Elasticsearch vs Milvus: Vector Databases Comparison
Compare Elasticsearch and Milvus side by side. Both are tools in the Vector Databases category.
Updated February 28, 2026
The short answer
Choose Elasticsearch if most widely deployed open-source vector database with massive community.
Choose Milvus if extreme scalability handling billions of vectors in distributed environments.
Want to compare Elasticsearch and Milvus on your own traffic?
Respan lets you trace LLM and agent calls across any model or framework, A/B test prompts on production traffic, and route requests across 250+ models through one gateway. Free tier covers 10K traces per month. Setup in 5 minutes, no credit card.
Quick Comparison
| Category | Vector Databases | Vector Databases |
| Pricing | — | Open Source |
| Best For | — | Organizations that need vector search at billion-scale with high throughput |
| Website | elastic.co | milvus.io |
| Key Features | — |
|
| Use Cases | — |
|
Pros & Cons: Elasticsearch vs Milvus
- +Most widely deployed open-source vector database with massive community
- +Mature ELK stack ecosystem with extensive integrations and tools
- +DiskBBQ algorithm enables efficient vector search without full in-memory indexes
- +Powerful hybrid search combining keyword, filters, and vector similarity
- +Consumption-based pricing—pay only for what you use
- −Can be complex to configure and optimize for vector search
- −Not purpose-built for vector search—added as a feature
- −Resource-intensive at scale without proper optimization
- −Pricing model complexity with multiple tiers and factors
- +Extreme scalability handling billions of vectors in distributed environments
- +Fully open-source under Apache 2.0 with no vendor lock-in
- +Hybrid search combining dense and sparse vectors in single queries
- +Rich index flexibility with IVF, HNSW, DiskANN and GPU acceleration
- +Managed cloud option reduces DevOps burden with auto-scaling
- −Complex distributed architecture requiring Kubernetes for production
- −Steep learning curve for index tuning and deployment configuration
- −High memory and storage requirements for large datasets
- −No in-place vector updates requiring delete and re-insert workarounds
Pricing: Elasticsearch vs Milvus
- · Apache 2.0 license
- · Self-hosted on Kubernetes
- · Full feature access
- · Community support
- · 5GB storage
- · 2.5M vCUs/month
- · Up to 5 collections
- · Up to 32 query CUs
- · Manual scaling
- · Email/ticket support
- · 99.95% uptime SLA
- · SSO and RBAC
- · 24/7 enterprise support
- · VPC peering
When to Choose Elasticsearch vs Milvus
- Billion-vector similarity search
- Large-scale recommendation systems
- Image and video retrieval
How to Choose a Vector Databases Tool
Key criteria to evaluate when comparing Vector Databases solutions:
About Elasticsearch
Elasticsearch is the world's most widely deployed, open-source vector database, operated by Elastic N.V. (NYSE: ESTC). Vector search is integrated into the widely used Elasticsearch search and analytics engine, leveraging the mature ELK stack ecosystem and offering powerful filtering, aggregation, and combined keyword + vector (hybrid) search capabilities. Founded in 2012 in Amsterdam, Elastic provides a platform for enterprise search, observability, and security use cases. Recent innovations include DiskBBQ, a new disk-friendly vector search algorithm that delivers more efficient vector search at scale and eliminates the need to keep entire vector indexes in memory. Elasticsearch's pricing model is consumption-based, charging only for the compute, storage, and data transfer actually used across three deployment tiers: Standard, Platinum, and Enterprise. With over 470 customers using Elastic for AI (including 410+ using it as a vector database), Elasticsearch has proven capabilities at massive scale.
About Milvus
Milvus is an open-source vector database built for scalable similarity search, capable of handling billions of vectors in distributed environments. Created by Zilliz, a company founded in 2017 by Charles Xie (former founding engineer of Oracle 12c cloud database), Milvus has become one of the most widely deployed vector databases with over 30,000 GitHub stars.
The database supports multiple index types including IVF, HNSW, and DiskANN, with GPU-accelerated search and hybrid search combining dense and sparse vectors in a single query. Milvus runs on Kubernetes for production deployments and is governed under the LF AI & Data Foundation. Zilliz Cloud offers a fully managed version with automatic scaling, starting with a free tier and usage-based pricing from $4 per million vector compute units.
Zilliz has raised approximately $113-132 million in funding, with a $60 million Series B extension in August 2022 led by Prosperity7 Ventures (Aramco). The company is headquartered in San Francisco with roughly 140 employees. Zilliz was named "Highest Performer" and "Easiest to Use" in G2's Summer 2025 Vector Database Grid Report.
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 Databasestools →Frequently Asked Questions
Other Vector Databases Tools
More Vector Databases Comparisons
Run Vector Databases in production with Respan
One platform for routing, observability, tracing, and evals across every LLM provider.