DATA INTELLIGENCE PLATFORM
The AI Data Platform that Orchestrates Data, GPUs, and AI Services at Scale
Compute is no longer the limiting factor. Data is.
AI systems depend on how fast data can be found, moved, and delivered to models. When that breaks, GPUs sit idle. Costs rise. Millions in infrastructure sit unused.
The DDN Data Intelligence Platform solves this: a metadata-driven architecture that unifies, orchestrates, and accelerates data across distributed environments, so AI runs at the speed your business needs.
What is Data Intelligence and Why it Matters
Most AI infrastructure was not designed for how AI actually runs today. Data is distributed across environments. Pipelines are fragmented. Training and inference operate in silos. There’s no system coordinating all of it.
The result is predictable:
– GPUs sit idle waiting for data
– Pipelines slow down under load
– Costs increase without improving output
This is not a compute problem. It is a data orchestration problem.
The DDN Data Intelligence Platform solves it by unifying access to data across edge, core, and cloud, through a metadata-driven architecture. Then its AI control plane operationalizes AI-as-a-Service across the full AI lifecycle from data ingestion all the way through deployment.
Recognized as a Key Player in AI Infrastructure
Why AI Infrastructure Breaks at Scale
Most AI infrastructure was not designed for how AI actually runs today.
Data is distributed across environments. Pipelines are fragmented. Training and inference operate in silos. There is no system coordinating how everything works together.
The result is predictable:
- GPUs sit idle waiting for data
- Pipelines slow down under load
- Costs increase without improving output
This is not a compute problem. It is a data orchestration problem.
The DDN Data Intelligence Platform solves it by unifying access to data across edge, core, and cloud, through a metadata-driven architecture. Then its AI control plane operationalizes AI-as-a-Service across the full AI lifecycle from data ingestion all the way through deployment.
Delivering Measurable AI Economics
Up to 99% GPU Utilization
70% Less Power. 18X More Output Per Watt
Reduce Cost Per Query by 90%
Months Faster Time to Production
KEY CAPABILITIES
A Unified Platform for AI Data Orchestration and Operations
Unified Data Across Distributed Environments
Metadata-Driven Data Intelligence
HOW IT WORKS
Four Components. One Intelligent Platform. The DDN Data Intelligence Platform.
Data Layer
AI Control Plane
AI Services Layer
Industry Solutions
USE CASES
Powering AI Production at Scale
AI Factories
AI Clouds and GPU as a Service
Sovereign AI
Enterprise AI at Scale
Ratanaphon Wongnapachant
SIAM AI Founder & CEO
CUSTOMER STORIES
Real Results. Real Impact. Real Stories.
Core42 powers national-scale AI with DDN
- Deploy enterprise AI without prohibitive costs
- Scale workloads globally without performance trade-offs
xAI trains Grok 10x faster with DDN
- 10x performance gain across AI workloads
- 200,000 GPUs running without data bottlenecks
Roche unlocks early cancer detection with DDN
- Analysis time cut from two weeks to two days
- Faster path from research to clinical impact
Questions About the DDN Data Intelligence Platform
A data intelligence platform orchestrates how data is stored, moved, and delivered across the AI lifecycle. It ensures data is available for training, inference, and RAG at the speed AI systems require.
Unlike traditional storage or infrastructure solutions, it coordinates data, compute, and AI services as a single system. This is what enables high GPU utilization and efficient AI at scale.
Most AI infrastructure is built around compute, but data pipelines cannot keep up. Data is distributed, fragmented, and often slow to access.
When data delivery lags, GPUs sit idle, pipelines stall, and costs increase without improving output. This is why improving data orchestration has a greater impact on AI performance than adding more compute.
DDN orchestrates how data moves across training, inference, and RAG. It ensures GPUs are continuously fed with data at the speed they require.
By eliminating data stalls and coordinating workloads, organizations can increase GPU utilization to as high as 99 percent and get more output from the same infrastructure.
The platform reduces cost by increasing efficiency at every layer. Higher GPU utilization means less idle infrastructure. Faster data delivery means more queries and tokens per GPU.
This leads to lower cost per query, lower cost per token, and better overall AI economics without increasing hardware spend.