Elasticsearch vs Neon: Vector Databases Comparison
Compare Elasticsearch and Neon side by side. Both are tools in the Vector Databases category.
Quick Comparison
| Category | Vector Databases | Vector Databases |
| Pricing | — | freemium |
| Best For | — | Developers wanting serverless Postgres with vector search |
| Website | elastic.co | neon.tech |
| Key Features | — |
|
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.
About Neon
Serverless Postgres with pgvector support. Scales to zero, branching for dev workflows.
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 →