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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
| Feature | Elasticsearch | Meilisearch | PostgreSQL FTS | Typesense |
|---|---|---|---|---|
| Type | Distributed search engine | Embedded search engine | Database built-in | Embedded search engine |
| Language | Java | Rust | C (PostgreSQL) | C++ |
| Setup Complexity | High (JVM tuning, cluster management) | Very Low (single binary, zero config) | None (already in PostgreSQL) | Very Low (single binary) |
| Typo Tolerance | Fuzzy queries (configurable) | Built-in, excellent (default) | None (pg_trgm extension for trigram) | Built-in, excellent (default) |
| Faceted Search | Excellent (aggregations) | Good (filters + facets) | Manual (COUNT + GROUP BY) | Good (filters + facets) |
| Relevance Tuning | Very High (BM25, custom scoring, boost) | Good (ranking rules, customizable) | Limited (ts_rank, ts_rank_cd) | Good (ranking rules) |
| Indexing Speed | ~10K docs/sec | ~100K docs/sec | ~5K docs/sec | ~150K docs/sec |
| Query Latency | 10-100ms | 1-10ms | 5-50ms | 1-5ms |
| Max Scale | Billions of documents | Millions of documents | Millions (depends on hardware) | Millions of documents |
| Operational Cost | High (dedicated cluster) | Low (single server) | None (same DB server) | Low (single server) |
| Best For | Enterprise, log analytics, massive scale | SaaS apps, developer-friendly search | Simple search, when you only have PostgreSQL | SaaS apps, instant search experiences |
When to Use What
| Scenario | Recommended Solution | Why |
|---|---|---|
| You just need basic keyword search in one table | PostgreSQL FTS | No new infrastructure, good enough for most simple searches |
| SaaS app, need typo-tolerant search, faceted filtering | Meilisearch or Typesense | Excellent developer experience, minimal ops, fast |
| Log analytics, SIEM, massive text corpus (1B+ docs) | Elasticsearch | Only option that scales to billions with aggregation |
| E-commerce product search with facets | Meilisearch or Typesense | Built-in facets, typo tolerance, instant search |
| You already run Elasticsearch for logging (ELK stack) | Elasticsearch | Use existing infrastructure, operational expertise already present |
| Minimum operational overhead, small team | Meilisearch | Single binary, zero config, auto-indexing |
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
| Feature | Meilisearch | Typesense |
|---|---|---|
| API Style | REST, intuitive | REST, intuitive |
| Typo Tolerance | Excellent, automatic | Excellent, automatic |
| Indexing Speed | ~100K docs/sec | ~150K docs/sec |
| Memory Usage | Higher (requires more RAM) | Lower (more memory efficient) |
| Client Libraries | 35+ official SDKs | 20+ official SDKs |
| Self-Hosted | Free (open source) | Free (open source) |
| Cloud | Meilisearch Cloud | Typesense Cloud |
| Best For | Developer happiness, rapid integration | Instant search (sub-10ms), high throughput |
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.
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