Use Cases

Built for every stage
of ML.

Researchers, production teams, and enterprises — each with a cluster model that fits how they work.

Platform Includes
✓Distributed Training
✓Inference Endpoints
✓Auto Fault Recovery
✓Live Telemetry
✓Python SDK + API
✓Multi-GPU Scheduling
ML ResearchersProduction ML TeamsEnterprise
Public Pool

Stop waiting for compute.
Start testing hypotheses.

Research moves at the speed of your experimentation loop. ResonTech makes that loop as fast as your ideas.

Problem

Queue
Waiting in GPU queues
University clusters and shared cloud queues kill research velocity. You submit a job and wait hours or days.
Billing
Overpaying for idle time
Cloud GPUs charge by the hour. An experiment that runs for 20 minutes costs you a minimum 60-minute block.
Recovery
Re-running failed jobs
A preempted spot instance or OOM crash means starting over. No checkpointing, no recovery — just wasted time.
Setup
Environment setup overhead
Every new machine means reinstalling CUDA, dependencies, and configs before you can run a single experiment.

How It Works

01
Upload shards to your bucket
Every account gets a private S3 bucket (Garage). Push your dataset once via browser, rclone, or SDK. Datasets of any size.
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02
Submit your experiment
Point to your script, specify GPU count. ResonTech picks the best nodes and hands each worker a presigned URL for its shard.
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03
Get results, automatically
Artifacts and logs stream back to your bucket under jobs/<name>/. Failed runs resume from the last checkpoint.
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04
Run the next experiment immediately
No cluster teardown or rebuild. Submit another job — it starts in seconds.

Key Benefits

✓Pay only for actual job time — zero idle cost between runs
✓Run 10 experiments in parallel for the cost of one sequential run
✓Automatic checkpoint recovery on failure
✓Any framework — PyTorch, TensorFlow, HuggingFace Trainer
✓No infra setup between experiments
✓Public pool: start immediately with no commitment

"The feedback loop is everything in research. We went from running 3 experiments a day to 15 — not because we have more compute, but because we stopped wasting time on infrastructure."

— ML Researcher, Computer Vision

Managed Cluster

One platform for training and serving.
Zero DevOps.

Most teams run training and inference on completely separate stacks. ResonTech is the first platform that does both, under one API, with dedicated GPU capacity.

Problem

Stack
Two separate infrastructure stacks
Training on one cloud, inference on another. Different configs, different bills, different failure modes to debug.
Scaling
Inference scaling nightmares
Traffic spikes mean manually scaling replicas, managing cold starts, and overpaying for idle inference capacity at 3AM.
Failures
Training failures block releases
A node failure mid-run restarts the job from zero. Your team loses compute time and the release schedule slips.
DevOps
ML engineers doing DevOps
Your ML engineers spend 30–40% of their time on infra, not on making better models. That is the real hidden cost.

How It Works

01
Get dedicated GPU capacity
Managed cluster assigns nodes reserved exclusively for your team. No queue contention, no noisy neighbors.
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02
Run training jobs
Submit jobs via the platform or API. Distributed training is auto-configured. Failures recover from checkpoints automatically.
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03
Deploy inference endpoints
Push your trained model. Get a live endpoint URL. Autoscaling, load balancing, and health checks are included.
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04
Monitor and iterate
One dashboard for training metrics, inference latency, GPU utilization, and costs — across both workloads.

Key Benefits

✓Dedicated nodes — no shared pool contention
✓Training + inference from one platform and API
✓SLA-backed uptime for production workloads
✓Autoscaling inference to zero between traffic spikes
✓OpenAI-compatible API for LLM serving
✓Checkpoint recovery — no full reruns after failure

"We killed our SageMaker setup and our Lambda Labs account on the same day. Everything runs on ResonTech now — training during the day, inference 24/7. One bill, one team managing it."

— ML Platform Lead, Series B startup

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Private Cluster

Your hardware. Your data.
Your compliance team won't complain.

For enterprises with data residency requirements, existing GPU infrastructure, or strict compliance obligations — Private Cluster brings ResonTech's orchestration to your own hardware.

Problem

Compliance
Data cannot leave the perimeter
Training data, model weights, and inference inputs are proprietary or regulated. You cannot send them to a third-party cloud.
Utilization
Idle on-prem GPUs
You invested in hardware but scheduling across multiple teams is manual, chaotic, and inefficient. GPUs sit idle for 40–60% of the time.
Orchestration
No consistent orchestration layer
Different teams use different tools — Slurm, Kubernetes, bare metal scripts. No unified platform, no central visibility.
Audit
Compliance and audit requirements
HIPAA, SOC 2, internal data governance — every training job and model deployment needs an audit trail.

How It Works

01
Connect your GPU fleet
Install the ResonTech agent on your on-prem or cloud-hosted GPU nodes. Takes minutes per node.
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02
Set access policies
Define which teams can access which nodes. Set GPU quotas, RBAC roles, and audit log destinations.
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03
Teams submit jobs normally
Your ML teams use the same platform and API as every other ResonTech cluster. Zero learning curve.
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04
Data never leaves your network
The kernel runs inside your perimeter. Training data, artifacts, and model weights stay on your hardware.

Key Benefits

✓Zero data egress — all compute stays within your perimeter
✓Air-gapped deployment available for sensitive environments
✓Unified scheduling across all your GPU hardware
✓RBAC, SSO integration, and full audit logs
✓Increases GPU utilization from ~40% to ~80%+
✓Compliance-ready: HIPAA, SOC 2, GDPR configurations

"We had 200 GPUs sitting at 45% utilization because scheduling was a mess. After deploying ResonTech's private cluster, we're at 82% — and compliance is finally happy."

— Head of AI Infrastructure, Fortune 500

Get Started

Not sure which cluster
type fits you?

See the full side-by-side comparison of Public Pool, Managed Cluster, and Private Cluster — features, pricing model, and who each is for.

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