ML Researchers — Public Pool
Research moves at the speed of your experimentation loop. ResonTech removes the infrastructure bottlenecks that slow that loop down — no queue wait, no hourly billing, no manual environment setup.
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 — just wasted time. |
| Environment setup | Every new machine means reinstalling CUDA, dependencies, and configs before you can run a single experiment. |
Workflow
| Step | What happens |
|---|---|
| 01 · Upload | Upload your training data and scripts to your S3 bucket via the Files page or rclone. Workers pull from your bucket when the job starts. |
| 02 · Submit | Point to your script via the platform or API. Specify GPU count. ResonTech picks the best available nodes. |
| 03 · Get results | Artifacts and logs are written back to your folder. Failed runs resume from the last checkpoint. |
| 04 · Iterate | No cluster teardown or rebuild. Submit another job — it starts in seconds. |
Key Benefits
- Billed by the second — not the hour
- Run 10 experiments in parallel for the cost of one sequential run
- Automatic checkpoint recovery on failure
- Any framework — PyTorch, TensorFlow, HuggingFace Trainer
- Bucket auto-provisioned — just upload data and submit
- Public pool: start immediately with no commitment
i
"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. That is the real hidden cost. |
Workflow
| Step | What happens |
|---|---|
| 01 · Dedicated capacity | Managed cluster assigns nodes reserved exclusively for your team. No queue contention, no noisy neighbors. |
| 02 · Training | Submit jobs via the platform or API. Distributed training is auto-configured. Failures recover from checkpoints automatically. |
| 03 · Inference | Push your trained model. Get a live endpoint URL. Autoscaling, load balancing, and health checks are included. |
| 04 · Monitor | 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
i
"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
Enterprise — Private Cluster
For enterprises with data residency requirements, existing GPU infrastructure, or strict compliance obligations — Private Cluster brings ResonTech's orchestration to your own hardware. Your data never leaves your perimeter.
Pain Points Solved
| Problem | What happens without ResonTech |
|---|---|
| Data compliance | Training data, model weights, and inference inputs are proprietary or regulated. They cannot go to a third-party cloud. |
| Idle on-prem GPUs | You invested in hardware but scheduling across multiple teams is manual, chaotic, and inefficient. GPUs sit idle 40–60% of the time. |
| No unified orchestration | Different teams use different tools — Slurm, Kubernetes, bare metal scripts. No unified platform, no central visibility. |
| Audit requirements | HIPAA, SOC 2, internal data governance — every training job and model deployment needs an audit trail. |
Workflow
| Step | What happens |
|---|---|
| 01 · Connect your fleet | Install the ResonTech agent on your on-prem or cloud-hosted GPU nodes. Takes minutes per node. |
| 02 · Set access policies | Define which teams can access which nodes. Set GPU quotas, RBAC roles, and audit log destinations. |
| 03 · Teams submit jobs | Your ML teams use the same platform and API as every other ResonTech cluster. Zero learning curve. |
| 04 · Data stays local | 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
i
"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