Meilisearch vs Elasticsearch: Compared

Quick Verdict

Meilisearch is the better choice for most self-hosters who need search for their application. It’s dramatically simpler to set up, uses far less memory, and delivers instant typo-tolerant results out of the box. Elasticsearch is the better choice for complex analytics, log aggregation, or enterprise-scale search where you need advanced query DSL, aggregations, and horizontal scaling.

Overview

Both are search engines you can self-host, but they target different use cases and skill levels.

Meilisearch — Dual-licensed MIT (Community) and BSL 1.1 (Enterprise features). 49k+ GitHub stars. Written in Rust. Designed as a fast, typo-tolerant search engine for end-user-facing applications. Single binary, zero configuration needed to start.

Elasticsearch — Elastic License 2.0 (SSPL for some components). The most widely deployed search engine in the world. Written in Java. Powers search, logging, and analytics at every scale from startups to Fortune 500. Part of the Elastic Stack (ELK).

Feature Comparison

Feature	Meilisearch	Elasticsearch
Primary use case	Application search	Search, analytics, logging
Typo tolerance	Built-in, automatic	Requires fuzzy query config
Setup time	Minutes	Hours
Query language	Simple filters + search	Full query DSL (JSON)
Relevance tuning	Ranking rules (simple)	Custom scoring (complex)
Faceted search	Yes (built-in)	Yes (aggregations)
Geo search	Yes	Yes
Full-text search	Yes	Yes
Aggregations	Basic (facets, stats)	Advanced (pipeline aggs)
Multi-tenancy	Via tenant tokens	Via index aliases, RBAC
Horizontal scaling	No (single-node CE)	Yes (sharding, replicas)
Real-time indexing	Near real-time	Near real-time
Document size limit	~100 KB default	No practical limit
API style	RESTful, intuitive	RESTful, verbose JSON
SDK availability	Official: JS, Python, PHP, Ruby, Go, Rust, Java, Swift, Dart	Official: Java, Python, JS, Go, Ruby, PHP, Rust, .NET
Auth	API key based	Built-in RBAC + API keys
RAM usage (idle)	~50-100 MB	~1-2 GB (JVM heap)
Storage engine	LMDB (disk-based)	Lucene (disk-based)
Docker image	getmeili/meilisearch	docker.elastic.co/elasticsearch/elasticsearch
Default port	7700	9200
License	MIT / BSL 1.1	Elastic License 2.0

Installation Complexity

Meilisearch is one of the simplest search engines to deploy:

services:
  meilisearch:
    image: getmeili/meilisearch:v1.35.1
    container_name: meilisearch
    ports:
      - "7700:7700"
    volumes:
      - meili_data:/meili_data
    environment:
      - MEILI_MASTER_KEY=your-master-key-change-this-min-16-chars
      - MEILI_ENV=production
    restart: unless-stopped

volumes:
  meili_data:

Start it, index some documents via the REST API, and search. That’s it. No schema definition required — Meilisearch infers it from your documents.

Elasticsearch requires more configuration:

services:
  elasticsearch:
    image: docker.elastic.co/elasticsearch/elasticsearch:8.19.11
    container_name: elasticsearch
    ports:
      - "9200:9200"
    volumes:
      - es_data:/usr/share/elasticsearch/data
    environment:
      - discovery.type=single-node
      - xpack.security.enabled=false
      - ES_JAVA_OPTS=-Xms512m -Xmx512m
    ulimits:
      memlock:
        soft: -1
        hard: -1
    restart: unless-stopped

volumes:
  es_data:

Elasticsearch needs JVM tuning (ES_JAVA_OPTS), vm.max_map_count=262144 set on the host (or it crashes), and understanding of index mappings for optimal performance. Security is on by default in 8.x, which adds TLS certificate management to the setup.

Meilisearch wins on simplicity by a wide margin.

Performance and Resource Usage

Meilisearch is built in Rust and designed for speed with minimal resources:

• Idle RAM: ~50-100 MB
• Search latency: Sub-50ms for most queries
• Indexing: Fast for datasets under 10M documents
• Storage: LMDB — data lives on disk, not in RAM. Index size is roughly 1-2x document size.

Elasticsearch is a Java application running on the JVM:

• Idle RAM: 1-2 GB minimum (JVM heap)
• Search latency: Sub-100ms typical, highly tunable
• Indexing: Designed for massive throughput at scale
• Storage: Lucene segments on disk, with file system cache leveraging available RAM

For a typical self-hosted application (product catalog, wiki, blog search), Meilisearch uses 10-20x less RAM than Elasticsearch. For log aggregation or analytics with billions of documents, Elasticsearch scales horizontally where Meilisearch cannot.

Community and Support

Meilisearch: 49k+ stars. Active Discord community. Commercial support via Meilisearch Cloud. Well-documented REST API. Growing ecosystem of integrations (Strapi, Laravel Scout, WordPress, etc.).

Elasticsearch: The industry standard. Massive community, extensive documentation, thousands of blog posts and tutorials. Commercial support from Elastic. Integration with every major framework and platform. Elastic Stack (Kibana, Logstash, Beats) provides a complete observability platform.

Elasticsearch has the larger ecosystem by orders of magnitude. Meilisearch has a friendlier, more accessible community for developers building application search.

Use Cases

Choose Meilisearch If…

• You need search for a web application (e-commerce, docs, blog)
• You want instant, typo-tolerant search results
• You’re running on limited hardware (VPS, Raspberry Pi)
• You want zero-config relevance that works well out of the box
• You don’t need aggregations or complex analytics
• You want the simplest possible setup
• Your dataset is under 10 million documents

Choose Elasticsearch If…

• You need log aggregation and analysis (ELK stack)
• You need complex aggregations and analytics queries
• You need horizontal scaling across multiple nodes
• You’re building an observability platform (with Kibana)
• You need advanced query DSL with custom scoring
• Your dataset exceeds 100 million documents
• You need RBAC and fine-grained access control

Final Verdict

Meilisearch is the right choice for application search. If you’re adding search to a website, app, or internal tool, Meilisearch gets you from zero to excellent search in minutes. The typo tolerance, faceting, and instant results work beautifully out of the box. The resource footprint is tiny compared to Elasticsearch.

Elasticsearch is the right choice for data infrastructure. If you need log aggregation, complex analytics, or search at massive scale, Elasticsearch is battle-tested at every scale imaginable. The trade-off is significantly more complexity and resource requirements.

For most self-hosters adding search to an application: Meilisearch. For building an observability stack: Elasticsearch (or consider OpenSearch as a fully open-source alternative).

Related

• How to Self-Host Meilisearch
• How to Self-Host Elasticsearch
• Meilisearch vs Typesense
• Elasticsearch vs OpenSearch
• Best Self-Hosted Search Engines
• Self-Hosted Algolia Alternatives
• Docker Compose Basics