Keywords AI

Elasticsearch vs Supabase

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

Quick Comparison

	Elasticsearch	Supabase
Category	Vector Databases	Vector Databases
Pricing	—	freemium
Best For	—	Full-stack developers building AI apps
Website	elastic.co	supabase.com
Key Features	—	pgvector hosting Managed Postgres Real-time Auth

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 Elasticsearch

Elasticsearch has added k-NN vector search capabilities to its distributed search and analytics engine. Teams can combine vector similarity search with Elasticsearch's powerful full-text search, filtering, and aggregation features in a single platform, making it ideal for hybrid search applications at enterprise scale.

View Elasticsearch 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.