Full-Text Search Implementation: Elasticsearch vs Meilisearch vs PostgreSQL FTS (2026)

Adding search to your application is one of those decisions where the wrong choice costs months of rework. Elasticsearch dominates the enterprise, Meilisearch has emerged as the developer-friendly alternative, and PostgreSQL's built-in full-text search covers surprisingly many use cases. This comparison helps you pick the right search solution for your scale and requirements.

Quick Comparison

When to Use What

PostgreSQL Full-Text Search: Getting Started

-- Enable FTS with a generated column + index
ALTER TABLE articles ADD COLUMN search_vector tsvector
  GENERATED ALWAYS AS (
    setweight(to_tsvector('english', coalesce(title, '')), 'A') ||
    setweight(to_tsvector('english', coalesce(body, '')), 'B')
  ) STORED;

CREATE INDEX articles_search_idx ON articles USING GIN (search_vector);

-- Search with ranking
SELECT title, ts_rank(search_vector, query) AS rank
FROM articles, to_tsquery('english', 'postgresql & performance') query
WHERE search_vector @@ query
ORDER BY rank DESC
LIMIT 20;

-- Limitations to know:
-- No typo tolerance (use pg_trgm for fuzzy matching)
-- No faceted search (implement with COUNT + GROUP BY)
-- Relevance tuning is basic compared to dedicated search engines

Meilisearch vs Typesense: Head-to-Head

Bottom line: Start with PostgreSQL FTS if you only need basic keyword search — it is free, already running, and handles 80% of use cases. Move to Meilisearch or Typesense when you need typo tolerance, faceted search, or instant-search UX. Only reach for Elasticsearch when you have a dedicated ops team and need to scale to billions of documents or complex aggregations. See also: PostgreSQL Query Optimization and PostgreSQL vs MySQL vs SQLite.