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. Thepg_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’sts_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 - Step-by-step data export and import guide with query and settings comparison
- Quick start guide - Set up Meilisearch in minutes
- Indexing documents - Import your data
PostgreSQL is a registered trademark of the PostgreSQL Global Development Group. This comparison is based on publicly available information and our own analysis.