The Execution Control Plane for AI Infrastructure
Vector Fabric provides an independent execution layer designed to help long-running AI/ML workloads continue through infrastructure disruption while providing visibility into workload execution and recovery.
The Infrastructure Paradox
AI Infrastructure Is Fragmenting. Execution Reliability Isn’t Keeping Up.
AI workloads increasingly run across cloud, specialized GPU providers, and private infrastructure. Each environment has different APIs, failure modes, availability, and operational characteristics.
Schedulers can decide where a workload starts. But once it is running, teams are often left managing failures, recovery, state, and execution evidence themselves.
Fragmented Infrastructure
AI teams increasingly operate across cloud, specialized GPU providers, and private infrastructure.
Volatile Compute
Workers disappear, capacity changes, and infrastructure failures can interrupt expensive workloads.
State Is Coupled
Without durable checkpoints and recoverable state, infrastructure failure can mean lost progress.
Operational Burden
Researchers and engineers shouldn't have to babysit infrastructure and manually reconstruct failed runs.
Infrastructure is heterogeneous. Execution shouldn't be fragile.
Platform Capabilities
Reliable Execution for AI Workloads
Execution Reliability
Help long-running workloads continue and complete when underlying infrastructure fails or fluctuates.
Checkpoint-Aware Recovery
Resume supported workloads from durable state rather than restarting entire runs from the beginning.
Infrastructure Flexibility
Operate across customer-approved cloud, specialized GPU, and private infrastructure without changing your workload code.
Execution Visibility
Maintain a clear, unified record of workload status, infrastructure events, and recovery actions across the run.
$ vectorfabric run --stateful
>> Environment verified across target infrastructure...
>> State Control: ACTIVE (Checkpointing Verified)
Durable Checkpoint Available
Interoperability
Built Around the Infrastructure You Already Use
Start with your existing compute environment. Vector Fabric adds an independent execution layer around supported AI workloads without requiring teams to replace their underlying infrastructure.
Execution Economics
Optimize for Cost-to-Completion, Not Just Cost-per-GPU-Hour
The cheapest GPU-hour isn't necessarily the cheapest successful workload. Failed runs, lost progress, idle capacity, and manual recovery all contribute to the true cost of execution.
Vector Fabric connects infrastructure usage with workload outcomes—creating visibility into the real cost of completing long-running AI computation.
Ideal Applications
Built for Workloads Where Failure Matters
Long-Running Training
Training and fine-tuning jobs where restarting means losing hours of compute.
Large Batch & Research
Compute-intensive experiments and processing pipelines with meaningful execution state.
Stateful AI Workloads
Workloads where checkpoints, artifacts, and intermediate state need to survive infrastructure interruption.
Heterogeneous Compute
Teams operating across cloud, specialized GPU infrastructure, or private environments.
Designed for Customer-Controlled Data
Vector Fabric is being designed to operate around customer-controlled workloads and data, adding execution and recovery capabilities without requiring Vector Fabric to become the system of record for sensitive datasets.
Have a Workload Where Execution Reliability Matters?
We're working with early design partners to understand real AI/ML workloads, infrastructure environments, and execution challenges.
Vetted By
Guided by pioneers in distributed systems, enterprise infrastructure, and foundational AI.
View Research & Advisory Council →