WHY ONE PATH THROUGH STORAGE ISN'T ENOUGH
AI workloads don't read storage the same way.
Training pulls sequential bandwidth across petabytes. Inference pulls sub-millisecond reads against KV caches. Retrieval pulls metadata-heavy concurrent reads against billions of objects. Agents pull mixed and bursty. A single path forces every workload to a compromise. The AI data path has to vary with the workload, or the workload pays for it in latency, throughput, or both.
INSIDE AIConnect
Different paths for training, inference, and retrieval.
The connector path
Scality ADI delivers multi- TB/sec reads and writes and sub-millisecond latency, including RDMA-enabled S3 where appropriate. Not every AI workload reads the same way, and a single connector path cannot serve every access pattern. AIConnect runs several paths in parallel and routes each workload to the one that fits.
Ecosystem integrations
Scality ADI integrates seamlessly with the AI infrastructure customers are deploying in production. Certifications, reference architectures, and tested integrations — not vendor compatibility lists.
Workload-aligned access
Each workload type uses the connector path that fits it. Training pulls sequential bandwidth. Inference pulls sub-millisecond KV cache reads. Retrieval pulls metadata-heavy concurrent reads. Agents pull mixed and bursty. The routing is the value, not any single path.
AICONNECT IN THE PORTFOLIO
AIConnect runs only under Scality ADI.
The AI data path is a platform capability of Scality ADI itself. It moves data from storage to the GPU at the speed each AI workload demands.
WHERE AIConnect RUNS
Multi-TB/s ingest, sub-millisecond latency, NVIDIA AIDP and CuObject integration, KV cache connector, and workload- aligned routing all ship under ADI as platform capabilities. The AI data path is part of the product, not bolted on.


















