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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.

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

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

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See how it stacks up

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