terradev-cli 1.3.1

Real multi-cloud GPU arbitrage — provision across 10 clouds in parallel with BYOAuth

Terradev

Parallel provisioning and orchestration for cross-cloud cost optimization.

Developers overpay for compute by only accessing single-cloud workflows or using slow sequential provisioning with inefficient egress and rate-limiting.

Terradev is a cross-cloud arbitrage CLI that saves developers 60% on end-to-end compute provisioning costs. It works faster than any sequential tool to compress + stage datasets, parallel-provision optimal instances + nodes, and deploy 5x faster than baseline provisions, at an average of 3 minutes, with egress optimization.

Terradev is built modular, managing APIs through the end-client's discrete portfolio of cloud credentials (BYOAPI).

Installation

pip install terradev-cli

With cloud provider extras:

pip install terradev-cli[all]     # AWS + GCP + Azure SDKs
pip install terradev-cli[aws]     # Just AWS (boto3)
pip install terradev-cli[gcp]     # Just GCP
pip install terradev-cli[azure]   # Just Azure

Quick Start

# 1. Configure your cloud credentials (BYOAPI — you own your keys)
terradev configure --provider runpod
terradev configure --provider aws
terradev configure --provider vastai

# 2. Get real-time quotes across all configured providers
terradev quote -g A100

# 3. Provision the cheapest instance (real API call)
terradev provision -g A100

# 4. Provision 4x H100s in parallel across multiple clouds
terradev provision -g H100 -n 4 --parallel 6

# 5. Dry-run to see the allocation plan without launching
terradev provision -g A100 -n 2 --dry-run

# 6. Manage running instances
terradev status --live
terradev manage -i <instance-id> -a stop
terradev manage -i <instance-id> -a start
terradev manage -i <instance-id> -a terminate

# 7. Execute commands on provisioned instances
terradev execute -i <instance-id> -c "python train.py"

# 8. Stage datasets near compute (compress + chunk + upload)
terradev stage -d ./my-dataset --target-regions us-east-1,eu-west-1

# 9. View cost analytics from the tracking database
terradev analytics --days 30

# 10. Find cheaper alternatives for running instances
terradev optimize

# 11. One-command Docker workload (provision + deploy + run)
terradev run --gpu A100 --image pytorch/pytorch:latest -c "python train.py"

# 12. Keep an inference server alive
terradev run --gpu H100 --image vllm/vllm-openai:latest --keep-alive --port 8000

BYOAuth — Bring Your Own Authentication

Terradev never touches, stores, or proxies your cloud credentials through a third party. Your API keys stay on your machine in ~/.terradev/credentials.json — encrypted at rest, never transmitted.

How it works:

1. You run terradev configure --provider <name> and enter your API key
2. Credentials are stored locally in your home directory — never sent to Terradev servers
3. Every API call goes directly from your machine to the cloud provider
4. No middleman account, no shared credentials, no markup on provider pricing

Why this matters:

• Zero trust exposure — No third party holds your AWS/GCP/Azure keys
• No vendor lock-in — If you stop using Terradev, your cloud accounts are untouched
• Enterprise-ready — Compliant with SOC2, HIPAA, and internal security policies that prohibit sharing credentials with SaaS vendors
• Full audit trail — Every provision is logged locally with provider, cost, and timestamp

CLI Commands

Command	Description
terradev configure	Set up API credentials for any provider
terradev quote	Get real-time GPU pricing across all clouds
terradev provision	Provision instances with parallel multi-cloud arbitrage
terradev manage	Stop, start, terminate, or check instance status
terradev status	View all instances and cost summary
terradev execute	Run commands on provisioned instances
terradev stage	Compress, chunk, and stage datasets near compute
terradev analytics	Cost analytics with daily spend trends
terradev optimize	Find cheaper alternatives for running instances
terradev run	Provision + deploy Docker container + execute in one command

Supported Cloud Providers

Provider	GPU Types	Spot Savings	Regions
AWS	A100, H100, A10G, RTX 4090	70-90%	Global
Google Cloud	A100, H100, L4, T4	60-80%	Global
Azure	A100, H100, ND A100 v4	65-85%	Global
RunPod	A100, H100, RTX 4090	50-70%	Global
Vast.ai	A100, H100, RTX 4090	50-75%	Global
Lambda Labs	A100, H100, RTX 3090	40-60%	US/EU
CoreWeave	A100, H100, RTX 4090	45-65%	US/EU
TensorDock	A100, RTX 4090, RTX 3090	50-70%	Global
Oracle Cloud	A100, GPU3, GPU2	40-60%	Global
Baseten	A100, H100	Inference-optimized	US/EU

Infrastructure Hooks

Terradev integrates with end-user managed infrastructure when available:

• Kubernetes — Deploy to existing K8s clusters
• Karpenter — Multi-cloud node provisioning via Karpenter CRDs
• Grafana — Dashboard creation for training job monitoring
• Open Policy Agent — Policy-as-code compliance enforcement

Cost Savings Examples

GPU Type	On-Demand (AWS)	Best Spot Price	Terradev Best	Savings
A100	$4.06/hr	$1.22/hr	$0.84/hr	79%
H100	$7.20/hr	$2.16/hr	$1.50/hr	79%
RTX 4090	$1.21/hr	$0.36/hr	$0.25/hr	79%

Prices queried in real-time from all 9 providers. Actual savings vary by availability.

Project Structure

terradev_cli/
├── cli.py                   # Main CLI (quote, provision, manage, status, etc.)
├── core/
│   ├── cost_tracker.py      # SQLite cost tracking database
│   ├── dataset_stager.py    # Compression, chunking, parallel staging
│   ├── egress_optimizer.py  # Cross-cloud data transfer cost optimization
│   ├── parallel_provisioner.py  # Concurrent multi-cloud deployment engine
│   ├── tier_manager.py      # Research / Research+ / Enterprise tier logic
│   └── rate_limiter.py      # API rate limiting
├── providers/
│   ├── base_provider.py     # Abstract provider interface
│   ├── aws_provider.py      # AWS EC2 (boto3)
│   ├── gcp_provider.py      # Google Cloud Compute
│   ├── azure_provider.py    # Azure Compute
│   ├── runpod_provider.py   # RunPod GraphQL API
│   ├── vastai_provider.py   # Vast.ai REST API
│   ├── lambda_labs_provider.py  # Lambda Cloud API
│   ├── coreweave_provider.py    # CoreWeave API
│   ├── tensordock_provider.py   # TensorDock API
│   └── provider_factory.py  # Dynamic provider instantiation
└── utils/
    └── formatters.py        # Output formatting

Pricing Tiers

	Research (Free)	Research+ ($49.99/mo)	Enterprise ($299.99/mo)
Provisions/month	10	100	Unlimited
Max concurrent instances	1	4	32
Providers	All 10	All 10	All 10 + priority
Cost tracking	Yes	Yes	Yes
Dataset staging	Yes	Yes	Yes
Egress optimization	Basic	Full	Full + custom routes

Integrations

Jupyter / Colab / VS Code Notebooks

pip install terradev-jupyter

%load_ext terradev_jupyter

%terradev quote -g A100
%terradev provision -g H100 --dry-run
%terradev run --gpu A100 --image pytorch/pytorch:latest --dry-run

GitHub Actions

- uses: theoddden/terradev-action@v1
  with:
    gpu-type: A100
    max-price: "1.50"
  env:
    TERRADEV_RUNPOD_KEY: ${{ secrets.RUNPOD_API_KEY }}

Docker (One-Command Workloads)

terradev run --gpu A100 --image pytorch/pytorch:latest -c "python train.py"
terradev run --gpu H100 --image vllm/vllm-openai:latest --keep-alive --port 8000

Requirements

• Python >= 3.9
• Cloud provider API keys (configured via terradev configure)

License

MIT License - see LICENSE file for details