WEKA announced that Andromeda, the platform that connects AI teams with high-performance compute across an open market of providers, is integrating WEKA NeuralMesh as a core data storage layer for its managed GPU clusters. Andromeda customers can choose between a dedicated NeuralMesh deployment and a GPU-native NeuralMesh Axon configuration, giving them consistent storage performance on any cluster running at any hyperscaler or AI cloud.
Andromeda operates across more than 50 compute providers worldwide, routing training and inference workloads through a growing fleet of managed clusters, and says it serves more than 100 AI customers, supporting billions of GPU-hours. Every cluster on the platform must meet the same quality benchmarks, regardless of who operates the infrastructure, and Andromeda certifies each one across GPU, storage, network fabric, and security before it reaches a customer. That standard exposed the problem: each provider brought its own storage environment, and performance varied from cluster to cluster.
NeuralMesh aims to standardize that data layer. For Andromeda, the platform provides a deployable storage architecture that is consistently configured across hardware providers, rather than relying on each cloud or data center’s native shared-storage implementation.
“Our goal is to enable the global flow of compute, and that means every cluster we deliver has to perform, no matter where the capacity comes from,” said Wil Moushey, CEO at Andromeda. “Standardizing on WEKA NeuralMesh has given our customers hyperscaler-grade consistency with open-market flexibility at the storage and memory layer, where performance is won or lost. Idle GPUs are throttling the pace of AI innovation. WEKA helps us ensure every GPU we manage is earning its keep.”
GPU-Native Storage With NeuralMesh Axon
NeuralMesh Axon is the GPU-native deployment of the NeuralMesh software platform. It fuses storage directly into Andromeda’s GPU servers, converting each cluster’s existing NVMe into a unified data layer that feeds training and inference. The economic argument follows from that: the hardware is already racked, powered, and paid for, so the performance arrives without adding footprint, power draw, or cost. WEKA cites one case where a research lab found its workloads bottlenecked by the storage provided by its cluster, and Andromeda deployed Axon on the same hardware rather than adding infrastructure.
Andromeda uses the NeuralMesh Kubernetes Operator to automate cluster deployment and lifecycle management. The company reports that new NeuralMesh Axon clusters can be deployed from bare metal in about 10 minutes. Roughly half of its NeuralMesh deployments use the Axon configuration, while the rest are dedicated deployments for customers requiring exclusive GPU compute and memory resources.
“Andromeda’s first WEKA NeuralMesh Axon deployment took just 10 minutes to stand up from bare metal. That became our blueprint,” said Vishvajit Kher, lead architect at Andromeda. “If a cloud provider doesn’t offer shared storage, we’re no longer blocked. We deploy WEKA NeuralMesh and know it will perform. It’s a full-featured system, up to 90% less expensive than market alternatives, delivering savings that compound as we scale.”
The deployment model enables Andromeda to bring up managed GPU clusters in environments where cloud providers do not offer shared storage. This broadens the range of provider infrastructure that meets its operational requirements.
Reported Production Results
Andromeda reports that NeuralMesh Axon clusters sustain more than 400 GB/s of aggregate throughput per cluster and exceed 6 million IOPS in production deployments. In one biotech workload involving metadata-intensive datasets with about one billion files per directory, the platform reduced data transfer time from hours to minutes.
The company also reports that AI teams using its managed clusters reduced environment startup times from about three minutes to 30 seconds. Faster environment initialization can increase the number of training, testing, and inference iterations teams can execute during a development cycle.
NeuralMesh includes fault-isolation capabilities to prevent localized failures from cascading across a cluster. For providers operating multi-tenant GPU environments, this helps maintain workload availability while reducing the operational impact of infrastructure faults.
Extending Across a Global AI Compute Network
The WEKA deployment is expanding across Andromeda’s global infrastructure, including dedicated storage configurations and GPU-native Axon systems. The companies also plan to extend the integration to newer generations of AI infrastructure as Andromeda adds capacity and provider coverage.
For managed GPU providers, the deployment highlights the growing need for a portable AI data platform. GPU capacity alone does not guarantee predictable AI performance when storage and metadata behavior vary across providers. Standardizing the storage layer helps maintain throughput, IOPS, and operational workflows across a distributed fleet of heterogeneous GPU clusters.