ClickHouse vs Elasticsearch: Vector Databases Comparison
Compare ClickHouse and Elasticsearch side by side. Both are tools in the Vector Databases category.
The short answer
Choose Elasticsearch if most widely deployed open-source vector database with massive community.
Want to compare ClickHouse and Elasticsearch on your own traffic?
Respan lets you trace LLM and agent calls across any model or framework, A/B test prompts on production traffic, and route requests across 250+ models through one gateway. Free tier covers 10K traces per month. Setup in 5 minutes, no credit card.
Quick Comparison
| 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 |
| — |
Pros & Cons: ClickHouse vs Elasticsearch
- +Most widely deployed open-source vector database with massive community
- +Mature ELK stack ecosystem with extensive integrations and tools
- +DiskBBQ algorithm enables efficient vector search without full in-memory indexes
- +Powerful hybrid search combining keyword, filters, and vector similarity
- +Consumption-based pricing—pay only for what you use
- −Can be complex to configure and optimize for vector search
- −Not purpose-built for vector search—added as a feature
- −Resource-intensive at scale without proper optimization
- −Pricing model complexity with multiple tiers and factors
How to Choose a Vector Databases Tool
Key criteria to evaluate when comparing Vector Databases solutions:
About ClickHouse
High-performance analytical database with vector search. Standard for observability + embeddings.
About Elasticsearch
Elasticsearch is the world's most widely deployed, open-source vector database, operated by Elastic N.V. (NYSE: ESTC). Vector search is integrated into the widely used Elasticsearch search and analytics engine, leveraging the mature ELK stack ecosystem and offering powerful filtering, aggregation, and combined keyword + vector (hybrid) search capabilities. Founded in 2012 in Amsterdam, Elastic provides a platform for enterprise search, observability, and security use cases. Recent innovations include DiskBBQ, a new disk-friendly vector search algorithm that delivers more efficient vector search at scale and eliminates the need to keep entire vector indexes in memory. Elasticsearch's pricing model is consumption-based, charging only for the compute, storage, and data transfer actually used across three deployment tiers: Standard, Platinum, and Enterprise. With over 470 customers using Elastic for AI (including 410+ using it as a vector database), Elasticsearch has proven capabilities at massive scale.
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 Databasestools →Frequently Asked Questions
Other Vector Databases Tools
More Vector Databases Comparisons
Run Vector Databases in production with Respan
One platform for routing, observability, tracing, and evals across every LLM provider.