BudPod

Run your own compute cloud.

BudPod is the platform to provision, orchestrate, and serve GPU compute on infrastructure you own. Built for enterprises and cloud providers.

Deployed on-premise, in colocation, and in sovereign, air-gapped environments.

The platform

One platform. Your whole fleet.

Pool your GPUs into a single service. Give your users on-demand pods, serverless endpoints, and multi-node clusters from one control plane. No custom tooling to build. No orchestration stack to maintain.

Provision compute on demand

Your users launch a fully configured, GPU-enabled environment on demand. From the newest accelerators to older cards already in your racks, BudPod runs them all.

Serve across every site and region

Present one service to your users while workloads run across your data centres, sites, and regions. Route by location, capacity, or policy.

Scale on autopilot

Serverless workers scale from zero to hundreds and back, tracking demand in real time. Idle capacity returns to the pool.

The components

Five components. One control plane.

Your users consume the fleet five ways, from one account.

Pods

On-demand GPU instances with full control of container and runtime.

Serverless

Autoscaling API endpoints that cost nothing when idle.

Clusters

Multi-node GPU with high-speed interconnect and Slurm.

Hub

One-click catalogue of templates and open-source models.

BudPod SDK

Serverless GPU endpoints from pure Python, no Docker.

BudPod SDK

GPU compute from pure Python.

Decorate a function, deploy it, and get a serverless GPU endpoint.

import budpod
 
@budpod.endpoint(gpu="H100")
def generate(prompt: str) -> str:
    model = load_model("my-org/llm")
    return model(prompt)
 
# $ budpod deploy  →  live GPU endpoint

No Dockerfile, no registry push CPU-to-GPU pipelines, routed automatically Works with Claude Code, Cursor, and Cline

Open source, on PyPI.

Fractional GPU

One GPU. Many isolated slices.

Partition accelerators into isolated slices. Tenants use only what they need. Utilisation goes up.

1×
Accelerator

→

Slice · Tenant A Slice · Tenant B Slice · Tenant C Slice · Tenant D

Tenant isolation

Isolation down to the fabric.

Every tenant is isolated. No routable path, no traffic bleed.

Tenant A

Tenant B

Tenant C

L2 and L3 segmentation RDMA fabric partitioning No cross-tenant path

Clusters

Coordinated capacity beyond one node.

Node

InfiniBand and RoCE v2 Slurm scheduling Shared storage across nodes Kubernetes-native orchestration Secure mTLS bridging

Developer experience

Every resource, programmable.

# Create a pod over the REST API
$ curl -X POST $BUDPOD_URL/v1/pods \
    -H "Authorization: Bearer $TOKEN" \
    -d '{"gpu":"A100","image":"pytorch"}'

$ budpod pods create --gpu A100 --image pytorch
$ budpod pods list
$ budpod pods stop pod_3f9a

from budpod import Client
 
client = Client()
pod = client.pods.create(gpu="A100", image="pytorch")
print(pod.status)

Full CRUD API CLI Language SDKs GitHub and CI/CD with rollback Real-time logs and metrics

Workloads

Every category of GPU workload.

Inference

Low-latency endpoints that scale with traffic.

Training & fine-tuning

Long jobs on high-memory GPUs with persistent storage.

Agents & automation

Systems that spin up fast and scale with load.

Media generation

Diffusion and video workflows on high-throughput GPUs.

Research & HPC

Rendering, simulation, and large data on self-service clusters.

A full self-service experience

From experiment to production, on your infrastructure.

Give your teams or customers the complete workflow in one account. No migrations between stages.

Spin up

A fully configured GPU environment on demand. Any supported accelerator, any site.

→

Build

Train models, fine-tune, and process data. Their containers, their frameworks, their code.

→

Deploy

Write a handler, push to serverless, and get a live inference endpoint. Auto-scaling, with no cost when idle.

→

Scale

From zero to many concurrent workers, automatically.

Built for two kinds of operator

One platform. Two ways to run it.

For enterprises

Self-service access to a shared GPU fleet

Give every internal team self-service access to a shared GPU fleet. Set quotas by department. Track utilisation and cost across the organisation. Stop teams from queuing for capacity and stop hardware from sitting idle.

For cloud & service providers

Launch a GPU-as-a-service business

Launch a GPU-as-a-service or compute-as-a-service business on your own hardware. Onboard tenants, isolate their workloads, meter their usage, and bill automatically. Bring a competitive cloud product to market without building the platform yourself.

Serverless, on hardware you own

Production inference without the warm-up tax.

Most serverless GPU platforms force a choice: pay for idle capacity, or accept cold-start latency. BudPod does neither, and it runs entirely on your infrastructure.

WorkersAutoscaling with demand

Autoscale automatically

From zero to many workers and back, tracking demand. No config files.

Fast cold starts

Warm-up engineering is handled for you, so endpoints respond without a warm-up delay.

Zero idle cost

An endpoint consumes no capacity when it is not running. Freed GPUs return to the shared pool.

Persistent network storage

Full AI pipelines with attached storage, and no egress charges inside your own network.

Multi-tenancy, metering, and control

Own the platform. Control every tenant.

Multi-tenancy is not a feature bolted on. It is the point.

Isolation by default

Every team or customer runs in a separate, secured tenant. Workloads, data, and networks stay apart.

Quotas and policy

Set limits on GPUs, spend, and priority per tenant. Enforce them automatically.

Metering and billing

Track usage down to the second. Generate chargeback for internal teams or invoices for paying customers.

Managed orchestration

BudPod queues and distributes work across your fleet. You do not build a scheduler.

Real-time observability

Live logs, metrics, and monitoring for operators and tenants alike. No custom frameworks required.

Run on any hardware, anywhere

Vendor-agnostic. Deployment-agnostic.

BudPod abstracts the hardware beneath it. Mix accelerators from different vendors and generations in one fleet. Add capacity without re-architecting.

GPU

HPU

TPU

NPU

CPU

A100

H100

H200

B200

MI300X

Gaudi 3

L40S

RTX 4090

TPU v5e

Cloud AI 100

Any accelerator

Modern and older GPUs, across vendors, in a single pool. Get more from the hardware you already own.

Any environment

Deploy on-premise, in colocation, in hybrid, or fully air-gapped. Your data and your models stay where your policy requires.

Sovereign by design

Run in disconnected and regulated environments with no dependency on a public cloud.

Enterprise-grade from day one

Built for scale. Secured for trust.

High availability

BudPod handles failovers and keeps workloads running when individual resources do not.

Secure by default

End-to-end data protection, tenant isolation, and controls that meet demanding compliance requirements.

Scale to thousands of GPUs

Grow the fleet as demand grows. The platform scales with your hardware, not against it.

Case studies

In production. At scale.

See how enterprises and cloud providers run compute on BudPod.

Case study

Enterprise

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Case study

Cloud provider

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Case study

Service provider

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Your infrastructure. Your compute service.

No hyperscaler tax. No lock-in. Own the platform and the hardware, and serve compute on your own terms.

Request a demo Talk to our team

Run your own compute cloud.

One platform. Your whole fleet.

Provision compute on demand

Serve across every site and region

Scale on autopilot

Five components. One control plane.

Pods

Serverless

Clusters

Hub

BudPod SDK

GPU compute from pure Python.

One GPU. Many isolated slices.

Isolation down to the fabric.

Coordinated capacity beyond one node.

Every resource, programmable.

Every category of GPU workload.

Inference

Training & fine-tuning

Agents & automation

Media generation

Research & HPC

From experiment to production, on your infrastructure.

One platform. Two ways to run it.

Self-service access to a shared GPU fleet

Launch a GPU-as-a-service business

Production inference without the warm-up tax.

Autoscale automatically

Fast cold starts

Zero idle cost

Persistent network storage

Own the platform. Control every tenant.

Isolation by default

Quotas and policy

Metering and billing

Managed orchestration

Real-time observability

Vendor-agnostic. Deployment-agnostic.

Any accelerator

Any environment

Sovereign by design

Built for scale. Secured for trust.

High availability

Secure by default

Scale to thousands of GPUs

In production. At scale.

Case study title

Case study title

Case study title

Your infrastructure. Your compute service.

Company

Product

Resources