Sold by: Cedana
Cedana AI Compute Fabric automatically checkpoints and migrates live GPU workloads across Amazon EKS and Slurm without losing progress. Achieve up to 2x higher AI job throughput per GPU while increasing utilization, improving reliability, and enabling dynamic prioritization.
Overview
Cedana AI Compute Fabric provides system-level checkpointing and migration for GPU workloads running on Amazon EKS and Slurm.
Cedana makes execution state portable across nodes and instances, allowing AI training, fine-tuning, inference, and distributed workloads to pause, move, and resume without losing progress.
Unlike application-level checkpoints, Cedana operates transparently at the system layer, requiring no code changes while preserving full process state, GPU memory, and distributed context.
By decoupling AI workloads from fixed infrastructure, Cedana increases GPU utilization and delivers up to 2x higher AI job throughput per GPU. Workloads automatically recover from node failures, spot interruptions, and maintenance events without restarting from scratch.
Teams can dynamically reprioritize jobs, rebalance clusters, consolidate underutilized GPUs, and safely run long jobs on Spot instances.
The result:
- Improved reliability
- Reduced wasted compute
- Lower cloud costs
- Shorter queue times
- Higher productivity per $/GPU
Cedana integrates in minutes with Amazon EKS, and Slurm environments and supports single-node and distributed multi-GPU/CPU workloads.
Ideal for AI startups, research labs, enterprises, and platform teams operating multi-tenant GPU clusters, Cedana enables infrastructure automation, spot resilience, SLA enforcement, and efficient AI factory operations across AWS.
Highlights
- Cloud-Native GPU Checkpointing for Amazon EKS Automatically checkpoint and migrate AI workloads across Amazon EKS without code changes. Preserve full execution state, including GPU memory and distributed processes, enabling seamless recovery from node failures, spot interruptions, and autoscaling events.
- Increase Throughput 2x and Reduce GPU Wait Times Boost AI training and inference throughput by eliminating lost work from failures and preemptions. Cedana improves GPU utilization, enables dynamic job prioritization on Amazon EKS and Slurm, and reduces queue times across multi-tenant GPU clusters.
- Automate Spot Instances for Long-Running AI Jobs Run training and stateful inference workloads reliably on Amazon EC2 Spot Instances without losing progress. Cedana automatically checkpoints and resumes GPU workloads across interruptions, enabling resilient Spot usage, lower cloud costs, and significantly higher throughput per $/GPU on Amazon EKS.
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Pricing
Pricing is based on actual usage, with charges varying according to how much you consume. Subscriptions have no end date and may be canceled any time.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Description | Cost/unit |
|---|---|---|
$/GiB-hour | Instance Memory under Management | $2.00 |
Dimensions summary
You pay based on usage, measured by the memory of instances under management. The single dimension is priced per GiB-hour, meaning you are billed for each gibibyte of instance memory managed for each hour. Your cost scales directly with how much instance memory the platform oversees and for how long. There are no separate tiers or fixed plans. The more instance memory you place under management, the more you pay; less memory means a lower bill.
Top-of-mind questions for buyers
One GiB-hour is one gibibyte of an instance's memory managed by the platform for one hour. The platform meters the total memory of instances it oversees, then multiplies that memory by the time it stays under management. Both memory size and duration drive the count.
The platform saves workload state so jobs can pause without losing progress. During a pause, no compute runs, but billing tracks instance memory under management. If an instance stays under management while paused, its memory continues to count. Removing the instance from management stops that memory from accruing charges.
The platform moves running jobs across instances, clusters, regions, and clouds without restarting. Billing follows the instance memory under management, not migration events. Cost reflects how much memory the platform oversees and for how long, regardless of how many times a workload moves.
www.cedana.com
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Please email support@cedana.ai
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