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GPU clouds & inference providers

AI & GPU Platforms

The multi-tenant platform around your models and accelerators: API, tenancy, routing, metering and billing, with day-2 operations included.

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From challenge to value

The platform is
the product.

Client work first, then applications we build for teams like yours.

01
From a client engagement

Inference as a service

The challenge

A model on an accelerator is not yet a product. Customers expect an API, keys, quotas and an invoice, and without a platform teams spend 12 to 18 months of senior engineering before the first one.

What we build

Infer8: an OpenAI-compatible API, tenants and per-customer keys, accelerator-aware routing, async jobs and one metered record per request, with day-2 operations included.

  • Every request metered and traceable end to end
  • Model and platform rollouts without dropped requests
  • Operated around the clock
Engineering considerations
  • Bursty image traffic beside long video jobs
  • Rollouts that drop no requests
  • Metering accurate enough to invoice
Delivery by design

Every request, one metered row.

The request path we run in production

  1. 01GatewayKeys, auth and policy
  2. 02RouterPer-model queues, free-slot gating
  3. 03WorkerThe model on its accelerator
  4. 04MeteringStage-by-stage timing for every request
Every request metered and traceable end to end.
02
From a client engagement

GPU as a service

The challenge

Accelerator capacity earns nothing until customers can reserve it, share it safely and pay for what they use.

What we build

A multi-tenant GPU service on Kubernetes: device plugins or DRA, partitioning and time-slicing, per-tenant quotas and usage metering, on NVIDIA or AMD Instinct.

  • GPU as a service for a $2B edge-AI company
  • Decentralised GPU as a service for a startup
  • Per-tenant usage you can invoice
Engineering considerations
  • Isolation between tenants on shared cards
  • Two scheduling models in one fleet
  • Capacity planning from real usage
Delivery by design

Capacity in. A service out.

How a GPU-as-a-service platform is built

  1. 01AcceleratorsNVIDIA, AMD, cloud GPUs
  2. 02SchedulingDevice plugin or DRA
  3. 03TenancyQuotas and reservations
  4. 04MeteringUsage per tenant
Capacity customers can reserve and pay for.
03
From a client engagement

Platform services beyond AI

The challenge

Node and database services have the same shape as inference: shared infrastructure, many tenants, upgrades that cannot interrupt anyone and usage to account for.

What we build

The same platform engineering, applied to node as a service for a blockchain company and database as a service for a German cloud provider: automated provisioning, isolation, verified backups and observability.

  • Node as a service for a blockchain company
  • Database as a service for a German cloud provider
  • A video platform for an a16z-backed company
Engineering considerations
  • Provisioning per tenant, as code
  • Backups proven by restore
  • Upgrades without interrupting tenants
Delivery by design

One pattern. Many services.

How we run platform services

  1. 01ProvisionAutomated, per tenant
  2. 02IsolateNetwork and access policy
  3. 03ProtectVerified backups
  4. 04ObserveDashboards and tested alerts
Applied to nodes, databases and video.

Your next step

Tell us what has
to run, and where.

Your users, workloads and constraints are where the design starts.

Book a 45‑minute discovery call