Paperspace

Drive the product through a documented public API G

Agent access

Connect an agent via an official MCP server G

Agent access

Delegate tasks to a built-in AI assistant inside the product G

Agentic features

Plug MCP servers into this product so it can use their tools G

Agent access

Use an official CLI G

Agent access

Build against official SDKs G

Agent access

Run the product headlessly / in CI for automation G

Agent access

Point an agent at llms.txt or agent-oriented docs G

Agent access

Set up automations that run autonomously in the background G

Agentic features

Download a machine-readable API spec (OpenAPI or equivalent) G

Api quality

Explore an interactive API reference with runnable examples G

Api quality

Issue scoped/least-privilege API credentials for an agent G

Agent access

Rely on versioned APIs with a documented deprecation policy G

Api quality

Subscribe to events via webhooks G

Agent access

Get AI-generated insights and suggestions from my data inside the product G

Agentic features

Operate the product with natural-language commands G

Agentic features

Test against a sandbox environment without touching production data G

Api quality

Attach persistent network storage that survives instance teardown, so datasets and checkpoints outlive any single GPU rental C

Storage

Provision a GPU, monitor it, run a workload, and tear it down end to end through documented APIs, CLI, or MCP without a human in the console C

Agent ops

Provision an on-demand GPU instance from the console or API and be running code on it within minutes C

Provision

SSH into my GPU instance with my own keys and get root-level control of the environment C

Ssh

Export all of my data in open formats and leave G

I am billed at per-second or per-minute granularity and only while my instance is actually running G

Billing

Provision a multi-node GPU cluster with fast interconnect for distributed training without a sales cycle C

Clusters

See the published per-GPU-hour price for every GPU type on a public pricing page without talking to sales G

Pricing

Define rules that trigger actions automatically on events G

Prevent my data from being used to train AI models G

Rent spot or interruptible GPU capacity at a deep discount with clearly documented preemption semantics G

Spot

Self-host the core product G

Run my own Docker image or custom machine template with my exact environment C

Images

Choose from current-generation datacenter GPUs (H100/H200/B200 class) as well as cheaper previous-generation options C

Hardware

Do everything through the API that I can do in the UI G

Set auto-shutdown timers or spend limits so a forgotten instance can't silently run up a huge bill C

Manage

Launch from pre-built ML templates (PyTorch, CUDA, vLLM, ComfyUI) instead of assembling an environment from scratch C

Templates

Start, stop, restart, and terminate instances programmatically and keep paying only for what is running C

Manage

Choose where my data is stored (region/residency) G

See documented quotas and instance limits and raise them through a defined process G

Quotas

Deploy code to autoscaling serverless GPU workers that scale to zero, instead of managing always-on instances C

Serverless

Move data in and out efficiently — S3-compatible endpoints, cloud-storage sync, or documented transfer tooling C

Data movement

Perform bulk operations across many items at once G

Query the GPU catalog with live pricing and availability from a public or documented endpoint before committing any spend G

Discovery

Schedule recurring jobs or workflows G

See real-time GPU availability by type and region before I try to provision, instead of discovering stockouts by failure C

Availability

Control data retention and deletion G

Opt out of telemetry and usage tracking G

Pull usage and billing breakdowns programmatically to attribute GPU spend by team or workload G

Billing

Read the product's source under an open license G

Schedule jobs on managed Slurm or Kubernetes instead of building my own scheduler on raw nodes C

Orchestration

Verify the provider's security and compliance posture (SOC 2, data handling, datacenter tiers) before putting proprietary models on it C

Compliance

Expose ports to serve applications from my instance and connect instances over private networking C

Networking

Open Jupyter or connect my IDE (VS Code/Cursor) to the instance in one step C

Ide

Lock in reserved or committed-use discounts for sustained GPU capacity G

Pricing

Manage team members with roles and scoped API keys so credentials and spend stay controlled G

Governance

Version, review, and roll back my automations G