Meilisearch is an open-source, lightweight search engine built in Rust that delivers fast, relevant search results with minimal configuration. Designed for developers building applications requiring intuitive search experiences—like e-commerce, SaaS platforms, or media libraries—it combines full-text search with semantic (vector-based) search in a single API. Unlike traditional solutions that require complex Elasticsearch setups or external ML pipelines, Meilisearch offers out-of-the-box typo tolerance, faceting, geosearch, and synonym support. Its RESTful API and SDKs for popular languages make it easy to integrate into existing stacks without heavyweight infrastructure.
Meilisearch is ideal for teams that need powerful search functionality without the operational overhead of managing distributed systems. Whether you’re building a product catalog, a content-rich blog, or a multi-tenant CRM, Meilisearch’s hybrid search and real-time indexing enable relevant results even with misspelled queries or complex filters. The Community Edition is fully open source under MIT, while Enterprise features like sharding and S3 snapshots are available for production-scale deployments under a commercial license.
What You Get
- Hybrid search - Combines keyword-based full-text search with vector embeddings for semantic relevance, enabling results that understand context—not just exact matches.
- Search-as-you-type - Delivers search results in under 50ms with live updates as users type, improving UX without requiring client-side caching or complex debouncing.
- Typo tolerance - Automatically corrects misspellings and typos (e.g., ‘aple’ → ‘apple’) with configurable edit distance settings, ensuring users find what they mean even with errors.
- Filtering and faceted search - Apply filters on any field (e.g., price, category) and build dynamic sidebars with facets using simple API parameters like
filters=category=books AND price<50.
- Sorting - Sort results by any numeric or date field (e.g.,
sort=price:asc or sort=created_at:desc) to prioritize results based on business logic.
- Synonym support - Define synonym sets (e.g., ‘tv’ → ‘television’) to expand search scope without requiring users to know exact terminology.
- Geosearch - Filter and sort results by geographic coordinates using
geoDistance in filters, e.g., filters=_geoPoint(48.8566, 2.3522) < 10km for location-based queries.
- Multi-Tenancy - Isolate search data per tenant using tenant tokens, enabling SaaS applications to serve personalized results without shared indexes.
- RESTful API - Integrate via HTTP endpoints with JSON payloads; no database schema changes required. Add documents with
curl -X POST 'http://localhost:7700/indexes/movies/documents' --data-binary '@movies.json'.
- AI-ready - Native support for LangChain and the Model Context Protocol (MCP) to integrate LLMs, enabling RAG pipelines with vector embeddings from Hugging Face or OpenAI.
- Easy deployment - Run in Docker with one command:
docker run -p 7700:7700 meilisearch/meilisearch:v1.9
Common Use Cases
- Building a multi-tenant SaaS dashboard - A CRM platform uses tenant tokens to ensure each company sees only its own contacts and deals, with hybrid search returning relevant results based on both keywords and semantic similarity.
- Creating a mobile-first e-commerce platform with 10k+ SKUs - Meilisearch handles product search with faceting (brand, price range), typo tolerance for misspelled brand names, and sorting by popularity or rating—all under 100ms latency.
- Problem: Users can’t find products due to typos or vague terms → Solution: Meilisearch’s typo tolerance and synonym mapping automatically corrects ‘iphon’ to ‘iPhone’ and maps ‘smartphone’ to related products
- DevOps teams managing microservices across multiple cloud providers - Meilisearch’s single-binary deployment and Docker support allow consistent search infrastructure across AWS, GCP, and on-premises without vendor lock-in.
Under The Hood
Meilisearch is a fast, open-source search engine built in Rust that emphasizes performance, simplicity, and developer experience. It provides real-time indexing capabilities with a modular architecture designed for extensibility and ease of use.
Architecture
Meilisearch follows a modular monolithic design with distinct crates encapsulating core functionalities like indexing and filtering. This approach enables clear separation of concerns and supports scalable development.
- Modular crate structure that isolates different domains such as search, indexing, and data handling
- Layered architecture with well-defined boundaries between core logic and external integrations
- Emphasis on extensibility through configurable components and plugin-like structures
Tech Stack
Built entirely in Rust, the project leverages systems programming capabilities for high-performance search operations. It uses a monorepo structure and integrates with standard system-level features.
- Rust as the primary language, enabling memory safety and high execution speed
- Cargo-based build system with Docker support for streamlined development and deployment
- Extensive use of standard library and external crates for configuration and monitoring
- Comprehensive testing infrastructure including unit, integration, and fuzzing capabilities
Code Quality
The codebase demonstrates a mature testing strategy with an emphasis on integration and workload validation. It maintains consistent style and naming practices, although some legacy patterns introduce technical debt.
- Strong focus on integration and workload-based testing over unit test coverage
- Consistent use of Result types and contextual error handling throughout the codebase
- Clear separation of concerns and modular organization across components
- Occasional presence of conditional logic and compatibility layers that indicate accumulated technical debt
What Makes It Unique
Meilisearch distinguishes itself through its real-time search capabilities and developer-centric design. Its approach to handling index updates and supporting multi-language text processing sets it apart from conventional search engines.
- Real-time index updates with minimal latency through asynchronous processing and efficient data structures
- Built-in support for multi-language text processing and intelligent ranking algorithms that adapt to user behavior
- Extensive dump format compatibility across versions, enabling seamless migration and data portability
- API-first design with rich filtering and faceting capabilities that simplify complex search queries