> ## Documentation Index
> Fetch the complete documentation index at: https://www.meilisearch.com/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# Meilisearch vs PostgreSQL full-text search

> Compare Meilisearch with PostgreSQL's built-in full-text search. Learn when a dedicated search engine outperforms database search.

PostgreSQL includes built-in full-text search capabilities through its `tsvector` and `tsquery` data types. While convenient for simple use cases, PostgreSQL's search falls short compared to dedicated search engines for user-facing applications.

## Quick comparison

|                        |         Meilisearch         |                     PostgreSQL FTS                    |
| ---------------------- | :-------------------------: | :---------------------------------------------------: |
| **Primary purpose**    |        Search engine        |                  Relational database                  |
| **Typo tolerance**     |           Built-in          |              Requires `pg_trgm` extension             |
| **Faceted search**     |        Native support       |                  Complex to implement                 |
| **Language support**   | CJK, Arabic, Hebrew + Latin | Limited (no native CJK; third-party extensions exist) |
| **Search-as-you-type** |   Optimized for under 50ms  |                 Not designed for this                 |
| **Relevancy tuning**   |  Configurable ranking rules |                    Basic `ts_rank`                    |
| **Frontend libraries** |   InstantSearch compatible  |                          None                         |

## What PostgreSQL FTS does well

### Single-system simplicity

Keeping search in your existing PostgreSQL database means no additional infrastructure to manage. For simple use cases, this reduces operational complexity.

### Transactional consistency

Search results are always consistent with your primary data, with no synchronization lag between database and search index.

### SQL integration

You can combine full-text search with regular SQL queries, joins, and aggregations in a single statement.

## When to choose Meilisearch instead

### You need typo tolerance

PostgreSQL's default full-text search cannot handle misspellings. The `pg_trgm` extension helps but doesn't provide true fuzzy matching with word proximity awareness. Meilisearch handles typos automatically with configurable tolerance per attribute.

### You want faceted search

Implementing faceted search in PostgreSQL is complex and resource-intensive, especially with multiple facet types and counts. Meilisearch provides optimized, first-class APIs for facet filtering and counting.

### You need instant search-as-you-type

PostgreSQL isn't optimized for the sub-50ms response times needed for search-as-you-type experiences. Full-text search queries on large datasets become costly, especially when ranking results.

### Your users speak non-Latin languages

PostgreSQL lacks dictionaries for Chinese, Japanese, Korean, and other languages requiring complex tokenization. Meilisearch provides optimized support for these languages with automatic detection.

### You want frontend integration

Search engines like Meilisearch work with InstantSearch libraries, providing pre-built UI components for search bars, facet filters, pagination, and more. PostgreSQL has no equivalent ecosystem.

### You need a public-facing search API

Meilisearch provides a secure REST API designed for public consumption with API key management and tenant tokens for multi-tenancy. Exposing PostgreSQL directly to clients creates security risks and requires building a custom API layer.

### Scaling is a concern

Full-text search queries on large PostgreSQL datasets compete for resources with your primary application workload. A dedicated search engine scales independently and uses data structures optimized for search operations.

### You want better relevancy

PostgreSQL's `ts_rank` only supports attribute weighting. Meilisearch offers configurable ranking rules for typo count, word proximity, exact matches, and custom business logic.

## When PostgreSQL FTS might be enough

Consider PostgreSQL full-text search if:

* You have a small dataset (thousands of documents)
* Search isn't user-facing or real-time search isn't required
* Basic keyword matching is sufficient
* You can't add additional infrastructure
* You're using a managed PostgreSQL service that restricts extensions

## Migration resources

Ready to upgrade from PostgreSQL full-text search:

* [Migrating from PostgreSQL](/resources/migration/postgresql_migration) - Step-by-step data export and import guide with query and settings comparison
* [Quick start guide](/getting_started/first_project) - Set up Meilisearch in minutes
* [Indexing documents](/resources/internals/documents) - Import your data

<Note>
  PostgreSQL is a registered trademark of the PostgreSQL Global Development Group. This comparison is based on publicly available information and our own analysis.
</Note>