Question: Can you suggest a solution that allows me to build personalized recommendation systems using machine-learned models?

If you want to use machine-learned models to build your own recommendation systems, Vespa is also worth a look. It's an online service for applying AI to large data sets, with a unified search engine and vector database that can handle vector search, lexical search and search in structured data. Vespa lets developers build production search applications at any scale, combining fast vector search and filtering with machine-learned models, so it's good for search, recommendation and personalization.

Another contender is Recombee, an AI-powered real-time recommender service. It offers a simple RESTful API and SDKs for easy integration into e-commerce, music, travel and other businesses. Recombee uses AI personalization to boost user engagement and sales through real-time recommendations, search and analytics. It offers deep-learning, collaborative filtering and content-based filtering to ensure the most relevant and personalized results, and it's designed to be highly scalable and secure.

For more sophisticated search and recommendation needs, Trieve offers a full-stack infrastructure that combines language models with fine-tuning ranking and relevance tools. It offers private managed embedding models, semantic vector search and hybrid search, so it's good for complex search use cases. Trieve offers merchandising relevance tuning and private data hosting, so customer data stays in their control.

Last, there's Qdrant, an open-source vector database and search engine designed for fast and scalable vector similarity searches. Qdrant is designed for cloud-native architecture and is optimized for high performance, with ease of use and simple deployment. It integrates with popular embeddings and frameworks, so it's good for advanced search and recommendation systems, with flexible pricing and strong security.