DataStax offers an AI‑ready data platform that combines the Astra DB cloud database, the low‑code Langflow workflow builder, and integrated tools for ingesting, enriching, and retrieving unstructured and multimodal data. The platform provides built‑in encryption, fine‑grained access controls, and governance, and can be deployed on‑prem, hybrid, or multi‑cloud environments to support scalable, production‑grade AI workloads.
Funding
$115M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
4O+1Founders
Product
Problem
Enterprises struggle to manage large volumes of unstructured, multimodal data needed for AI applications, facing high operational complexity, fragmented tooling, and limited ability to secure and govern data across on‑prem, hybrid, and multi‑cloud environments.
Solution
DataStax provides an AI‑ready data platform that combines the Astra DB cloud database, the low‑code Langflow workflow builder, and integrated tooling for data ingestion, enrichment, and retrieval. The platform automates handling of real‑time unstructured data, offering built‑in encryption, fine‑grained access controls, and governance features. It runs on any infrastructure—on‑prem, hybrid, or multi‑cloud—allowing organizations to deploy and scale AI workloads with consistent performance. Vector search and knowledge‑graph capabilities enable fast similarity queries, while Langflow’s visual flow designer simplifies building and deploying generative AI pipelines. Together with IBM watsonx integration, the solution delivers end‑to‑end orchestration of data and AI models, reducing total cost of ownership and operational overhead.
Target Audience
Primary customers are large enterprises and technology teams that need to build, scale, and govern AI‑driven applications across distributed environments, including data engineers, AI developers, and IT operations groups.
Features
- Astra DB provides high‑availability, linear‑scalable multi‑model storage with low‑latency vector search for AI workloads
- Built‑in data ingestion, enrichment, and retrieval pipelines that automate handling of unstructured and multimodal data
- Enterprise‑grade security and governance, including encryption at rest, role‑based access controls, and audit logging
- Langflow low‑code visual designer for rapid creation, testing, and deployment of generative AI workflows
- Open‑source, low‑code tooling that supports deployment on any cloud, on‑prem, or hybrid environment
- Seamless integration with IBM watsonx.data and watsonx Orchestrate for end‑to‑end AI model management
- Multi‑model support (vector, graph, document) enabling flexible data modeling for diverse AI use cases