Question: Can you recommend a platform that supports private managed embedding models and hybrid search for customized search experiences?

If you're looking for a platform that supports private managed embedding models and hybrid search for customized search experiences, Trieve is a great option. It offers a full-stack infrastructure for building search, recommendations, and Retrieval-Augmented Generation (RAG) experiences. Trieve supports private managed embedding models and offers features like SPLADE full-text neural search, semantic vector search, and hybrid search. It also lets customers bring their own embedding model or use open-source defaults, so you can have control and flexibility over the data.

Another top contender is Pinecone, a vector database designed for fast querying and retrieval of similar matches. Pinecone supports low-latency vector search, metadata filtering, real-time updates, and hybrid search that combines vector search with keyword boosting. It has a scalable serverless design and integrates with a variety of data sources and models, so it's a good fit for enterprises with heavy search loads. Pinecone also offers a range of pricing options, including a free starter plan, and supports major cloud providers.

If you're looking for a more complete AI-powered search solution, take a look at Algolia. Algolia offers tools for fast, scalable, and easy-to-use search experiences. It combines keyword search with vector understanding and offers dynamic re-ranking based on user behavior and trends. Algolia is good for a range of industries, including enterprise search, headless commerce, and mobile apps, and offers flexible pricing with a Pay-As-You-Go option and committed plans. Developers can use Algolia with detailed documentation and API clients in multiple languages and frameworks.

Last, Qdrant is an open-source vector database and search engine that's designed for fast and scalable vector similarity searches. It offers cloud-native scalability and high-availability, along with ease of use and deployment options, including a free tier with a 1GB cluster. Qdrant integrates with leading embeddings and frameworks, so it's a good choice for advanced search and recommendation systems. It offers flexible deployment options and strong security features, so you can use embeddings efficiently in full-fledged applications.