AI infrastructure is heterogeneous.
Execution shouldn’t be fragile.
Vector Fabric provides an independent execution plane for long-running AI/ML workloads—reducing the complexity of heterogeneous GPU infrastructure, maintaining execution visibility, and preserving progress when infrastructure fails.
The Infrastructure Paradox
AI Infrastructure Is Fragmenting. Execution Reliability Isn’t Keeping Up.
AI workloads increasingly run across public hyperscalers, specialized GPU clouds, and on-premises clusters. Each environment presents disparate hardware configurations, failure modes, driver behaviors, and provisioning APIs.
Schedulers can decide where a job starts. But once it is live, teams are left manually wrangling infrastructure volatility, coupled runtime state, and broken runs.
Fragmented Infrastructure
Teams operate across heterogeneous providers with incompatible interfaces, disparate networking fabrics, and non-uniform scheduling semantics.
Volatile Compute
Spot capacity disappears, nodes silently degrade, and hardware timeouts interrupt long-running jobs without proactive coordination.
State Is Coupled
Job execution state remains tethered to specific physical nodes. When hardware faults occur, progress is forfeited back to distant epochs.
Operational Burden
Engineers spend hours actively orchestrating, synchronizing, and babysitting runs when nothing has failed—diverting engineering focus to basic machine operations.
Decouple workload execution from underlying physical machine fragility.
Platform Capabilities
Reliable Execution for AI Workloads
Execution Reliability
Enables long-running training, fine-tuning, and batch pipelines to continue through underlying hardware degradations and spot capacity evictions.
Checkpoint-Aware Recovery
Resumes workloads directly from verified durable state stored in your sovereign object storage, reducing lost progress after infrastructure interruption.
Infrastructure Flexibility
Execute across supported cloud, specialized GPU, and private infrastructure without tying workload execution to a single provider.
Execution Visibility
Maintains unified execution lineage, logging health heartbeats, provider transitions, and checkpoint state in real time.
$ vectorfabric status --job pretrain-moe-v2
Interoperability
Built Around the Infrastructure You Already Use
Start with your current compute stack. Vector Fabric wraps an independent execution and fault-tolerance layer around supported AI workloads without requiring teams to migrate storage or replace bare metal.
Execution Economics
Optimize for Cost-to-Completion, Not Just Cost-per-GPU-Hour
Lower GPU prices don't necessarily mean lower execution costs. An interrupted workload that must restart can erase the savings from cheaper compute.
Vector Fabric focuses on cost-to-completion—helping teams use heterogeneous GPU infrastructure without making reliability the tradeoff.
Target Workloads
Built for Long-Running, Infrastructure-Sensitive Workloads
Long-Running Training
Multi-day pre-training and fine-tuning workloads where restarting can waste substantial compute and engineering time.
Large Batch Inference
High-throughput synthetic data pipelines and distributed embedding workloads with significant state.
Stateful Workloads
Jobs requiring strict coordination of optimizer states, model weights, and partitioned dataset shards.
Heterogeneous Clusters
Infrastructure teams distributing execution across dynamic mixtures of private on-prem and specialized GPU clouds.
Designed for Customer-Controlled Data
Vector Fabric manages the execution control plane without ingesting proprietary training datasets, weights, or tokens. All workload artifacts remain strictly isolated inside your sovereign infrastructure and VPC boundaries.
Have a workload where execution reliability matters?
We are actively onboarding select engineering teams running production training, fine-tuning, and batch pipelines across complex compute environments.
Guided by distributed systems, AI infrastructure, and enterprise technology leaders → Meet our Advisory Council