MEETCOMPUTE.

The execution layer for production AI.

Choose the model you want to run. Compute transforms it into a production-ready service by orchestrating the entire execution lifecycle, automatically managing the runtime, networking, security, and platform services behind every deployment.

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Infrastructure without complexity.

No providers to compare. No runtimes to configure. No infrastructure to operate. Compute brings everything together behind a single execution layer.

See how it works.

01 —

Choose your model

Pick any supported open-weight model from Hugging Face. Compute validates its requirements and prepares it for deployment.

02 —

Compute handles the rest

Infrastructure, runtime, networking, storage, and security are automatically provisioned and managed for every deployment.

03 —

Build your application

Access your deployment through an OpenAI-compatible API and start building while Compute continues to operate everything behind it.

MORETHAN ANENDPOINT.

Every production service, already connected.

Compute combines inference, networking, security, routing, and platform services into a single execution environment designed to grow with your AI deployments.

Pay for capacity.

Predictable pricing for AI workloads.

Compute charges for the capacity your deployment uses, not for every request, token, or generated image. Scale capacity up or down as demand changes while keeping your pricing predictable and your infrastructure costs under control.

Explore pricing

Runtime load

71%

DeepSeek

What Compute already includes

Model deployment

Pick a model and let Compute validate runtime requirements and prepare capacity for serving.

Multi-provider routing

The scheduler selects capacity across available providers for cost, availability, and latency.

Persistent storage

Mount persistent disks across workloads, share state between launches, and keep it ready for every run.

OpenAI-compatible API

Expose deployed models behind the standard OpenAI contract so existing clients keep working.

Scheduled operations

Use cron-based start and stop rules so workloads run when needed and stay off when they do not.

Security controls

Apply regional controls, verified runtime checks, and private-by-default operations for production workloads.

Observability

See processed tokens, CPU, RAM, GPU, and token throughput in one place to optimise usage and troubleshoot workloads.

Serverless

Start models on demand and stop them after a configurable idle period to reduce wasted capacity for bursty workloads.

Python

client = OpenAI(
    base_url="https://api.qdiv0.com/v1",
    api_key="..."
)

response = client.responses.create(
    model="moonshotai/Kimi-K3",
    input="..."
)

FROM COMPUTE TO CODE.

Use the SDK you already know.

Compute changes how your models run, not how your applications connect. Every deployment is exposed through an OpenAI-compatible API, so your SDKs, clients, and workflows keep working unchanged while you switch models and scale without rewriting your code.

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