Get scalable vector ANN search with Milvus, a cloud-native database for founders, backed by 45k+ GitHub stars
advancedโฑ 1-2 days๐ต Free + LLM API costs
45,361 stars4,137 forksGoQuality 9/10Updated 7/24/2026100% free ยท open source
What it is
Use Milvus to find similar data in seconds.
What you can make with it
Automations that compare customer data in your database and trigger actions based on similarities, like adding new Stripe subscribers to Notion.
How it helps
It helps you scale vector search, making it efficient to compare large datasets in real-time, something you can't do manually or with smaller tools.
Real use case example
"A founder wants to add new users to a loyalty program based on their purchase history. She uses Milvus to quickly compare new users with existing members and sends personalized recommendations to those with similar buying behavior."
If you're new
Beginners should pick this up when they need a simple way to compare similar data in their database.
If you're senior
Senior engineers and dev teams reach for Milvus when building massive-scale applications that require fast vector search and similarity calculations.
Common confusion cleared up
Don't confuse Milvus with other databases โ it's specifically designed for fast and scalable vector search and similarity calculations.
Best inside these AI tools
Codex CLIGPT (ChatGPT)Any AI Client
Pairs with
Stripe webhookNotion databaseClaude Code
Why we list it on WorkflowStacks: We include Milvus because it's a best-in-class, open-source vector search database for scalable projects.
What it does
Milvus is a cloud-native database that enables scalable vector Approximate Nearest Neighbors (ANN) search for founders to efficiently manage and query large datasets of vectors.
โขBuilding a recommendation system that requires fast and accurate similarity searches
โขDeveloping a computer vision application that needs to efficiently query large datasets of image embeddings
โขCreating a natural language processing model that requires scalable semantic search capabilities
Quick start
1Create a new Milvus server instance by running the command: `docker run -d --name milvus-db -p 19530:19530 -v /tmp/milvus:/var/lib/milvus milvusdb/milvus:latest`
2Use the Milvus Python SDK to connect to the server: `from milvus import Milvus, Client`
3Create a new index and insert data into the Milvus database: `client.create_index('test', 'ivf_sq8')` and `vectors = [[1.0, 2.0], [3.0, 4.0]]`
4Perform a vector search query: `client.search('test', 'ivf_sq8', vectors)`
Heads up: Make sure to allocate sufficient GPU resources when running Milvus, as it requires significant GPU memory to perform efficient vector searches, and ensure that the Docker container has the necessary permissions to access the host machine's GPU
Saves to your device
Topics
anns
cloud-native
diskann
distributed
embedding-database
embedding-similarity
embedding-store
faiss
golang
hnsw
image-search
llm
nearest-neighbor-search
rag
vector-database
vector-search
vector-similarity
vector-store
What's inside โ free to inspect
No purchase needed
Read the entire source before you build โ unlike paid marketplaces that hide it behind a buy button.