With nearly 12,000 GitHub stars and a lineage tracing back to Sphinx Search, Manticore Search is the C++ search database that delivers the full-text, vector, and hybrid search capabilities of Elasticsearch at a fraction of the resource cost — starting in under a second and consuming just 40MB RAM for an empty instance. The SQL-first interface speaks the MySQL wire protocol, meaning mysql client, MySQL Workbench, and any MySQL-compatible driver connects natively without adapters, while the HTTP JSON API provides RESTful access for modern applications. Over 20 full-text operators handle proximity search, quorum matching, field-start and field-end constraints, MAYBE operators, and regex patterns, backed by stemming, lemmatization, stopwords, synonyms, wordforms, and advanced morphology in 70+ languages. Vector search with HNSW indexing enables semantic similarity queries, and hybrid mode combines keyword relevance with vector distance in a single ranked result set using a cost-based query optimizer. Real-time indexing delivers sub-second document availability after insert, sharded tables distribute data across nodes, and Galera-based synchronous replication ensures high availability. Conversational search via CREATE CHAT MODEL and CALL CHAT integrates LLM-backed responses with KNN retrieval and conversation history directly inside the database. Client libraries ship for PHP, Python, JavaScript, TypeScript, Java, Go, Rust, and Elixir. Columnar storage via the Manticore Columnar Library handles analytical workloads on large datasets. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. GPLv3 licensed.

ParadeDB turns the Postgres you already run into a full search and analytics engine, so your application data, full-text search, vector retrieval, and aggregations all live in one database. Create one ParadeDB index over a table and you can run relevance-ranked BM25 queries with tokenizers, phrase matching, highlighting, and Top-K retrieval using plain SQL. The same index stores pgvector embeddings, so similarity search composes with text predicates and filters, and hybrid queries fuse both rankings when a question needs keywords and meaning at once. Aggregations push into the index too: ordinary GROUP BY queries accelerate, and Elasticsearch-style JSON aggregations return facets, histograms, percentiles, and cardinality for filterable search interfaces. Because the index updates in the same transaction as your writes, search results are never stale and there is no ETL pipeline, sync job, or second system to babysit. JOINs against the rest of your schema keep working, since search is just another index inside your system of record. Teams typically retire a separate search cluster while keeping sub-second relevance search, semantic retrieval for AI features, and faceted analytics on the data they already store. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. AGPL-3.0 licensed.