Vector database for AI applications # Qdrant vector database template The Qdrant template defines a container with a 5 GB persistent volume and API-key authentication. Provisioning is currently unavailable in the international sandbox. [Get started](https://panel.deplomo.com/signup)[Back to marketplace](/marketplace) ## Qdrant template details The template keeps Qdrant’s port private and configures an API key. Resource use and query performance depend on workload and configuration. ### Qdrant vector database Qdrant is written in Rust and provides vector search features. Resource requirements and query performance depend on data, workload, and configuration. ### Qdrant marketplace template The template defines a Qdrant container with a 5 GB persistent volume and API-key authentication. Provisioning is currently unavailable in the international sandbox. ### Vector similarity and filtering Qdrant supports vector search methods and metadata filtering. Search behavior depends on vector configuration, distance metric, data, and query design. ### Private network access and authentication The template keeps the Qdrant port private and configures an API key. Access from other services depends on network configuration; provisioning is currently unavailable in the international sandbox. ## Frequently asked questions about Qdrant Answers to common questions about vector search, Qdrant configuration, and application integration: What is Qdrant used for? What role can vector databases play in AI applications? When should I use a dedicated vector database? Can Qdrant be used with Persian-language embeddings? How can an application connect to Qdrant? ## Qdrant vector search: features and deployment considerations Qdrant is an open-source vector database used for similarity search. The Deplomo template defines a container with a persistent volume and API-key authentication; provisioning is currently unavailable in the international sandbox. Resource requirements depend on vector dimensions, index settings, and workload. ### How does Qdrant search vectors? Qdrant indexes vectors and supports similarity search, including approximate nearest-neighbor methods such as HNSW. Results depend on embeddings, index settings, and query design. For relational data alongside vectors, you could use a database such as [Postgresql host](/marketplace/apps/postgresql) . Open WebUI can also be configured to work with a vector database such as Qdrant for document-retrieval workflows. See the [open webui](/marketplace/apps/openwebui) template for a separate local-model interface; these services are not preconfigured to work together. ### Qdrant template compared with self-managed vector databases Running a vector database requires planning storage, memory, indexes, access controls, and backup procedures. Resource needs depend on vector dimensions and workload; test representative data before choosing a configuration. The Deplomo marketplace template defines a Qdrant container, a 5 GB persistent volume, and API-key authentication. Provisioning is currently unavailable in the international sandbox; performance and reliability depend on the deployment and workload. ### Qdrant deployment options Compare database features, configuration, resource requirements, and operating needs.

Feature	Qdrant marketplace template	Relational database with vector features	Manual setup on a VPS
Vector search and indexing	Qdrant supports vector indexes and similarity search; performance depends on data and configuration	Performance depends on database, indexes, data, and workload	Requires configuration and testing for the selected workload
Setup and deployment	Template defines a container and persistent storage; provisioning currently unavailable	Extension availability depends on database and version	Self-managed setup requires configuration for storage, networking, and access
Metadata filtering	Qdrant supports payload filters; behavior and performance depend on query and index configuration	Features depend on database and configuration	Memory requirements vary with index and workload
Package pricing and resource limits	Fixed monthly EUR package prices by zone, with hard limits	Provider prices and limits depend on the selected service	Resource availability and package terms vary by provider

Choose a vector database based on your data, retrieval requirements, resource needs, and operating model. Test retrieval quality with your own embeddings. ### Qdrant template requirements and integration Review the Qdrant template and its API requirements before deployment. Provisioning is currently unavailable in the international sandbox. - **Marketplace template** The Qdrant template is listed in the marketplace; service provisioning is currently unavailable in the international sandbox. - **Review resource and storage needs** Resource and storage limits are determined by the selected monthly EUR zone package. Plan for vector dimensions, indexes, and expected workload. - **API access** The template configures an API key, but provisioning is currently unavailable in the international sandbox. No service address or deployment time is promised. - **Connect with a client library** When a Qdrant service is available, configure a compatible client with the service address and API key. Check the client documentation for collection and vector setup. ### Qdrant marketplace template summary The marketplace lists a Qdrant container with persistent storage and API-key authentication. Provisioning is currently unavailable in the international sandbox; retrieval quality, performance, and capacity depend on configuration and workload. Review the Qdrant template and its resource limits. ## Review the Qdrant marketplace template The template defines persistent storage and API-key authentication. Deplomo packages have fixed monthly EUR prices by zone and hard resource limits. [Qdrant template overview](https://panel.deplomo.com/signup)