Hybrid Cloud
Qdrant Hybrid Cloud is a deployment tier of the Qdrant vector database that allows organizations to run managed Qdrant clusters on any cloud platform, on-premise infrastructure, or edge locations.
Publisher review
Qdrant Hybrid Cloud is a deployment tier of the Qdrant vector database that allows organizations to run managed Qdrant clusters on any cloud platform, on-premise infrastructure, or edge locations. Launched by Qdrant (founded in 2021), this tier targets enterprises that need data privacy, sovereignty, and low-latency retrieval without sacrificing the performance of a dedicated vector database. It is particularly suited for AI applications like semantic search, recommendation systems, and RAG pipelines where data must stay within specific geographic or regulatory boundaries. By offering a unified management interface, Qdrant Hybrid Cloud simplifies cluster administration across diverse environments, making it a fit for teams that require both flexibility and operational control.
The Hybrid Cloud tier provides a full set of Qdrant's core capabilities, including advanced one-stage filtering, storage optimization, quantization, and both horizontal and vertical scalability. It supports payload-based sharding, which allows distributing data based on metadata attributes for more efficient queries. A standout feature is BM42, a pure vector-based hybrid search model that combines keyword and semantic retrieval for more accurate and efficient results. The system is built in Rust, delivering high performance even with billions of vectors, as demonstrated in benchmarks where Qdrant clusters on the West Coast showed 50ms lower latency compared to East Coast deployments. The tier integrates seamlessly with Kubernetes, enabling orchestration across any supported infrastructure.
In the competitive vector database landscape, Qdrant Hybrid Cloud positions itself against Weaviate, Elasticsearch, Milvus, Pinecone, and Redis. Unlike fully managed services like Pinecone, Qdrant offers a self-hosted option with managed convenience, appealing to organizations that cannot use public cloud due to compliance or cost constraints. Compared to open-source alternatives like Milvus, Qdrant provides a more streamlined deployment experience with built-in hybrid search via BM42. However, it lacks the extensive ecosystem of Elasticsearch or the serverless simplicity of Pinecone. The tier supports deployment on Oracle Cloud Infrastructure, Red Hat OpenShift, Vultr, DigitalOcean, OVHcloud, Scaleway, Civo, and STACKIT, giving it broad infrastructure compatibility.
The honest trade-offs with Qdrant Hybrid Cloud include undisclosed pricing, which complicates budget planning compared to competitors with transparent tiers. While the feature set is rich, public documentation on specific implementation details for hybrid cloud setups is limited, potentially requiring deeper vendor engagement. The focus on vector workloads means it may not replace general-purpose databases for non-vector queries. Additionally, the reliance on Kubernetes for orchestration could be a barrier for teams without containerization expertise. Despite these drawbacks, the combination of data isolation, advanced search models, and flexible deployment makes it a compelling choice for enterprises prioritizing sovereignty and performance.
How it works
-
Kubernetes integration
Seamlessly deploys and manages Qdrant clusters on Kubernetes, enabling orchestration across cloud, on-premise, or edge environments.
-
Multi-environment deployment
Supports any cloud platform, on-premise infrastructure, or edge locations, including OCI, Red Hat OpenShift, Vultr, DigitalOcean, and more.
-
Data privacy isolation
Ensures data privacy and sovereignty through complete database isolation, keeping data within specified geographic or regulatory boundaries.
-
Unified management interface
Offers a single pane of glass for managing clusters across all deployment locations, simplifying operations and monitoring.
-
Advanced filtering and optimization
Provides one-stage filtering, storage optimization, quantization, and payload-based sharding for efficient vector search.
-
BM42 hybrid search model
A pure vector-based hybrid search model that enhances retrieval accuracy and efficiency by combining keyword and semantic approaches.
-
Horizontal and vertical scalability
Scales both horizontally by adding nodes and vertically by increasing node resources, handling billions of vectors with minimal latency.
Strengths and trade-offs
Strengths
- High performance with Rust-based architecture, achieving 50ms lower latency on West Coast deployments compared to East Coast in benchmarks.
- Advanced one-stage filtering and quantization reduce storage costs while maintaining search accuracy for large-scale vector datasets.
- BM42 hybrid search model provides more accurate and efficient retrieval than traditional vector-only or keyword-only approaches.
- Flexible deployment across 8+ infrastructure providers including OCI, Red Hat OpenShift, and Vultr, offering broad compatibility.
Trade-offs
- Pricing details are not disclosed for the Hybrid Cloud tier, making it difficult to estimate costs compared to competitors with transparent pricing.
- Public documentation on specific implementation details for hybrid cloud setups is limited, potentially requiring direct vendor support.
- Focus on vector workloads means it is not a general-purpose database, limiting its use for non-vector queries or transactional workloads.
- Reliance on Kubernetes for orchestration may pose a barrier for teams without containerization or DevOps expertise.
Pricing context
Pricing not disclosed for Hybrid Cloud tier; Qdrant Cloud starts at $25/month for a 1GB cluster, with enterprise plans available by quote.
Getting started with Hybrid Cloud
-
Sign up for Hybrid Cloud
Visit the Qdrant website and request access to the Hybrid Cloud tier. Complete the registration form with your organization details. A sales representative will contact you to discuss deployment requirements and provide credentials for the management interface.
-
Connect your Kubernetes cluster
Install the Qdrant operator on your Kubernetes cluster using the provided Helm chart or YAML manifests. Configure the operator with your credentials and specify the target environment, such as cloud, on-premise, or edge. Verify the connection through the unified management interface.
-
Configure cluster settings
In the management interface, define your cluster parameters including node count, resource limits, and storage options. Enable features like payload-based sharding and quantization based on your data distribution needs. Set data isolation policies to comply with geographic or regulatory requirements.
-
Load and index vector data
Use the Qdrant client library or REST API to connect to your cluster. Upload your vector embeddings and associated payloads. Configure the BM42 hybrid search model by specifying both semantic and keyword fields. Run an initial indexing job to build the search index.
-
Deploy and monitor the cluster
Promote your cluster to production by scaling nodes horizontally or vertically as needed. Set up monitoring alerts in the unified interface for latency, storage usage, and query performance. Schedule regular backups and test failover procedures to ensure operational readiness.
Frequently Asked Questions
What is Qdrant Hybrid Cloud and how does it work?
Qdrant Hybrid Cloud is a deployment tier that lets organizations run managed Qdrant vector database clusters on any cloud, on-premise infrastructure, or edge locations. It provides a unified management interface for cluster administration across diverse environments, targeting enterprises needing data privacy and low-latency retrieval for AI workloads.
Does Qdrant Hybrid Cloud support data privacy and sovereignty?
Yes, Qdrant Hybrid Cloud ensures data privacy and sovereignty through complete database isolation, keeping data within specified geographic or regulatory boundaries. This makes it suitable for AI applications like semantic search and RAG pipelines where data must stay compliant with local laws or internal policies.
What is the BM42 hybrid search model in Qdrant Hybrid Cloud?
BM42 is a pure vector-based hybrid search model that combines keyword and semantic retrieval for more accurate and efficient search results. It enhances retrieval accuracy compared to traditional vector-only or keyword-only approaches, making it a standout feature for applications requiring precise search across large datasets.
How does Qdrant Hybrid Cloud compare to Pinecone and Milvus?
Unlike fully managed Pinecone, Qdrant Hybrid Cloud offers a self-hosted option with managed convenience, appealing to organizations with compliance constraints. Compared to open-source Milvus, Qdrant provides a more streamlined deployment experience with built-in hybrid search via BM42, but lacks Pinecone's serverless simplicity or Milvus's extensive community ecosystem.
What are the deployment options for Qdrant Hybrid Cloud?
Qdrant Hybrid Cloud supports deployment on Oracle Cloud Infrastructure, Red Hat OpenShift, Vultr, DigitalOcean, OVHcloud, Scaleway, Civo, and STACKIT. It integrates seamlessly with Kubernetes for orchestration across cloud, on-premise, or edge environments, offering broad infrastructure compatibility for flexible deployment.
What are the main drawbacks of Qdrant Hybrid Cloud?
Pricing details are not disclosed for the Hybrid Cloud tier, complicating budget planning. Public documentation on hybrid cloud implementation is limited, potentially requiring vendor support. The focus on vector workloads means it cannot replace general-purpose databases, and reliance on Kubernetes may challenge teams without containerization expertise.
Alternatives
How Hybrid Cloud compares
Direct head-to-head against 3 competitors. Picked by 7wData.
Hybrid Cloud
- Pricing
- Pricing not disclosed for Hybrid Cloud tier; Qdrant Cloud starts at $25/month for a 1GB cluster, with enterprise plans available by quote.
- Target
- Qdrant Hybrid Cloud is a deployment tier of the Qdrant vector database that allows organizations to run managed Qdrant clusters on any cloud platform, on-premise
- Strength
- High performance with Rust-based architecture, achieving 50ms lower latency on West Coast deployments compared to East Coast in benchmarks.
- Watch for
- Pricing details are not disclosed for the Hybrid Cloud tier, making it difficult to estimate costs compared to competitors with transparent pricing.
IBM Hybrid Cloud
- Pricing
- Custom/Contact sales
- Target
- Enterprises with legacy systems
- Deployment
- On-prem + public cloud
- Strength
- Mainframe integration
- Watch for
- Complex pricing tiers
Rackspace Private Cloud
- Pricing
- $1,500+/month base
- Target
- Mid-market VMware shops
- Deployment
- Managed VMware stack
- Strength
- 24/7 Fanatical Support
- Watch for
- Acquired by Apollo 2021
Data Intensity Private Cloud
- Pricing
- Oracle workload pricing
- Target
- Oracle application hosting
- Deployment
- Oracle-optimized hardware
- Strength
- Oracle-certified DBAs
- Watch for
- Niche focus limits flexibility
User reviews
No user reviews yet. Be the first to write one.
Sources
Reporting on this tool draws on these publicly available sources.