Capabilities

Built for inference and compute spikes

Memory allocation, framework support, and container deployment, configured for model workloads.

Optimized inference pipelines

Processing pipelines tuned for low-latency model inference and API delivery, so responses return quickly under real request loads.

Scalable execution environments

Execution environments sized to absorb dynamic computational spikes, with memory allocated to the workloads that need it most.

Isolated containers

Each workload runs in its own container, keeping processes independent and protecting data boundaries between applications.

Architecture

Heavy processing runs apart from the public frontend

Background model work runs in its own environment, while your public web frontend stays lean and responsive. Requests reach the inference layer through defined APIs, so a surge in computation never slows the pages your visitors load.

This separation keeps long-running jobs from competing with user traffic and gives each layer its own resources to grow.