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. If you want to compare the best vector databases for your data, try Agentset.

Rank: #8License: AGPL v3 / SSPL / Elastic License 2.0Cost: medium

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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Trusted by teams building production RAG applications

5M+

Documents

1,500+

Teams

99.9%

Uptime

Deployment

Self-Hosted, Managed Cloud, Serverless

Cost

Serverless: usage-based (ECU); Hosted: starts ~$95/month; Self-hosted: free (infra cost only)

Index Types

HNSW, int8_hnsw, int4_hnsw, bbq_hnsw, Flat

Deployment

Infrastructure Options

Deployment Types

Self-Hosted
Managed Cloud
Serverless

Cloud Providers

AWS
Azure
GCP
Alibaba Cloud

Strengths

What Elasticsearch Does Well

Mature and powerful hybrid search (BM25 + vector) out of the box
Excellent for organizations already using Elasticsearch
Unified platform for logs, metrics, traditional search, and vectors
Advanced quantization options (int8, int4, bbq) reducing memory 8-32x
Supports up to 4096 dimensions per vector
Multiple similarity metrics (L2, cosine, dot product)
Sophisticated filtering, aggregations, and security features
Strong RAG API support (RRF, Retriever framework)
GPU acceleration support with NVIDIA cuVS
Excellent documentation and large community
60+ regions across major cloud providers
Enterprise-grade security (RBAC, field/document-level security)

Weaknesses

Potential Drawbacks

Steep learning curve and operational complexity
Higher resource consumption (memory, CPU) than specialized vector DBs
Not optimized for vector-only workloads
Can be expensive to run at scale
Historically slower vector indexing (improved in recent versions)
Overkill for greenfield projects needing only vector search
Requires expertise to tune and optimize properly
License complexity with three options

Use Cases

When to Choose Elasticsearch

Ideal For

Organizations already using Elasticsearch
RAG applications requiring hybrid search (semantic + lexical)
Mixed workloads (logs, metrics, search, vectors) in one platform
Enterprise applications needing advanced security and compliance
Applications requiring complex filtering and aggregations
Teams wanting mature, battle-tested infrastructure
Use cases needing full-text + vector + geospatial search combined

Not Ideal For

Greenfield projects needing only vector search
Small teams without Elasticsearch expertise
Applications requiring simplest possible setup
Cost-sensitive projects at large scale
Pure vector workloads without text search needs

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.

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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);
}