interconnected applications with Cypher query language support.

Starting at Free
Free Trial 30 days
VS

Redis

0.0 (0 reviews)

Redis is an open-source, in-memory data store used as a high-performance database, cache, message broker, and streaming engine to power real-time applications with sub-millisecond latency.

Starting at Free
Free Trial 14 days

Quick Comparison

Feature Memgraph Redis
Website memgraph.com redis.io
Pricing Model Freemium Freemium
Starting Price Free Free
FREE Trial ✓ 30 days free trial ✓ 14 days free trial
Free Plan ✓ Has free plan ✓ Has free plan
Product Demo ✓ Request demo here ✓ Request demo here
Deployment saas on-premise desktop cloud on-premise desktop
Integrations Kafka Redpanda Pulsar Docker Kubernetes Python Rust C++ Tableau Power BI AWS Google Cloud Microsoft Azure Docker Kubernetes Prometheus Grafana Python Node.js Java
Target Users small-business mid-market enterprise small-business mid-market enterprise
Target Industries finance cybersecurity logistics
Customer Count 0 0
Founded Year 2016 2011
Headquarters London, UK Mountain View, USA

Overview

M

Memgraph

Memgraph is an in-memory graph database designed to help you handle complex, highly connected data with sub-millisecond latency. You can build applications that require real-time insights, such as fraud detection systems, recommendation engines, or network monitoring tools. Because it stores data in-memory, you get significantly faster performance than traditional disk-based databases while maintaining ACID compliance for data reliability.

You can easily transition to Memgraph if you are already familiar with the Cypher query language, as it is fully compatible. The platform allows you to ingest data directly from streaming sources like Kafka or Pulsar, enabling you to run graph algorithms on live data as it arrives. Whether you are a developer at a startup or an engineer at an enterprise, you can deploy it on-premise or in the cloud to scale your graph-based applications efficiently.

strtoupper($product2['name'][0])

Redis

Redis helps you build lightning-fast applications by storing data in-memory rather than on traditional disks. You can use it as a primary database, a high-speed cache to offload your main database, or a message broker for real-time communication between services. It supports diverse data structures like strings, hashes, lists, and sets, giving you the flexibility to handle complex data requirements without sacrificing speed.

You can deploy it as an open-source solution or choose the managed Redis Cloud service to handle scaling and availability automatically. Whether you are building a real-time leaderboard, a session store, or a high-traffic e-commerce site, it provides the low-latency performance your users expect. The platform scales effortlessly from small development projects to massive enterprise deployments requiring millions of operations per second.

Overview

M

Memgraph Features

  • In-Memory Engine Access your data at lightning speeds with an in-memory storage engine designed for high-throughput and low-latency applications.
  • Cypher Compatibility Use the industry-standard Cypher query language to build and migrate your graph applications without learning a new syntax.
  • Real-time Streaming Connect directly to Kafka, Redpanda, or Pulsar to run complex graph analytics on your data streams as they happen.
  • MAGE Library Run advanced graph algorithms like PageRank or community detection using the built-in Memgraph Advanced Graph Extensions library.
  • ACID Compliance Ensure your data remains consistent and reliable with full ACID transactional support even during high-concurrency workloads.
  • Multi-Language Support Write your custom procedures and transformations in Python, C++, or Rust to extend the database functionality.
strtoupper($product2['name'][0])

Redis Features

  • In-Memory Data Store. Store your data entirely in RAM to achieve sub-millisecond response times for your most demanding applications.
  • Versatile Data Structures. Use native support for lists, sets, sorted sets, and hashes to map your application data naturally.
  • Built-in Replication. Configure primary-replica setups to ensure your data remains available and scales across multiple nodes easily.
  • Redis Pub/Sub. Build real-time chat apps and notification systems using high-performance messaging patterns between your services.
  • Data Persistence. Balance performance and safety by choosing between point-in-time snapshots or append-only files for disk backups.
  • Redis Stack Modules. Extend your database capabilities with integrated search, document mapping, and graph processing in a single tool.

Pricing Comparison

M

Memgraph Pricing

Community
$0
  • In-memory graph database
  • Cypher query language
  • MAGE algorithm library
  • Stream processing (Kafka/Pulsar)
  • ACID transactions
  • Community support
R

Redis Pricing

Fixed (Essentials)
$0
  • Up to 30MB storage
  • 30 concurrent connections
  • Basic database monitoring
  • Community support
  • Single zone deployment

Pros & Cons

M

Memgraph

Pros

  • Extremely low latency for deep relationship queries
  • Seamless integration with existing Kafka data streams
  • Easy migration for users familiar with Neo4j
  • Strong support for custom Python procedures
  • Efficient memory management for large datasets

Cons

  • Memory costs can scale with data size
  • Smaller community compared to legacy graph databases
  • Enterprise features require a custom quote
A

Redis

Pros

  • Extremely low latency for real-time data needs
  • Simple API that is easy for developers to learn
  • Extensive library support for almost every programming language
  • Highly flexible data structures for various use cases

Cons

  • Memory costs can become high at massive scales
  • Requires careful configuration for guaranteed data persistence
  • Learning curve for complex clustering and sharding
×

Please claim profile in order to edit product details and view analytics. Provide your work email @productdomain to receive a verification link.