Horizontal scaling
Separate request intake from workers so capacity can grow independently.
Inference scaling
Scale the harness with the workload.
Design queues, compute, and concurrency around demand. Treat execution capacity as a system-level concern.
Inference scalingConcept architecture
SELECTED STAGE / 01
API gateway
Define the inputs and access boundaries before execution begins.
WHAT WE DESIGN
A focused approach to inference scaling.
Backpressure
Bound queues and expose waiting states before demand overwhelms dependencies.
Resource scheduling
Match task requirements to capacity and define fair concurrency policies.
THE ENGAGEMENT
From diagnosis to a testable design.
A deployment topology, capacity assumptions, and a load-testing plan.
- 01
Map the workload
Review representative tasks, dependencies, and constraints.
- 02
Design the intervention
Define responsibilities, boundaries, and expected behavior.
- 03
Validate the result
Evaluate against the agreed quality and operational objectives.
Questions worth asking.
Does this require replacing our model or infrastructure?
We start with your current system. The design may improve the surrounding harness without changing the model. Any infrastructure change should follow from your requirements and evaluation results.
How do you judge whether an optimization works?
Use representative workloads and agreed acceptance criteria. Compare response quality, token consumption, latency, and operational behavior together. No universal improvement percentage is promised.
Can human review remain part of the process?
Yes. Approval gates, reviewer evidence, and explicit revision paths can be designed into the execution lifecycle.
LET’S WORK THROUGH IT
Bring us your workload.
Start with your requirements, data, and definition of a good result.