Technical Guide
Sizing compute for an AI governance proxy at peak LLM request volume
Size an inline AI governance proxy from peak concurrency and the measured cost of each enabled policy, not from average request rate. Model peak and burst traffic, measure per-request overhead at each enforcement depth, find the point where latency degrades on a single instance, then add headroom and failover capacity. Validate the result with load tests that reproduce your real payloads, streaming behavior, and policy set.
Sizing path for an inline governance proxy
The sizing process moves through five stages. Each stage is covered in the sections that follow.
| Stage | What to do |
|---|---|
| 1. Characterize workload | Peak and burst rate, concurrency, payload and token size, streaming share. |
| 2. Price each policy | Measure CPU, memory, network, and I/O cost per enforcement function. |
| 3. Derive capacity | Find per-instance throughput at your latency target. |
| 4. Add resilience | Plan headroom, autoscaling, and failure-mode capacity. |
| 5. Validate | Load test and monitor added p95 and p99 latency. |
Start with the workload inputs that drive proxy demand
Size from peak concurrency rather than average request rate. Characterize the workload first: peak and burst rate, concurrency, payload and token size, and streaming share.
Build the sizing model from peak and burst traffic
Model peak and burst traffic, then find the point where latency degrades on a single instance. Capacity is the per-instance throughput you can sustain at your latency target.
Which governance functions stress which resources
Measure per-request overhead at each enforcement depth. Size from the measured cost of each enabled policy, including CPU, memory, network, and I/O cost per enforcement function.
Plan headroom, autoscaling, and failure-mode capacity
Once per-instance capacity is known, add headroom and failover capacity. Plan headroom, autoscaling, and failure-mode capacity together.
Validate sizing with load testing before rollout
Validate the result with load tests that reproduce your real payloads, streaming behavior, and policy set. Load test and monitor added p95 and p99 latency.
Questions to ask vendors about performance and sizing
- What throughput and added p95 and p99 latency have been published, and under which payload sizes, streaming settings, concurrency, and enabled policies?
- How does measured overhead change as enforcement moves from authentication only to inspection, tool-call evaluation, and full audit logging?
- What sizing guidance exists per unit of request rate or concurrency, and which resource saturates first?
- How does the proxy behave under overload or component failure: fail-open, fail-closed, queuing, or load shedding?
- What HA and failover capacity is recommended?
- Which metrics, traces, and logs expose saturation, and are load-testing tools or reference test profiles available?
Plan enforcement capacity before it becomes a bottleneck
If you are designing inline runtime governance for AI agents and want to discuss sizing, failure modes, and validation approaches, speak with the Trussed AI team.
Talk to an Expert