Qdrant is an open-source and fully managed high-performance Vector Database. The vector search engine provides a production-ready service with a convenient API to store, search, and manage vector embeddings.
Qdrant is an open-source and fully managed high-performance Vector Database. The vector search engine provides a production-ready service with a convenient API to store, search, and manage vectors with an additional payload Qdrant is tailored to extended filtering support on additional metadata fields, which can be stored as payload along with vector embeddings. With Qdrant, embeddings, and neural network encoders can be turned into full-fledged applications for matching, searching, recommending, and much more solutions to make the most of unstructured data. It is easy to use, deploy and scale, blazing fast and accurate simultaneously.
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Pricing is based on the duration and terms of your contract with the vendor. This entitles you to a specified quantity of use for the contract duration. If you choose not to renew or replace your contract before it ends, access to these entitlements will expire.
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This listing offers one pricing dimension: Private Cloud, billed by Units under an annual contract. You deploy Qdrant fully on your own on-premise infrastructure, giving you control over data and residency. Pricing scales with the number of Units you commit to for the year. There are no separate tiers or instance sizes to choose from here. This deployment suits sensitive workloads, air-gapped setups, and environments needing full isolation. To size your commitment or understand how Units map to your workload, contact the vendor.
Top-of-mind questions for buyers
What does one Unit represent for the Private Cloud deployment?
The pricing table bills by Units for an on-premise deployment, but it does not define what one Unit maps to in compute, memory, or storage terms. Unit sizing depends on your workload and cluster needs. Contact the vendor to map Units to your data volume and required capacity.
How does the Private Cloud deployment differ from a fully managed setup?
With Private Cloud, you run Qdrant entirely on your own infrastructure or edge locations. You control data residency, isolation, and security, which suits air-gapped and regulated workloads. A managed setup instead runs the software on provider-hosted infrastructure. This listing covers only the on-premise Private Cloud option.
What features come with the Private Cloud deployment?
You get a dedicated, isolated on-premise deployment with support for air-gapped setups and full isolation. Custom service-level agreements are available. It includes access management, backup options, and disaster recovery. Contact the vendor to confirm which capabilities apply to your environment.
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Qdrant is an open-source and fully managed high-performance Vector Database. The vector search engine provides a production-ready service with a convenient API to store, search, and manage vector embeddings.
This product has charges associated with it for seller support. Qdrant is an open source, high performance vector database and similarity search engine designed for AI, machine learning, and semantic search applications.
This production-ready AMI delivers a pre-configured stack featuring Qdrant vector database, LangChain, LlamaIndex, and MinIO. Designed by Intuz for AI developers building RAG, semantic search, and ML-powered applications on AWS.
This product has charges associated with it for hardening, security configuration, and support.
Qdrant is an open-source, high-performance vector database for AI and semantic search - used in RAG pipelines, recommendation systems, LangChain, LlamaIndex, and any workload requiring similarity search over embeddings. This Lynxroute build is security baked in: unique API key at first boot, UFW firewall pre-configured, and CIS Level 1 hardened Ubuntu 24.04 LTS base.
Web UI included. fastembed pre-installed for local embedding without external API calls.
Apache-2.0 license - fully auditable, no vendor lock-in.
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