Run your GPUs like a frontier lab
Schedule training jobs, track experiments, and serve endpoints. Use one tool or all three, with reliability built into each.
5M+ GPU hours managed
From local to 64 H100s in under an hour - the fastest GPU setup we've seen
Scaled to hundreds of GPUs on demand,reducing infrastructure costs by 50%.
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Backed by
Stop managing GPUs. Start using them.
Join leading AI teams that reduce their compute costs by 50% while shipping faster.
Meet Konduktor
Deploy AI workloads at scale on any cloud with a simple YAML file. Paste in your existing torchrun command, set num_nodes, and Konduktor handles the distributed setup.
num_nodes: 64 # scale up your workload
accelerators: H100:8
image_id: nvcr.io/nvidia/pytorch:26.06-py3
priority-class: high-priority
run: |
torchrun --master_addr=$MASTER_ADDR ...
Catch hardware failures before they kill your run
Konduktor detects bad GPUs and nodes, cordons them, and reschedules your job onto healthy hardware automatically. Its health checks were built from managing 5M+ GPU hours across nearly every cloud.
Debug your training code faster
Aggregated logs and diagnostics surface the real error so you fix the bug instead of chasing infrastructure ghosts.
From model checkpoint to production API in minutes
Deploy inference endpoints with autoscaling, load balancing, and health checks built in, on your own cluster.
Track every experiment without the lag
Meet Pluto, experiment tracking built for responsiveness at scale. W&B and Neptune-compatible APIs, dual-logging support, MCP integration. Sub-second UI even with thousands of runs.
Responsive
Filter thousands of runs, compare metrics, and drill into results without waiting, even mid-training.
Scalable
Handle 100M+ logged data points without slowing down. Built for teams running large-scale experiments across distributed infrastructure.
Reliable
Over 5 billion metric points stored with zero dropped. Client-side buffering holds your metrics and ensures nothing is lost mid-run.
Know exactly where your compute is going
Managers get a per-team, per-engineer view of GPU usage and cost, making it easy to allocate compute to what matters most.
Deploy anywhere
Deploy Konduktor on any cloud or on bare metal. Whether your GPUs are on a hyperscaler, neocloud, or in your own racks, your team gets one consistent interface.
Frequently Asked Questions
Submitting jobs in Trainy's platform is done via a simple yaml file that can work across clouds. You just need to enter your existing torchrun or equivalent launch command and our platform handles the rest. Read our docs for more details.
No. For most of our customers, we help them pick a cloud provider offering that makes the most sense for their specific use case. We then assist with hardware validation to ensure they are getting the promised performance. If you already have a reserved GPU cluster, our solution can be deployed in the cloud or on-prem. For startups, we can help you go from cloud credits to a functional multi-node training setup with high bandwidth networking in < 20 mins.
Kubernetes gives AI teams higher ROI on the same pool of compute. All top-tier AI research teams (OpenAI, Meta, etc.) have similar systems in place. With automated scheduling and cleanup of queued workloads, AI engineers never have to worry about GPU availability or compatibility. On the other hand, decision makers get improved visibility and control into their team's cluster usage and can make informed purchasing decisions.
Trainy offers all of the resource sharing and scheduling benefits of Slurm with much more. Teams get better workload isolation via containerization, integrated observability, and improved robustness with comprehensive health monitoring.
The first step to reducing GPU spend is cutting idle time. If you have a reserved cluster, this means having a fault-tolerant scheduler in place. A scheduler allows your team to maintain a workload queue and keep your GPUs busy 24/7, while fault-tolerance ensures that GPU failures do not require manual restarts. New and restarted workloads are placed on healthy nodes, even if they fail in the middle of the night. Once idle time has been minimized, step 2 is to look at your workload efficiency.
Most Trainy customers stream data into their GPU cluster from an object store such as Cloudflare R2. In the longer term, we are looking at distributed file system integrations, but this does not exist today.
Yes. Konduktor gives you a single submission interface across clusters in different clouds and regions, so your team can queue and route jobs wherever capacity is available. Each individual job runs on one cluster, but you manage and burst across all of them from one place.
The earlier, the better. When your company is exploring gen AI applications, we help you run large-scale experiments cost-effectively. When the time comes to choose a cloud provider, we work with you to navigate cloud provider offerings, and ensure you are getting maximum performance.
Ready to scale your AI training? Get enterprise-grade GPU infrastructure up and running in 20 minutes.