MongoDB Atlas Vector Search vs Elasticsearch
Compare deployment options, cost efficiency, and features to choose the right vector database for your application. If you want to compare these models on your data, try Agentset.
Database Comparison
MongoDB Atlas Vector Search takes the lead.
Both MongoDB Atlas Vector Search and Elasticsearch are powerful vector databases designed for efficient similarity search and storage. However, their deployment options and features differ in important ways.
Why MongoDB Atlas Vector Search:
- MongoDB Atlas Vector Search ranks higher overall
- Elasticsearch has more permissive licensing
- MongoDB Atlas Vector Search has 1 more strengths
Vector Databases Are Just One Piece of RAG
Agentset gives you a managed RAG pipeline with the top-ranked models and best practices baked in. No infrastructure to maintain, no vector database to operate.
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MongoDB Atlas Vector Search
MongoDB Atlas Vector Search is a native vector database capability built directly into MongoDB Atlas, eliminating data synchronization between operational and vector databases. It enables hybrid queries combining vector similarity with MongoDB's powerful document model, metadata filtering, aggregation pipelines, and geospatial search—all within a single unified platform.
Elasticsearch
Elasticsearch is one of Europe's most widely deployed open-source search engines that includes native vector database capabilities. It combines dense vector search with traditional full-text BM25 keyword search for powerful hybrid retrieval, making it ideal for RAG applications that need both semantic and lexical search capabilities.
Build RAG in Minutes, Not Months
Agentset gives you a complete RAG API with fully managed vector storage and retrieval. Upload your data, call the API, and get accurate results from day one.
import { Agentset } from "agentset";
const agentset = new Agentset();
const ns = agentset.namespace("ns_1234");
const results = await ns.search(
"What is multi-head attention?"
);
for (const result of results) {
console.log(result.text);
}Feature Comparison
Infrastructure & Technical Details
| Feature | MongoDB Atlas Vector Search | Elasticsearch |
|---|---|---|
| Deployment | Managed Cloud, Self-Hosted (Enterprise), Community Edition | Self-Hosted, Managed Cloud, Serverless |
| Cost | Free tier: M0 (512MB); Flex: $8-$30/mo; Dedicated: starts $57/mo (M10); Community: Free | Serverless: usage-based (ECU); Hosted: starts ~$95/month; Self-hosted: free (infra cost only) |
| License | SSPL (self-managed) / Proprietary (Atlas Cloud) | AGPL v3 / SSPL / Elastic License 2.0 |
| Index Types | Hierarchical Navigable Small World (HNSW-like), Quantized indexes | HNSW, int8_hnsw, int4_hnsw, bbq_hnsw, Flat |
| Cloud Providers | AWS, Azure, GCP | AWS, Azure, GCP, Alibaba Cloud |
| Regional Flexibility | high | high |
| Strengths | 13 | 12 |
| Weaknesses | 10 | 8 |