Meilisearch
Meilisearch is an open-source ultra-fast search and AI retrieval platform. It provides full-text, semantic, hybrid and multimodal retrieval, supports vector storage, and can be deployed on the cloud or self-hosted. Officially, it is said to be used by more than 20,000 teams.
Updated: 2026-10-26
What is Meilisearch?
Meilisearch is an open-source search and AI retrieval platform for developers, developed by the Meilisearch team. Its official website positions it as "One platform to build, scale, and unify search and AI retrieval". Different from general search engines, it integrates full-text, semantic, hybrid, multimodal retrieval and vector storage into the same engine. It can be hosted on Meilisearch Cloud or the self-hosted open-source version, making it suitable for teams with high requirements for data controllability. The official website states that it is trusted by more than 20,000 teams.
Who is it for?
- Developers and engineering teams who need to embed in-site search into their websites or applications and desire low latency and easy integration.
- Product teams engaged in e-commerce, media, and enterprise knowledge bases who want to unify search entry points to reduce the maintenance costs of multiple search systems.
- Algorithm and platform teams building RAG or AI applications that require vector storage and hybrid search capabilities.
- DevOps and architecture teams that prefer self-hosting and value data sovereignty and compliance can fully control the deployment environment.
Key features
6Full-text Search
The official description states that it offers search-as-you-type functionality, with results returned in 50 milliseconds. Out-of-the-box relevance sorting adapts to most usage scenarios.
Semantic Search
Understands the semantics and context of queries through AI embeddings, delivering results that are more meaningful and aligned with user intent, rather than just keyword matching.
Hybrid Retrieval
Combines the efficiency of full-text retrieval with the depth of semantic retrieval, delivering a search experience that balances speed and understanding, and handles complex queries more robustly.
Multimodal Retrieval
Leveraging AI embeddings, it enables unified search across images, videos, audio, and text, catering to the retrieval needs of rich media content.
Vector Storage
Enables storage and retrieval of vectors for advanced search, similarity queries, and generative AI use cases such as RAG, eliminating the need for a separate vector database.
Filtering, Faceting, and Sorting
Provides a toolset for building complex search interfaces, integrates full-text and semantic search, and supports geospatial search as well as federated search and analytics.
How to use Meilisearch
- 1Start with the official smart presets for zero-configuration launch, then adjust parameters on demand, and deployment is usually completed in a few minutes.
- 2Evaluate the cost and compliance boundaries of cloud hosting and self-hosting; for sensitive data, prioritize self-hosting to gain control over data flow.
- 3Build complex interfaces by combining filtering, faceting, and geosearch; for long-text retrieval, it is recommended to segment the text for indexing to ensure relevance and performance.
- 4Official billing, quotas, and features may be subject to change. Please double-check the official documentation and latest instructions before using it commercially in a production environment to avoid exceeding quotas.
Traffic & popularity
Data month Sep 2026
Meilisearch receives about 303.6K visits per month and ranks #125,593 worldwide.
Monthly visits
12 monthsTraffic sources
- Search49.5%
- Direct41.0%
- Referrals6.4%
- Social1.7%
- AI assistants0.8%
- Other0.6%
Top countries
- United States14.6%
- India6.3%
- Vietnam4.7%
- Brazil3.5%
- Taiwan, China3.4%
Estimated from third-party traffic data.
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