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We’re excited to share that Qdrant and Cohere are partnering on the launch of Qdrant Hybrid Cloud to enable global audiences to build and scale their AI applications quickly and securely. With Cohere's world-class large language models (LLMs), getting the most out of vector search becomes incredibly easy. Qdrant's new Hybrid Cloud offering and its Kubernetes-native design can be coupled with Cohere's powerful models and APIs. This combination allows for simple setup when prototyping and deploying AI solutions.
It’s no secret that Retrieval Augmented Generation (RAG) has shown to be a powerful method of building conversational AI products, such as chatbots or customer support systems. With Cohere's managed LLM service, scientists and developers can tap into state-of-the-art text generation and understanding capabilities, all accessible via API. Qdrant Hybrid Cloud seamlessly integrates with Cohere’s foundation models, enabling convenient data vectorization and highly accurate semantic search.
With Qdrant Hybrid Cloud, users have the flexibility to deploy their vector database in an environment of their choice. By using container-based scalable deployments, global businesses can keep both products deployed in the same hosting architecture. By combining Cohere’s foundation models with Qdrant’s vector search capabilities, developers can create robust and scalable GenAI applications tailored to meet the demands of modern enterprises. This powerful combination empowers organizations to build strong and secure applications that search, understand meaning and converse in text.
Take Full Control of Your GenAI Application with Qdrant Hybrid Cloud and Cohere
Building apps with Qdrant Hybrid Cloud and Cohere’s models comes with several key advantages:
Data Sovereignty: Should you wish to keep both deployment together, this integration guarantees that your vector database is hosted in proximity to the foundation models and proprietary data, thereby reducing latency, supporting data locality, and safeguarding sensitive information to comply with regulatory requirements, such as GDPR.
Massive Scale Support: Users can achieve remarkable efficiency and scalability in running complex queries across vast datasets containing billions of text objects and millions of users. This integration enables lightning-fast retrieval of relevant information, making it ideal for enterprise-scale applications where speed and accuracy are paramount.
Cost Efficiency: By leveraging Qdrant's quantization for efficient data handling and pairing it with Cohere's scalable and affordable pricing structure, the price/performance ratio of this integration is next to none. Companies who are just getting started with both will have a minimal upfront investment and optimal cost management going forward.
Start Building Your New App With Cohere and Qdrant Hybrid Cloud
We put together an end-to-end tutorial to show you how to build a GenAI application with Qdrant Hybrid Cloud and Cohere’s embeddings.
Tutorial: Build a RAG System to Answer Customer Support Queries
Learn how to set up a private AI service that addresses customer support issues with high accuracy and effectiveness. By leveraging Cohere’s models with Qdrant Hybrid Cloud, you will create a fully private customer support system.
Documentation: Deploy Qdrant in a Few Clicks
Our simple Kubernetes-native design lets you deploy Qdrant Hybrid Cloud on your hosting platform of choice in just a few steps. Learn how in our documentation.
Read Hybrid Cloud Documentation
Ready to Get Started?
Create a Qdrant Cloud account and deploy your first Qdrant Hybrid Cloud cluster in a few minutes. You can always learn more in the official release blog.