Elasticsearch vs Pinecone
Compare deployment options, cost efficiency, and features to choose the right vector database for your application.
Database Comparison
Elasticsearch takes the lead.
Both Elasticsearch and Pinecone are powerful vector databases designed for efficient similarity search and storage. However, their deployment options and features differ in important ways.
Why Elasticsearch:
- Elasticsearch ranks higher overall
- Elasticsearch offers more deployment options
- Elasticsearch is more cost-effective
- Elasticsearch has more permissive licensing
- Elasticsearch has 5 more strengths
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.
Pinecone
Pinecone is a fully managed, proprietary cloud vector database designed for high-performance RAG pipelines. It abstracts away infrastructure, scaling, replication, and index management. Pinecone is popular among companies building production RAG systems that need predictable latency and fully hosted operations.
Feature Comparison
Infrastructure & Technical Details
| Feature | Elasticsearch | Pinecone |
|---|---|---|
| Deployment | Self-Hosted, Managed Cloud, Serverless | Managed Cloud |
| Cost | Serverless: usage-based (ECU); Hosted: starts ~$95/month; Self-hosted: free (infra cost only) | Storage: $0.33/GB/mo; Write Units: $4/million; Read Units: $16/million; Minimum $50/mo |
| License | AGPL v3 / SSPL / Elastic License 2.0 | Proprietary |
| Index Types | HNSW, int8_hnsw, int4_hnsw, bbq_hnsw, Flat | Dense (HNSW-like), Sparse |
| Cloud Providers | AWS, Azure, GCP, Alibaba Cloud | AWS, Azure, GCP |
| Regional Flexibility | high | low |
| Strengths | 12 | 7 |
| Weaknesses | 8 | 7 |