Convex vs Memgraph 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

Convex

0.0 (0 reviews)

Convex is a full-stack TypeScript development platform that replaces your traditional database, server-side functions, and caching layers with a unified, real-time backend-as-a-service to accelerate application building.

Starting at Free
Free Trial NO FREE TRIAL
VS

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

Quick Comparison

Feature Convex Memgraph
Website convex.dev memgraph.com
Pricing Model Freemium Freemium
Starting Price Free Free
FREE Trial ✘ No free trial ✓ 30 days free trial
Free Plan ✓ Has free plan ✓ Has free plan
Product Demo ✓ Request demo here ✓ Request demo here
Deployment saas saas on-premise desktop
Integrations GitHub Netlify Vercel Clerk Auth0 Stripe Sentry Figma Kafka Redpanda Pulsar Docker Kubernetes Python Rust C++ Tableau Power BI
Target Users solopreneur small-business mid-market small-business mid-market enterprise
Target Industries finance cybersecurity logistics
Customer Count 0 0
Founded Year 2021 2016
Headquarters San Francisco, USA London, UK

Overview

C

Convex

Convex provides you with a complete backend for your web applications, eliminating the need to manage separate databases, middleware, or complex state management. You can write your entire backend in TypeScript, using familiar syntax to define schemas, functions, and data mutations. It automatically handles data persistence, real-time synchronization, and server-side logic execution without you ever touching infrastructure or worrying about scaling.

You can build reactive applications where your UI stays perfectly in sync with your data automatically. It solves the common headache of 'glue code' by providing a unified environment where your frontend and backend share types and logic seamlessly. Whether you are building a collaborative document editor or a complex SaaS dashboard, you get a production-ready environment that scales from your first prototype to millions of users.

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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.

Overview

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Convex Features

  • Real-time Subscriptions Keep your UI in sync automatically with built-in data subscriptions that push updates to your frontend instantly.
  • ACID Transactions Ensure your data remains consistent with fully transactional functions that prevent partial updates or corrupted states.
  • TypeScript Integration Share types between your frontend and backend to catch errors early and enjoy full autocomplete across your stack.
  • Scheduled Functions Run background jobs and delayed tasks directly within your backend without setting up external cron services.
  • File Storage Upload and serve large files like images or videos using integrated storage that links directly to your database.
  • Vector Search Build AI-powered features easily with native vector search capabilities for semantic queries and RAG-based applications.
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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.

Pricing Comparison

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Convex Pricing

Free
$0
  • 1 GB database storage
  • 10 GB file storage
  • 1 million monthly function calls
  • Community support
  • Real-time data sync
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Memgraph Pricing

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

Pros & Cons

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Convex

Pros

  • Eliminates the need for complex state management libraries
  • Exceptional developer experience with end-to-end TypeScript safety
  • Automatic scaling handles traffic spikes without manual intervention
  • Unified backend reduces the amount of boilerplate code required

Cons

  • Requires commitment to the Convex ecosystem and architecture
  • Limited to JavaScript and TypeScript for backend logic
  • No local-only offline mode for mobile-first applications
A

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
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