Accelerate AI application development and deployment with the platform that supports RAG apps, from idea to production.
Visual IDE for building RAG applications, based on LangChain and leveraging leading AI tools.
Ultra-low latency database with vector and knowledge graph capabilities.
Get your data AI-ready with chunking, vector embedding, and knowledge graph generation.
Integrated with the top AI-cloud providers—NVIDIA, AWS, Microsoft Azure, and Google Cloud.
Improve query relevancy, app performance, and lower operational costs with an effective chunking strategy.
Getting unstructured data ready for AI is a critical step. Learn how vector embeddings power AI applications.
Improve query relevancy without bolt-on solutions. Generate and store knowledge data in your vector database.
Integrate with the industry-leading applications you already use without learning multiple APIs.
The DataStax AI PaaS simplifies AI RAG app development and data management, from idea through to production, whether cloud or self-managed, in a secure and scalable manner via deep integrations with the top AI cloud providers.
DataStax AI PaaS is a cloud-based platform designed to accelerate AI application development. It integrates tools like Langflow for AI workflows, Astra DB for vector search, and a NoSQL data API, allowing developers to build and scale generative AI applications with minimal infrastructure management.
AI PaaS provides vector search capabilities that help AI models quickly retrieve relevant data, essential for use cases like natural language processing, recommendation engines, and generative AI applications. It also integrates with frameworks like TensorFlow, PyTorch, and OpenAI APIs.
Yes, AI PaaS is optimized for real-time AI workloads, enabling low-latency data retrieval and processing. By integrating with Astra Streaming, it can power real-time AI pipelines, supporting applications like fraud detection, real-time personalization, and predictive analytics.
AI PaaS simplifies AI development by offering a managed environment with built-in vector search, data APIs, and AI workflow tools. This reduces the time and resources needed to build AI applications, allowing developers to focus on creating innovative solutions without managing complex infrastructure.
AI PaaS uses Astra DB for vector search and NoSQL data storage, enabling AI models to access and analyze large datasets in real time. Langflow provides a low-code environment for building AI workflows and retrieval-augmented generation (RAG) applications, making it easy to develop AI agents and pipelines.
Yes, AI PaaS supports deployments on AWS, Google Cloud, and Microsoft Azure, allowing businesses to scale AI applications globally while avoiding vendor lock-in. Its cloud native architecture ensures seamless performance across different environments.
AI PaaS offers enterprise-grade security features such as end-to-end encryption, role-based access control (RBAC), and compliance with industry standards like GDPR, SOC 2, and HIPAA. This ensures that AI models and data are protected from unauthorized access.
AI PaaS is used by industries such as financial services, healthcare, retail, and telecommunications, where real-time AI insights and scalable AI applications are essential. Use cases include fraud detection, personalized customer experiences, predictive maintenance, and AI-powered chatbots.
