Elasticsearch vs OpenSearch vs Meilisearch: Choosing a Search Engine

Search engines are not interchangeable. Elasticsearch and OpenSearch are distributed, feature-rich platforms for serious search infrastructure. Meilisearch is a focused, developer-friendly engine that gets you 90% of the value with 10% of the operations. This guide compares them on the dimensions that decide projects: relevance, features, operations, and cost.

The Landscape

Engine	License	Model	Best For
Elasticsearch	Elastic License	Distributed	Large-scale, complex search
OpenSearch	Apache 2.0	Distributed	Open-source Elasticsearch alternative
Meilisearch	MIT	Single-node	Fast time-to-value, small-medium indexes

Feature Comparison

Feature	Elasticsearch	OpenSearch	Meilisearch
Full-text search	Strong	Strong	Strong
Typo tolerance	Configurable	Configurable	Built-in, excellent
Vector search	Native (kNN)	Native (kNN)	Hybrid (2024+)
Facets/filters	Strong	Strong	Strong
Aggregations	Strong	Strong	Basic
Distributed scaling	Yes	Yes	Limited
Query DSL	JSON	JSON	Simple API
Operations burden	High	High	Low

Elasticsearch: The Full Platform

Elasticsearch is the most complete search engine: full-text, vector, aggregations, machine learning, and a mature ecosystem (Kibana, Logstash, Beats). It is also the most complex to operate.

// Index mapping with both text and vector fields
PUT /products
{
  "mappings": {
    "properties": {
      "name": { "type": "text" },
      "description": { "type": "text" },
      "embedding": {
        "type": "dense_vector",
        "dims": 1536,
        "index": true,
        "similarity": "cosine"
      }
    }
  }
}

from elasticsearch import Elasticsearch

es = Elasticsearch("http://localhost:9200")

results = es.search(
    index="products",
    query={
        "multi_match": {
            "query": "wireless noise cancelling headphones",
            "fields": ["name^3", "description"],
            "fuzziness": "AUTO",
        }
    },
    aggs={"by_brand": {"terms": {"field": "brand.keyword"}}},
)

Choose Elasticsearch when you need the full platform: complex relevance tuning, aggregations, vector + keyword hybrid search, and the operational capacity to run a cluster.

OpenSearch: The Open-Source Alternative

OpenSearch is a fork of Elasticsearch 7.10, maintained by AWS under Apache 2.0. It is API-compatible with the Elasticsearch 7.x API, so most code and tooling transfers directly.

from opensearchpy import OpenSearch

client = OpenSearch(hosts=["http://localhost:9200"])

results = client.search(
    index="products",
    body={
        "query": {
            "multi_match": {
                "query": "wireless headphones",
                "fields": ["name^3", "description"],
            }
        }
    },
)

Choose OpenSearch when you want Elasticsearch-class features without the Elastic license, especially on AWS where it is a first-class managed service.

Meilisearch: Fast Time-to-Value

Meilisearch is the anti-Elasticsearch: one binary, sensible defaults, and a simple API. Typo tolerance, faceting, and ranking work out of the box with almost no configuration.

import meilisearch

client = meilisearch.Client("http://localhost:7700", "masterKey")
index = client.index("products")

# Index documents — no mapping required
index.add_documents([
    {"id": 1, "name": "Wireless Noise Cancelling Headphones", "brand": "Acme"},
    {"id": 2, "name": "Wired Earbuds", "brand": "Beta"},
])

# Search with typo tolerance built in
results = index.search("wireless noice cancelling", {
    "attributesToHighlight": ["name"],
    "filter": "brand = Acme",
})

// Ranking rules are declarative, not code
{
  "rankingRules": [
    "words",
    "typo",
    "proximity",
    "attribute",
    "sort",
    "exactness"
  ]
}

Choose Meilisearch when you need good search fast: product search, documentation search, site search. It handles millions of documents on a single node, which covers most applications.

Vector Search Comparison

All three now support vector search, but the maturity differs:

• Elasticsearch: mature kNN with HNSW, hybrid search with RRF, and a full vector ecosystem.
• OpenSearch: mature kNN with HNSW and IVF, hybrid search support.
• Meilisearch: hybrid search added recently; simpler but less tunable.

If vector search is the core of your product (semantic search, RAG retrieval), Elasticsearch or OpenSearch give you more control. If it is a feature alongside keyword search, Meilisearch's hybrid mode is enough.

Operations: The Real Cost

Aspect	Elasticsearch	OpenSearch	Meilisearch
Cluster setup	Complex	Complex	One binary
Shard tuning	Required	Required	Automatic
Monitoring	Kibana	Dashboards	Built-in UI
Memory profile	Heavy	Heavy	Light
Managed options	Elastic Cloud	AWS OpenSearch	Meilisearch Cloud

The operational burden is the hidden cost. A three-node Elasticsearch cluster needs shard planning, JVM tuning, and monitoring. Meilisearch runs as a single process with sensible defaults. For a team without dedicated search engineers, that difference is decisive.

Decision Guide

Your situation	Choose
Complex relevance, aggregations, big data	Elasticsearch
Elasticsearch features, open-source license	OpenSearch
Fast search with minimal operations	Meilisearch
Vector search as a core feature	Elasticsearch / OpenSearch
Site/product search on a single node	Meilisearch
Already on AWS, want managed search	OpenSearch

Implementation Checklist

• Estimate index size and query rate honestly
• Check whether you need aggregations and complex relevance
• Evaluate vector search requirements before choosing
• Count the operational hours your team can spend on search
• Prototype with Meilisearch first; escalate only if it cannot scale
• Plan the migration path if you outgrow your choice

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