Fractional GPU.
Sovereign Cloud.
Full MLOps Platform.
Run AI inference at a fraction of the cost-with data residency, per-second billing, and zero egress fees.
No credit card required. Free credits applied instantly.
Trusted by teams shipping AI
GPU Pricing
Transparent, per-hour pricing. Pick your GPU, choose your fraction.
A100
80GB VRAM
H100
80GB VRAM
L4
24GB VRAM
L40S
48GB VRAM
RTX4090
24GB VRAM
T4
16GB VRAM
V100
32GB VRAM
Products
Fractional GPU
Allocate 12.5% to 100% of any GPU. Pay only for what you use.
- A10G, A100, H100 available
- Sub-second scaling
- Real-time cost tracking
- Auto-scaling support
Serverless GPU
Zero cold-start GPU functions. Scale to zero when idle.
- Event-driven execution
- Pay per millisecond
- Auto-scaling to 1000+ instances
- Built-in load balancing
Baremetal GPU Server
Dedicated physical servers with full GPU access. No virtualization overhead.
- Full hardware access
- Custom configurations
- PCIe passthrough
- NVLink support
Pod
Containerized GPU workloads with persistent storage and networking.
- Docker & OCI compatible
- Persistent volumes
- Private networking
- SSH & Jupyter access
VM
Full virtual machines with GPU passthrough and root access.
- Ubuntu, Debian, CentOS
- Custom images supported
- Full root access
- Snapshots & backups
Time-Travel Notebooks
GPU-powered Jupyter notebooks with checkpoint time-travel. Rewind to any cell state, branch experiments, and never lose work.
- Rewind to any checkpoint
- Branch & compare experiments
- Pre-installed ML libraries
- GPU acceleration
Launch Templates
Pre-configured environments for ML, inference, and scientific computing. One click to deploy.
Axolotl CUDA 12
L40SYAML-driven LLM fine-tuning framework supporting QLoRA, LoRA, full fine-tune, FSDP, and DeepSpeed. JupyterHub included. Built on Ubuntu 22.04 with CUDA 12.4 and PyTorch 2.6. Included: - Axolotl with DeepSpeed - PyTorch 2.6 (cu124), flash-attn - transformers, peft, bitsandbytes, accelerate - JupyterHub/Lab - SSH access Use cases: LLM fine-tuning, QLoRA training, distributed training
LLaMA Factory CUDA 12
L40SNo-code browser WebUI for fine-tuning 100+ LLM models. Supports LoRA, QLoRA, and full training with wandb logging. Built on Ubuntu 22.04 with CUDA 12.4 and PyTorch. Included: - LLaMA-Factory LLaMABoard UI (port 7860) - PyTorch (cu124) - transformers, peft, trl, accelerate, bitsandbytes - SSH access Use cases: No-code LLM fine-tuning, LoRA/QLoRA training, model evaluation
Unsloth CUDA 13
L40S2x faster, 70% less VRAM fine-tuning for Llama, Qwen, Mistral, and Phi models. Optimized with xformers and TRL. JupyterHub included. Built on Ubuntu 22.04 with CUDA 12.4. Included: - Unsloth optimized trainer - PyTorch (cu124), xformers - trl, peft, accelerate, bitsandbytes - JupyterHub/Lab - SSH access Use cases: Memory-efficient LLM fine-tuning, LoRA training
Unsloth Studio Cuda 13
No code platform powered by unsloth, 2x faster, 70% less VRAM fine-tuning for Llama, Qwen, Mistral, and Phi models. Optimized with xformers and TRL. JupyterHub included. Built on Ubuntu 22.04 with CUDA 12.4. Included: - Unsloth optimized trainer - PyTorch (cu124), xformers - trl, peft, accelerate, bitsandbytes - JupyterHub/Lab - SSH access Use cases: Memory-efficient LLM fine-tuning, LoRA training
ML Operations
Everything you need to train, deploy, and monitor models in production. All tools integrated, zero glue code.
Experiments
Track, compare, and reproduce ML experiments with automatic metric logging and artifact versioning.
Model Registry
Version, stage, and deploy models with full lineage tracking and approval workflows.
Pipelines
Orchestrate end-to-end ML workflows with DAG-based pipelines and automatic retries.
Monitoring
Real-time model performance dashboards with latency, throughput, and error rate tracking.
Drift Detection
Automatically detect data and model drift. Get alerted before performance degrades.
Schedules
Automated retraining, batch inference, and recurring jobs with cron-based scheduling.
Approvals
Governance workflows for model deployment. Multi-stage approvals with audit trails.
Audit Logs
Complete audit trail of all platform actions. Track who did what, when, and why.
Agentic Platform
Secure, isolated sandboxes for AI agents. Execute code, access GPUs, and scale autonomously - all within controlled environments.
AI Sandboxes
Isolated execution environments
Give your AI agents their own GPU-powered sandbox. Each sandbox runs in complete isolation with configurable resource limits, network policies, and automatic cleanup.
- Isolated containers per agent session
- GPU access with fractional allocation
- Network policies & egress controls
- Real-time stdout/stderr streaming
- Auto-terminate on timeout or budget
- Persistent workspace across sessions
Code Execution Agents
Let AI agents write and execute code safely. Full Python/Node runtime with GPU libraries pre-installed.
Research Agents
Agents that run experiments, train models, and log results - all in isolated GPU environments with automatic tracking.
Multi-Agent Orchestration
Spin up multiple sandboxes for agent swarms. Each agent gets its own isolated environment with shared storage.
Developer Tools
SDK, CLI, API, or YAML. Your choice.
import podstack
from sklearn.datasets import load_iris
from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import train_test_split
# Initialize Global Network
HTTP response times from 18 monitoring nodes across 6 continents. Average response: 398ms
Ready to accelerate your ML workflow?
Start with ₹500 free credits. No credit card required.
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