GPU Cloud · AI Startups · India

AI Infrastructure Built
for Startups

Dedicated GPU pods, elastic compute and MLOps-ready environments — priced for early-stage teams, built to scale with you.

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Why Startups Choose METPL

No hyperscaler bills. No setup overhead. Just GPU compute that works from day one.

Startup Pricing

Pay-as-you-go GPU pods with no surprise egress fees or reserved-instance lock-in.

Ready in Minutes

Spin up a GPU pod with Jupyter, Docker or bare-metal access — no hardware lead time.

Scale Without Migration

Move from a single GPU to a multi-node cluster on the same platform as you grow.

Architect Support

Direct access to infra engineers who understand AI workloads — not a ticketing queue.

What You Get

Dedicated GPU Pods

Get your own GPU pod — no noisy neighbours, no shared contention. NVIDIA A-series and H-series GPUs available for training, fine-tuning and inference workloads.

  • Single GPU to multi-GPU pod configurations
  • NVMe SSD-backed local storage for fast dataset access
  • Pre-installed CUDA, cuDNN, PyTorch and TensorFlow
  • SSH, Jupyter and VS Code Server access out of the box
  • Hourly, weekly and monthly billing options
  • Snapshot and restore for long-running experiments
CI/CD & MLOps Integration

Connect your existing GitHub, GitLab or Bitbucket pipelines to GPU compute. Trigger training runs automatically on commits or schedule batch fine-tuning jobs overnight.

  • GitHub Actions and GitLab CI runner support
  • MLflow and Weights & Biases experiment tracking
  • Automated model registry and versioning
  • Container-based job queuing with Kubernetes
  • Webhook triggers for training pipeline automation
  • Inference endpoint deployment directly from registry
Object Storage & Dataset Management

Store large training datasets, model checkpoints and inference artefacts on high-throughput object storage that is co-located with your GPU pods for minimal transfer latency.

  • S3-compatible object storage API
  • Dataset versioning and lineage tracking
  • Private buckets with IAM-style access policies
  • High-bandwidth data pipeline to GPU pods
  • Lifecycle policies for cost-efficient archiving
  • Secure data residency within India
Inference & API Serving

Deploy your trained models as low-latency REST or gRPC endpoints. Auto-scale inference pods based on request volume without managing Kubernetes yourself.

  • One-click model deployment from registry to endpoint
  • Auto-scaling inference pods with GPU sharing
  • Built-in rate limiting and API key management
  • Custom domain and TLS termination
  • Latency and throughput monitoring dashboard
  • vLLM and Triton Inference Server support

Startup Use Cases

LLM Fine-Tuning

Fine-tune Llama, Mistral or your own base model on proprietary data using LoRA and QLoRA on dedicated A100 / H100 pods.

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Computer Vision Products

Train and deploy CV models for detection, segmentation or video analytics with high-throughput GPU pods and co-located storage.

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AI SaaS Backends

Run scalable inference APIs for your AI SaaS product with auto-scaling GPU pods, custom domains and per-customer isolation.

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How It Works

1
Choose GPU Pod Size
2
Spin Up in Minutes
3
Push Data & Code
4
Train & Fine-Tune
5
Deploy Inference API
6
Scale as You Grow

Startup Plans

All plans include storage, networking and architect support. Build a custom quote →

Seed Pod

For prototyping and early experiments

₹4,999/month
  • 1× NVIDIA GPU
  • 8 vCPU & 32 GB RAM
  • 500 GB NVMe Storage
  • 1 TB Object Storage
  • SSH + Jupyter Access
  • Email & Chat Support
Apply Now
Scale Pod

For production AI products at scale

Custom
  • Multi-node GPU Cluster
  • Custom CPU & RAM Config
  • Petabyte-scale Storage
  • Private VPC Networking
  • SLA-backed Uptime
  • Dedicated Architect
Consult Us

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Build Your AI Product on METPL

From first model to production inference — we have the GPU infrastructure to take you there.

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Get a Startup Proposal

Tell us about your AI workload and we'll recommend the right pod configuration within 24 hours.