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Dragonfly

High-performance in-memory data store with Redis/Memcached compatibility and vector search for AI

★ 30,790 GitHub BSL-1.1 in-memory-databaseredis-compatiblevector-searchcachinghigh-performancememcached-alternative RAG & Knowledge

Overview

Dragonfly is an in-memory data store built for modern application workloads. Fully compatible with Redis and Memcached APIs, it requires no code changes to adopt. Dragonfly delivers 25X more throughput, higher cache hit rates with lower tail latency, and runs on up to 80% less resources. With built-in vector search support via HNSW indexes, Dragonfly is ideal for AI applications requiring fast similarity search alongside caching and real-time data serving. It supports replication, persistence, Lua scripting, and RESP2/RESP3 protocols. With 30k+ GitHub stars and widespread adoption, it is the most performant Redis alternative available.

Requirements

Min vCPU
1
Min RAM
4096 MB
Min Disk
10 GB
Rec vCPU
2
Rec RAM
2048 MB
Rec Disk
20 GB

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Docker Compose

# Generated by Run This Ai — docker-compose.yml
services:
  dragonfly:
    image: dragonflydb/dragonfly:latest
    restart: unless-stopped
    ports:
      - 8080:8080
    volumes:
      - ./data/dragonfly:/data

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Guides & articles

How to Run Dragonfly with Docker: A Quick Start Guide

Get Dragonfly running in under 5 minutes with Docker. Includes vector search setup, docker-compose, and performance tuning for AI workloads.

Jun 27, 2026

Dragonfly: The Ultra-Fast Redis Alternative Powering AI Workloads

Learn about Dragonfly — the high-performance Redis-compatible in-memory data store with vector search for AI workloads. 25X more throughput, 80% less resources.

Jun 27, 2026