Meilisearch is an open-source search engine API built in Rust that adds fast, relevant search to your websites and applications. It delivers results in under 50 milliseconds with built-in typo tolerance, filtering, faceting, and AI-powered hybrid search that combines full-text and semantic matching. Available as a free self-hosted solution or a managed cloud service, it supports SDKs for 10+ languages and integrates with frameworks like React, Vue, Laravel, and Rails. Used by companies like Hugging Face and Louis Vuitton.

• Instant Search-as-You-Type: Returns results in under 50 milliseconds with real-time updates as users type.
• AI-Powered Hybrid Search: Combines keyword search with semantic understanding via vector embeddings for more relevant results.
• Built-in Typo Tolerance: Handles misspellings automatically so that users find the correct results without extra setup.
• Filtering and Faceted Search: Allows users to refine results with filters, sorting, and categories, including location-based search.
• Custom Ranking Rules: Controls how results are ordered using built-in and customizable relevance rules.
• Synonyms and Stop Words: Improves search accuracy by defining equivalent terms and ignoring common filler words.
• Built-in Vector Search: Stores and queries embeddings for similarity searches, recommendations, and RAG applications.
• Multi-modal Search Support: Enables searching across text and images using embeddings for broader discovery.
• Multi-search and Federation: Queries multiple indexes or data sources in a single request for complex search experiences.
• Multi-tenancy and Access Control: Secures data with API keys and tenant tokens for multi-user applications.
• Advanced Conversational Search: Builds chat-style search experiences using RAG and LLM integrations.

• Simple Setup: Works out of the box with sensible defaults. No complex configuration needed to get relevant results.
• Developer-Friendly API: Clean RESTful API with official SDKs for JavaScript, Python, PHP, Ruby, Go, Rust, Java, .NET, Swift, and Dart.
• Free Self-Hosting Option: Fully open-source under the MIT license with no licensing costs for self-managed deployments.
• Strong Community & Support: Active open-source community with a public feedback repository and responsive team that listens to user input.

• Limited Horizontal Scaling: Primarily scales vertically on a single server. Horizontal sharding is an enterprise feature, which may limit very large deployments.
• Fewer Advanced Enterprise Features: Lacks some Elasticsearch capabilities like function scoring with decay functions and index aliasing for zero-downtime reindexing.
• Cloud Pricing Can Add Up: Usage-based pricing can increase costs at high query volumes.
• Performance Constraints at Very Large Scales: Handling very large datasets or high-traffic workloads may require additional optimization.

• E-Commerce Product Search: Add fast, typo-tolerant product search with faceted filtering by price, brand, and category. One Meilisearch customer reported a 43% improvement in purchase conversion after switching their search engine.
• Documentation & Knowledge Base: Power instant search across docs, wikis, and help centers. The French National Center for Scientific Research (CNRS) uses Meilisearch to help researchers find information quickly across their knowledge base.
• SaaS Application Search: Build app-wide search across contacts, projects, and records in multi-tenant SaaS products. Works well for CRM tools, task managers, and note-taking apps where users need to find specific entries fast.
• AI Model & Dataset Discovery: Power search across large AI catalogs, making models, datasets, and resources easy to explore.
• Media & Content Platforms: Enable readers and viewers to search articles, videos, and media archives. Le Monde, the French newspaper, uses Meilisearch to power their content search.