Keywords AI

ClickHouse vs Elasticsearch

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

Quick Comparison

	ClickHouse	Elasticsearch
Category	Vector Databases	Vector Databases
Pricing	freemium	—
Best For	Teams needing analytics and vector search in one store	—
Website	clickhouse.com	elastic.co
Key Features	Analytics + vector High scale Observability	—

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 ClickHouse

High-performance analytical database with vector search. Standard for observability + embeddings.

View ClickHouse profile →Visit website

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

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.