Run your own compute cloud.
Bud Pod is the platform to provision, orchestrate, and serve GPU compute on infrastructure you own. Pool your fleet into a single service — on-demand pods, serverless endpoints, and multi-node clusters from one control plane. Built for enterprises and cloud providers.
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.
Run your own compute cloud.
The utilisation problem is a serving problem — hardware sits idle not because there's no demand, but because there's no service in front of it.
Pods
- Fully configured GPU environments, on demand
- Full control of container and runtime
- Any supported accelerator, any site
Serverless
- Autoscaling endpoints — zero idle cost
- No cold-start delay
- Freed GPUs return to the pool
Clusters
- InfiniBand / RoCE v2 interconnect
- Shared storage · Kubernetes-native
- Slurm scheduling · mTLS bridging
Hub
- Templates, ready to run
- Open-source models included
- Deployed in one click
Bud Pod SDK
- Decorate a function → live GPU endpoint
- No Dockerfile, no registry push
- Open source, on PyPI
Fractional GPU & isolation
- Isolated slices of a single GPU
- L2/L3 segmentation · RDMA fabric partitioning
- Isolation down to the fabric
Pods
Your compute service.
- Bud Pod is Layer 02 of the eight-layer Bud stack — the GPU cloud layer. The utilisation problem is a serving problem: rented clouds meter every hour and hold your data; owned hardware sits idle behind a queue of tickets. Bud Pod pools GPUs across five silicon vendors — NVIDIA, AMD, Intel, Qualcomm, Huawei — into one multi-tenant service, served on demand.
- A full self-service lifecycle: spin up (pods, clusters, hub templates) → build (their stack, not yours) → deploy (handler to live endpoint) → scale (zero to hundreds of workers and back to zero, freed GPUs returning to the pool).
- Six facets of the platform: Pods — fully configured GPU environments on demand; Serverless — autoscaling endpoints with zero idle cost and no cold-start delay; Clusters — multi-node with InfiniBand / RoCE v2, Slurm, shared storage, Kubernetes-native, mTLS bridging; Hub — a one-click catalogue of templates and open-source models; Bud Pod SDK — decorate a Python function into a live serverless GPU endpoint, no Dockerfile, no registry push, open source on PyPI; Fractional GPU and tenant isolation — isolated GPU slices with L2/L3 segmentation and RDMA fabric partitioning, no cross-tenant path.
- Operator controls make the fleet a business: isolation by default, quotas and policy per tenant, per-second metering and billing with chargeback or invoices, managed orchestration, real-time observability — for enterprises giving internal teams self-service access, and for cloud and service providers running GPU-as-a-service.
- The payoff: your infrastructure, your compute service. No hyperscaler tax. No lock-in.
Deployed on-premise, in colocation, and in sovereign, air-gapped environments.
Idle racks become your compute service.
GPU capacity is the scarcest resource in the enterprise — and the worst served. Rented clouds meter every hour and hold your data; the hardware in your own racks sits idle behind a queue of tickets. Bud Pod turns infrastructure you own into a cloud you run: pooled into one service, multi-tenant by design, metered to the second, and served on demand. No hyperscaler tax. No lock-in.
What makes the fleet a business.
Schedulers share a cluster; a compute cloud serves one. These six are the difference between hardware your teams queue for and a service they consume.
Serverless without the warm-up tax
Most serverless GPU platforms force a choice: pay for idle capacity, or accept cold-start latency. Bud Pod does neither — and it runs entirely on your infrastructure.
Isolation down to the fabric
Every team or customer runs in a separate, secured tenant — workloads, data, and networks kept apart with L2/L3 segmentation and RDMA fabric partitioning.
Metered to the second
Track usage down to the second, with quotas on GPUs, spend, and priority per tenant — chargeback for internal teams, invoices for paying customers.
Fractional GPUs
Accelerators partition into isolated fractional slices — tenants use only what they need, and the utilisation of every card goes up.
Vendor-agnostic pool
Mix accelerators from different vendors and generations in one fleet — from B200s to RTX 4090s — and add capacity without re-architecting.
Sovereign by design
Deploy on-premise, in colocation, hybrid, or fully air-gapped — your data and your models stay where your policy requires.
Five ways to consume the fleet.
Your users consume the pool five ways from one account — and the tenancy model underneath keeps every one of them isolated.
Pods
Fully configured GPU environments on demand, with full control of container and runtime — any supported accelerator, any site.
Serverless
Autoscaling API endpoints that cost nothing when idle — freed GPUs return to the shared pool.
Clusters
Multi-node GPU with high-speed interconnect, shared storage across nodes, Kubernetes-native orchestration, and secure mTLS bridging.
Hub
A one-click catalogue of templates and open-source models, ready to run on the fleet.
Bud Pod SDK
Decorate a Python function and deploy — a live serverless GPU endpoint with no Dockerfile and no registry push. Open source, on PyPI.
Fractional GPU & isolation
Isolated slices of a single GPU, with L2/L3 segmentation and RDMA fabric partitioning keeping tenants apart down to the fabric.
The full story, in depth.
The five components in full, the tenancy and isolation model, the serverless economics, and the hardware coverage.
Product Brief
Bud Pod Product Brief
The deep-dive product reference
The five components in detail, multi-tenant isolation down to the fabric, per-second metering and billing, the SDK, and the full hardware and environment matrix.
Read the product briefPlatform White Paper
The Enterprise AI Management Platform
Where Pod fits in the platform
The platform-level argument — why compute, training, serving, and governance belong on one plane, and the economics that follow.
Read the white paperPut your data on it.
The fastest way to see what an integrated AI operating system does for your enterprise is a proof-of-concept on your infrastructure, with your data.