Elasticsearch vs Meilisearch — which should I choose?

Meilisearch is simpler to set up and excellent for straightforward search use cases under 10 million documents. Elasticsearch handles complex queries, aggregations, and massive datasets better. We recommend Meilisearch for most projects and Elasticsearch for enterprise-scale.

How do you handle search relevance tuning?

We implement configurable ranking rules, field boosting, query-time signals (like popularity and recency), and A/B testing to continuously improve result quality based on real user behavior.

Can search work across multiple data sources?

Yes. We build federated search that queries multiple backends simultaneously and merges results into a unified, ranked response. Each source can have independent indexing and access controls.

Search That Finds What Users Actually Want

Full-text search, vector similarity, faceted filters, and autocomplete. Our AI-managed teams build search systems that handle millions of documents with sub-100ms results.

Solution: Search Engine

Great search is the difference between users finding what they need and leaving in frustration. Production search systems combine full-text indexing, semantic understanding, faceted navigation, typo tolerance, and relevance tuning. Whether searching products, documents, or knowledge bases, the search experience directly impacts user satisfaction and conversion.

Stack Components

Elasticsearch / Meilisearch (Search Engine): Distributed search engine providing full-text search, fuzzy matching, highlighting, and faceted navigation with millisecond response times.
Vector Database (Pinecone) (Semantic Search): Stores embeddings for semantic similarity search, enabling "search by meaning" in addition to keyword matching.
Node.js / Python (Search API): Backend service handling query parsing, spell correction, synonym expansion, and result ranking with personalization.
React (Search UI): Responsive search interface with instant results, autocomplete suggestions, filter panels, and result highlighting.
Redis (Query Cache): Caches popular queries and autocomplete suggestions for sub-10ms response times on frequently searched terms.

Best For

E-commerce product search
Document and knowledge base search
Job board and listing search
Marketplace multi-attribute search
Internal enterprise search
Media library and asset search

Case Studies

E-Commerce Product Search: Advanced product search for an online marketplace with 500K+ listings, faceted filters, typo tolerance, and personalized ranking.
- Search results returned in under 50ms for all queries
- Typo tolerance and synonym matching improving result relevance by 30%
- Faceted filters for category, price, brand, rating, and custom attributes
- Search-driven conversion rate increased by 22%
Enterprise Knowledge Search: Unified search across Confluence, Google Drive, Slack, and internal databases for a 1,000-person company.
- Single search bar querying 5 data sources simultaneously
- Semantic search understanding natural language questions
- Access-controlled results respecting source permissions

Frequently Asked Questions

Elasticsearch vs Meilisearch — which should I choose?: Meilisearch is simpler to set up and excellent for straightforward search use cases under 10 million documents. Elasticsearch handles complex queries, aggregations, and massive datasets better. We recommend Meilisearch for most projects and Elasticsearch for enterprise-scale.
How do you handle search relevance tuning?: We implement configurable ranking rules, field boosting, query-time signals (like popularity and recency), and A/B testing to continuously improve result quality based on real user behavior.
Can search work across multiple data sources?: Yes. We build federated search that queries multiple backends simultaneously and merges results into a unified, ranked response. Each source can have independent indexing and access controls.