ML Researchers — Public Pool
Research moves at the speed of your experimentation loop. ResonTech removes the infrastructure bottlenecks that slow that loop down.
Pain Points Solved
| Problem | What happens without ResonTech |
|---|---|
| Queue wait | University clusters and shared cloud queues kill research velocity. You submit a job and wait hours or days. |
| Idle billing | Cloud GPUs charge by the hour. An experiment that runs for 20 minutes costs you a minimum 60-minute block. |
| Failed job recovery | A preempted spot instance or OOM crash means starting over. No checkpointing, no recovery. |
| Environment setup | Every new machine means reinstalling CUDA, dependencies, and configs before you can run a single experiment. |
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
- Public pool: start immediately with no commitment
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"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
Production ML Teams — Managed Cluster
Most teams run training and inference on completely separate stacks. ResonTech is one platform for both — dedicated GPU capacity, same API, one dashboard.
Pain Points Solved
| Problem | What happens without ResonTech |
|---|---|
| Two infrastructure stacks | Training on one cloud, inference on another. Different configs, different bills, different failure modes to debug. |
| Inference scaling | Traffic spikes mean manually scaling replicas, managing cold starts, and overpaying for idle inference capacity at 3AM. |
| Training failures | A node failure mid-run restarts the job from zero. Your team loses compute time and the release schedule slips. |
| ML engineers doing DevOps | Your ML engineers spend 30–40% of their time on infra, not on making better models. |
Key Benefits
- Training + inference on one platform, one dashboard, one bill
- Dedicated reserved nodes — no queue contention
- Zero-config inference endpoints with autoscale to zero
- 99.9% uptime SLA on managed cluster
Enterprise — Private Cluster
Regulated industries — healthcare, finance, defense — can't send training data to shared infrastructure. ResonTech's Private Cluster brings the orchestration kernel to your hardware.
Pain Points Solved
| Problem | What happens without ResonTech |
|---|---|
| Data residency | You can't send patient data, financial records, or classified material to a public GPU cloud. Full stop. |
| Existing GPU fleet | You already own expensive GPU hardware. Getting it to run distributed ML workloads reliably requires a team of infrastructure engineers. |
| Compliance requirements | HIPAA, SOC 2, GDPR, and defense-grade requirements rule out shared multi-tenant infrastructure. |
| DevOps overhead | Running your own Kubernetes-based ML platform requires specialized engineering and constant maintenance. |
Key Benefits
- Kernel runs entirely within your network perimeter
- Zero data egress — training data never leaves your infrastructure
- Air-gapped mode available — no inbound internet after setup
- Works on your existing GPU fleet
- Dedicated account manager and custom SLA
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Contact office@reson.tech for enterprise deployments.