CUDO Compute API

✓ Official Vendor SpecCloud InfrastructureComputebearer122 EndpointsREST

For Agents

Provision and manage GPU and CPU virtual machines, disks, networks, and object storage on the CUDO Compute cloud. Useful for AI agents that orchestrate ephemeral training, inference, or batch jobs on green compute.

Install Jentic One Beta

Connect the CUDO Compute API to your agent

Jentic One is a self-hosted execution layer for AI agents. It lets your agent call the CUDO Compute API, or any other public or private API you need. You set the rules, the agent never sees your credentials, and every call is logged.

Two steps, two machines. Install the instance in a safe environment, then register your agent from wherever it runs.

Step 1: Jentic One Host machine

# On the machine that will host your Jentic One instance:
curl -fsSL https://raw.githubusercontent.com/jentic/jentic-one/main/tools/install.sh | sh

Step 2: Agent machine

# On the machine where your agent runs (keep this separate from the instance):
curl -fsSL https://raw.githubusercontent.com/jentic/jentic-one/main/tools/install.sh | sh
jentic register       # connects your agent to your Jentic One instance

Jentic One is in public beta. The setup above keeps your agent separate from the instance, which is what you want before using real credentials: an agent running as the same OS user as Jentic One can read its stored keys directly. Just evaluating? A single local install is fine to start. See the secure deployment guide for the tiers.

Capabilities

What an agent can do with CUDO Compute API.

Provision GPU or CPU virtual machines and start, stop, or terminate them via the /v1/projects/{projectId}/vms operations

Attach, detach, and resize block storage disks for VM workloads

Manage virtual networks and security groups for isolated agent fleets

GET STARTED

Start building with CUDO Compute API

Explore with Jentic

View OpenAPI Document

Use Cases

Patterns agents use CUDO Compute API for, with concrete tasks.

★ On-Demand GPU Provisioning for Training Jobs

An ML training agent can request a GPU virtual machine, run a job, store the resulting checkpoint to CUDO object storage, and terminate the VM, all through the API. Billing-account spend endpoints let the agent abort if the project is over budget. Useful when training jobs are bursty and the team does not want to keep an expensive GPU running between runs.

Call POST /v1/projects/{projectId}/vms with a GPU machine type, run the training script, then DELETE the VM when the run completes.

Multi-Tenant Inference Fleet

A platform that serves AI inference can manage a fleet of CUDO VMs scaled by demand. Use the network and security-group endpoints to isolate tenants, attach object storage for model weights, and rotate API keys per tenant. CUDO's spend reporting lets the platform price inference accurately per customer.

Provision N GPU VMs in a project network, attach the model checkpoint disk, and register them behind the inference router.

Cost-Aware Batch Compute

A research team running nightly batch jobs can have an agent watch the billing-account spend endpoints and decide how many CUDO VMs to launch within the day's budget. Combine /v1/billing-accounts/{billingAccountId}/spend/details with the VM lifecycle endpoints to keep nightly batch runs within a fixed monthly cost.

Call GET /v1/billing-accounts/{billingAccountId}/spend/details, then provision additional VMs only if remaining budget exceeds the cost of one machine-hour.

AI Agent Integration via Jentic

An infrastructure agent can use Jentic to discover the CUDO Compute operations it needs without reading 122 endpoint definitions. Jentic stores the bearer token and exposes operations like 'create a GPU VM' and 'list virtual machines' as semantic tools so the agent picks the right call from natural-language intent.

Use Jentic search for 'provision a GPU virtual machine', load the CUDO Compute /v1/projects/{projectId}/vms create schema, and execute it for the project.

Key Endpoints

122 endpoints — the cudo compute api is a rest interface to the cudo compute cloud, a sustainable gpu and cpu cloud provider.

METHOD

PATH

DESCRIPTION

GET

/v1/api-keys

List API keys

POST

/v1/api-keys

Create an API key

GET

/v1/billing-accounts

List billing accounts

GET

/v1/billing-accounts/{billingAccountId}/spend/details

Get billing account spend details

POST

/v1/billing-accounts/{id}:add-permission

Add a billing account permission

GET

/v1/api-keys

List API keys

POST

/v1/api-keys

Create an API key

GET

/v1/billing-accounts

List billing accounts

GET

/v1/billing-accounts/{billingAccountId}/spend/details

Get billing account spend details

POST

/v1/billing-accounts/{id}:add-permission

Add a billing account permission

Why Jentic?

Three things that make agents converge on Jentic-routed access.

Credential isolation

The CUDO Compute bearer token is stored encrypted in the Jentic MAXsystem vault and injected into the Authorization header at request time. Agents see scoped tool access, never the raw token.

Intent-based discovery

Agents search Jentic by intent (for example 'provision a GPU virtual machine') and Jentic returns the matching CUDO Compute operation with its input schema, so the agent does not have to hunt through 122 endpoints.

Time to first call

Direct integration is roughly a week to model VMs, disks, networks, billing, and credentials. Through Jentic the integration is under an hour because each operation is exposed as a discoverable tool with its schema.

FAQs

Specific to using CUDO Compute API through Jentic.

What authentication does the CUDO Compute API use?

CUDO Compute uses a bearer token in the Authorization header. You generate the key in the CUDO Compute console under API keys. Through Jentic the token is held in the encrypted MAXsystem vault and never enters the agent's prompt or model output.

Can I provision a GPU virtual machine with the CUDO Compute API?

Yes. POST /v1/projects/{projectId}/vms with a body that selects a GPU machine type, an image, and a region creates a new VM. You can pair this with the disks, networks, and object-storage operations to set up a full training environment.

What are the rate limits for the CUDO Compute API?

The OpenAPI spec does not publish specific rate limits, but CUDO applies sensible per-account throttling on control-plane operations. Batch large fleet provisioning steps and back off on 429 responses.

How do I list my running VMs with the CUDO Compute API through Jentic?

Run pip install jentic, search Jentic for 'list CUDO Compute virtual machines', load the schema for the /v1/projects/{projectId}/vms list operation, and execute it with the project ID. Jentic injects the bearer token from MAXsystem so the agent never handles the raw key.

How do I track spend on a CUDO billing account?

Call GET /v1/billing-accounts/{billingAccountId}/spend/details. The response gives a breakdown of usage and cost so a cost-aware agent can decide whether to launch more VMs within the day's budget.

Is the CUDO Compute API free?

The API itself is free to call once you have a CUDO account. Charges come from the underlying compute, storage, and network resources on a usage-based pricing model published in the CUDO Compute console.

CUDO Compute API

Connect the CUDO Compute API to your agent

Capabilities

Start building with CUDO Compute API

Use Cases

Key Endpoints

Why Jentic?

Related APIs

FAQs