Keywords AI

Milvus vs Supabase

Compare Milvus and Supabase side by side. Both are tools in the Vector Databases category.

Quick Comparison

	Milvus	Supabase
Category	Vector Databases	Vector Databases
Pricing	Open Source	freemium
Best For	Organizations that need vector search at billion-scale with high throughput	Full-stack developers building AI apps
Website	milvus.io	supabase.com
Key Features	Billion-scale vector search GPU-accelerated indexing Distributed architecture Multiple index types Cloud and self-hosted options	pgvector hosting Managed Postgres Real-time Auth
Use Cases	Billion-vector similarity search Large-scale recommendation systems Image and video retrieval Genomics and scientific computing Enterprise-scale RAG systems	—

When to Choose Milvus vs Supabase

Choose Milvus if you need

Billion-vector similarity search
Large-scale recommendation systems
Image and video retrieval

Pricing: Open Source

How to Choose a Vector Databases Tool

Key criteria to evaluate when comparing Vector Databases solutions:

Query performanceSearch latency and throughput at your expected data scale and query volume.

ScalabilityHow well the database handles growing from thousands to billions of vectors.

Hosting modelFully managed cloud, self-hosted, or embedded options depending on your infrastructure needs.

Filtering supportAbility to combine vector similarity search with metadata filters efficiently.

Integration ecosystemNative integrations with popular frameworks like LangChain, LlamaIndex, and Haystack.

About Milvus

Milvus is an open-source vector database built for scalable similarity search, capable of handling billions of vectors. Backed by the Zilliz company, Milvus supports multiple index types (IVF, HNSW, DiskANN), GPU-accelerated search, and multi-tenancy. Zilliz Cloud offers a fully managed version with automatic scaling. Milvus is widely used in enterprise deployments requiring high-throughput vector search at scale.

View Milvus profile →Visit website

About Supabase

The #1 platform for pgvector. Open-source Firebase alternative with built-in vector search via Postgres.

View Supabase profile →Visit website

What is Vector Databases?

Purpose-built databases for storing, indexing, and querying high-dimensional vector embeddings used in semantic search, RAG, and recommendation systems.

Browse all Vector Databases tools →

Frequently Asked Questions

What is a vector database?▾

A vector database stores high-dimensional numerical representations (embeddings) of data like text, images, or audio, and enables fast similarity search across millions or billions of vectors using approximate nearest neighbor algorithms.

Do I need a dedicated vector database or can I use pgvector?▾

For small to medium datasets (under 10 million vectors), pgvector in PostgreSQL works well and avoids adding another service. For larger datasets or when you need advanced features like hybrid search and real-time indexing, a dedicated vector database is recommended.

How do I choose the right embedding model for my vector database?▾

Match the embedding model to your use case. For general text search, models like OpenAI text-embedding-3 or Cohere embed-v3 work well. For domain-specific applications, consider fine-tuned models. Always benchmark with your actual data.