Execution Assurance for AI Workloads.
Infrastructure failures shouldn't become workload failures.
Vector Fabric provides an independent execution layer that helps AI teams verify infrastructure, monitor execution, preserve recoverable state, and recover supported workloads when underlying compute fails.
Execution Paradox
What Happens After the Workload Starts?
AI infrastructure has become increasingly heterogeneous, but execution responsibility remains fragmented.
Schedulers and infrastructure platforms can provision compute and launch workloads. Once a long-running workload is executing, however, teams still need to understand whether it is progressing, preserve recoverable state, respond to infrastructure failures, and determine what actually happened across the run.
Infrastructure Changes
Workers can disappear, capacity can become unavailable, and infrastructure conditions can change while a workload is running.
Progress Has Value
Hours of completed computation can represent meaningful time, cost, and research progress.
Recovery Requires State
Moving execution is not enough. A workload must have durable state and compatible resume behavior to continue from a checkpoint.
Execution Needs Evidence
Teams need visibility into attempts, infrastructure events, checkpoints, recovery actions, and final workload status.
The challenge isn't simply keeping infrastructure alive. It's preserving execution through infrastructure change.
Platform Capabilities
Execution Control Across Supported Environments
Execution Reliability
Help long-running, stateful workloads continue execution and reach completion when underlying compute resources fail or fluctuate.
Checkpoint-Aware Recovery
Coordinate recovery using durable workload checkpoints, resuming progress rather than restarting executions from scratch.
Infrastructure Flexibility
Run workloads across supported cloud, specialized GPU, or private compute environments without modifying core application logic.
Execution Visibility
Maintain a structured execution record of attempts, infrastructure events, recovery actions, and final workload completion status.
Durable Recovery
Recovery Requires More Than Restarting a Container
Infrastructure can be replaced. Workload progress cannot—unless execution state has been preserved.
Vector Fabric is checkpoint-aware. For supported workloads, durable checkpoints provide a recovery boundary that allows execution to resume after infrastructure interruption.
* Recovery behavior depends on the workload's checkpoint and resume capabilities. Durable checkpoint storage provides recoverable state independent of individual compute workers.
Interoperability
Built Around the Infrastructure You Already Use
Vector Fabric works with supported customer compute environments, adding an independent execution layer around AI workloads without requiring teams to replace their underlying infrastructure.
Bring your workload and infrastructure. Vector Fabric adds execution assurance across the workload lifecycle.
Differentiating Architectural Principle
A New Machine Isn’t a Recovered Workload
Infrastructure orchestration can provision another worker after a failure. But successful workload recovery requires more: identifying recoverable state, recreating a compatible execution environment, resuming correctly, and tracking the new attempt as part of the same workload lifecycle.
Vector Fabric is built around the workload—not just the machine running it.
Generic Infrastructure Failover
Replaces failed compute, but does not necessarily restore workload state or execution continuity.
Workload-Level Recovery
Uses durable workload state to coordinate recovery and maintain continuity across execution attempts.
True Economics
The Real Cost Is Cost-to-Completion
GPU price per hour tells only part of the story. Failed runs, lost progress, retries, idle resources, and engineering intervention all contribute to the true cost of completing an AI workload.
Vector Fabric connects infrastructure usage with workload outcomes—creating visibility into the true operational effort and infrastructure cost required to finish a job.
Workload Fit
Where Execution Assurance Matters Most
Long-Running Training
Training and fine-tuning jobs where interruption can erase hours of expensive computation.
Stateful AI Workloads
Jobs with checkpoints, artifacts, or intermediate state that must survive infrastructure interruption.
Large Batch & Research
Compute-intensive experiments and pipelines where restarting creates meaningful time or cost penalties.
Heterogeneous Environments
Teams operating workloads across supported cloud, specialized GPU, or private infrastructure.
The longer the workload, the more valuable its accumulated execution state becomes.
Bring Us a Workload Where Execution Matters
We're working with early design partners to understand real AI/ML workloads and the infrastructure challenges surrounding them. If you have a long-running, stateful, compute-intensive, or operationally difficult workload, we'd like to understand how it runs today and explore where Vector Fabric could help.
Explore a Design Partnership