Cedana is the
Automation Layer For AI Factories

Automatically save, migrate, and resume live GPU workloads across your infrastructure, increasing your AI productivity per $/GPU. Works from a single node to entire AI factories. Start with one instance and scale seamlessly.

Unlock Your 
Scheduler
Seamless integration with your existing infrastructure,
designed for high-performance computing.

Work with What you Have

No rip-and-replace. No code changes. No disruption to your teams.

Kubernetes & SLURM

Build for HPC. Native support for SLURM workload manager job queues.

Your First Migration in Under 30 min

AI Workloads
Cannot Move Once Running

AI workload execution state is tightly bound to the GPUs where jobs start, forcing schedulers like Kubernetes and Slurm to commit resources upfront and locking infrastructure into rigid allocations that waste compute and limit productivity per $/GPU.

Expensive Failures

Over-provisioned GPUs

Idle GPUs

Rigid Infrastructure


Cedana Brings
Liquidity to AI Infrastructure

By making AI workload execution state portable, live GPU workloads can be safely saved, migrated, and resumed across instances without losing progress or restarting. With execution mobility, infrastructure becomes adaptive in real time, improving reliability, productivity, and operational efficiency.

Automated Reliability

Workloads automatically migrate to healthy infrastructure and resume after failures with no lost progress.

Eliminate Overprovisioning

Automatic migration and recovery remove the need for large safety buffers to meet SLAs and QoS.

Adaptive Infrastructure

Kubernetes and SLURM adapt workloads in real time to failures and demand.

Maximize Throughput

Workloads shift to idle GPUs, reclaiming capacity and maximizing cluster throughput.


The Cedana
Difference

Without Migration

With Migration

Expensive Failures

Up to 65% compute lost

Over-provisioned GPUs

10-50% Capacity Buffers

Idle GPUs

Stranded Compute While Jobs Wait

Rigid Infrastructure

Schedulers Cannot Adapt

Automated Reliability

Workloads Resume Automatically

Eliminate Overprivisioning

SLAs and Reliability without Safety Buffers

Maximize Throughput

Workloads Migrate to Idle GPUs

Adaptive Infrastructure

Workloads Adjust in Real Time


Automation Use Cases

Designed for high-performance compute environments where reliability and throughput are non-negotiable.

Reliability

Automatically continue workloads from catastrophic failures without losing progress or restarting.

Productivity

Automatically migrate workloads to eliminate idle GPUs and increase throughput.

Reliability

Automatically continue workloads from catastrophic failures without losing progress or restarting.

2-5x

Improvement in Productivity

"Cedana's infrastructure layer allowed us to increase throughput by 80% without changing our code, effectively doubling our research velocity."

Caltech Computational Biology

Department of Biology and Biological Engineering

Before / After Implementation

Throughput


Built for High Performance AI and HPC

Native support for NCCL and MPI workloads. Achieve massive scale with note-aware scheduling and low-latency interconnect optimization.

Advanced Workloads

Works with distributed multi-node compute, including NCCL and MPI workloads. Works with both CPU and GPU workloads.

Scalability

Works across on-premise clusters, hybrid environments, and cloud infrastructure. Scale from a single node, to cluster, to AI factory.


READY TO
SCALE?

Run a proof of Concept on Your Infrastructure.

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