Tutorials, product updates, and insights from the Meilisearch team
Meilisearch Cloud now ships a built-in chat UI. Select an index, get an auto-generated system prompt, guardrails, and an inspector tab to debug - no separate AI pipeline required.
Meilisearch Cloud now supports sharding and replication - letting your search infrastructure scale horizontally, stay available during updates, and serve users from the nearest node. Here is what that means and who it is for.
Learn what RAG-as-a-Service is, why it matters, common use cases, key benefits, and how to evaluate providers to build more accurate AI applications faster.
The company uses Meilisearch to deliver fast, faceted inventory search across multi-location dealer marketplaces - without fighting the tool.
Learn what self-RAG is, how it works, and why self-reflective retrieval-augmented generation reduces hallucinations and improves reliability in LLM systems.
The good, the bad, and the leaky: jemalloc, bumpalo, and mimalloc in Meilisearch
Where Meilisearch is heading next: serverless indexes, an AI gateway with our own models, a richer Cloud dashboard, and a more capable chat engine — all converging into one information retrieval platform.
Learn how RAG in AI works in practice, how to improve retrieval relevance, evaluate quality, secure data, and keep results up to date in production.
How this global electronics manufacturer elevates product discovery with faster, more relevant search at scale
We patched LMDB to support nested read transactions on uncommitted writes - eliminating full database scans and making Meilisearch's vector store 3× faster
Find out what Search-as-a-Service is, how it works, key pros and cons, top providers, how to choose the right one, and more.
