> ## Documentation Index > Fetch the complete documentation index at: https://docs.embeddingsync.com/llms.txt > Use this file to discover all available pages before exploring further. # Introduction > Power your AI applications without data pipelines or vendor lock-in. [//]: # "\"Hero \\"Hero" ## 🔗 Embedding Sync **Embedding Sync** is a managed service that keeps your application databases and vector databases in sync. It automatically synchronizes your application database (initially supporting Postgres) with your vector database (currently supporting Pinecone). Embedding Sync allows you to integrate AI-powered features into your applications without the hassle of managing data pipelines. By using your own database and Pinecone vector store, you'll always have your data and won't have to worry about vendor lock-in. There are three core components: 1. **🔗 Sync Engine**: Our managed service that securely connects to your application database and vector store. It performs real-time synchronization and supports a variety of data types. 2. **📊 Embedding Models**: We offer automated embedding generation for your data columns. We currently support the SentenceTransformers all-MiniLM-L6-v2 model or OpenAI's text-embedding-ada-002. More models are on the way. 3. **🎮 Playground**: An front-end UI that lets you quickly query your embeddings, helping you to explore and understand your data. To get started, check out our [quickstart tutorial](/quickstart). [//]: # "🎥 Demo Coming soon. This will showcase how Embedding Sync seamlessly operates in real-time to keep your databases in sync." ## 💾 Currently Supported Databases Current supported databases: * Postgres * Pinecone (as a vector database) If your preferred database isn't listed, we would love to hear from you. ## 📈 Roadmap * Support for additional databases like MySQL, MongoDB. * Logical replication support for Postgres. * Expanded range of embedding models including custom models. * Support for multi-column embeddings and metadata sync.