Compute, orchestrated.
Infrastructure that keeps up with the idea.
Deploy inference, training, and agent workloads on elastic compute that feels as immediate as local development.
runset / execution-7f2a
runset deploy inference.py --gpu h100Execution log
- 00:00.021
Container image resolved
- 00:00.116
H100 worker allocated
- 00:00.284
Runtime ready in us-central-1
- 00:00.842
First token streamed
Workload is executing on an H100 GPU with live region, queue, and latency monitoring.
The runtime
One runtime from first prompt to production.
01 / DEFINE
Environment as code
Package dependencies, hardware, and regions beside the function that runs them.
import runset
@runset.service(
image="python:3.12",
gpu="L40S",
regions=["ord", "iad"],
)
def inference(request):
return model.generate(request.prompt)02 / START
Sub-second starts
Restore warm snapshots close to demand and begin useful work in milliseconds.
03 / SCALE
Elastic accelerators
Match each request to the right accelerator without managing a standing fleet.
04 / TRACE
Observability included
Follow a request across queues, workers, and model calls from one trace.
Workloads
One control plane. Three execution modes.
Inference
Serve every model at the speed of demand.
Route latency-sensitive model traffic onto warm, right-sized accelerators without provisioning a fleet.
- Continuous request batching
- Region-aware routing
- OpenAI-compatible endpoints
- Scale to zero between bursts
Infrastructure
Capacity where the workload lands.
Run against the hardware your system needs, close to the data and people it serves. Runset routes each request across a live, continuously measured fleet.
Security
Boundaries built into every run.
Security controls stay close to execution, so protection is part of the path to production instead of a gate added afterward.
Every workload runs inside an ephemeral boundary with its own network policy and encrypted volume.
Production proof
Measured in production, not in promises.
Teams use Runset to replace provisioning delays with measured, repeatable execution across the systems they already operate.
We moved a multi-stage inference pipeline from a fragile queue to one deployable unit. The first production run landed before our migration review ended.
Runset gave our team one operational picture for GPUs, regions, and releases. We spend our time improving the model now, not reconciling infrastructure.