Metrics - Hexel Studio

Metrics quantify how your workloads behave: latency, throughput, and resource consumption. They guide scaling decisions and surface problems before they become outages.

What you can measure

Metric area	Definition	Reported as
Latency	Time from when the platform receives a request to when it sends the response. Includes agent processing time.	Percentiles: p50, p95, p99
Throughput	Number of requests completed per second by the resource. Reflects actual work done, not just requests received.	req/sec
Resource usage	Actual CPU and memory consumption measured against the tier ceiling (e.g., 0.4 vCPU used of 0.5 vCPU `standard` tier).	Absolute value + percentage of tier capacity

Access

Metrics are available in the Console by resource and time range. Use them together with usage to relate performance to cost.

Production monitoring

For production workloads, watch these signals and alert on sustained regressions:

Signal	Watch for	Action
Latency (p95/p99)	Rising tail latency	Scale out, or move to a larger tier.
Throughput	Sustained drop with steady input	Check instance health and provider rate limits.
Resource usage	CPU/memory near tier ceiling	Move to a larger tier or add replicas.
Instance state	Transitions to `failed`	Inspect logs and redeploy.

Pair metric alerts with logs so an alert links directly to the activity that caused it.

Interpreting alerts

Three production patterns and what they indicate:

Pattern	What you see	What it means	Action
Latency spike + flat throughput	p95/p99 jumps but req/sec stays constant	A downstream dependency (tool call, Data Platform query) is slow. The agent is waiting, not overloaded.	Check tool execution times and Data Platform response latency.
Resource usage at ceiling + rising latency	CPU or memory ≥ 90% of tier capacity, latency climbing	The instance is compute-bound. Requests queue because there is no headroom.	Scale to a larger tier or add replicas. This will not self-resolve.
Throughput drops to zero + instance state `failed`	No requests processed, instance transitions to `failed`	The instance crashed or failed health checks. The platform stopped routing traffic.	Inspect logs for the crash cause. Redeploy after fixing the image.

Rate limit responses (429) include X-RateLimit-Limit, X-RateLimit-Remaining, and X-RateLimit-Reset headers. Monitor Remaining to anticipate throttling before it impacts users.

Common mistakes

Scaling on a single spike. Look at sustained trends, not one-off peaks.
Ignoring resource usage. High CPU against tier capacity signals a need to scale or change tier.

Best practices

Watch latency and resource usage before and after changes.
Use metrics to choose between scaling and moving to a larger tier.
Set expectations based on trends, not isolated samples.

Logs

Qualitative detail behind the numbers.

Scaling

Act on what metrics show.

Usage

Relate performance to cost.

Tiers & Limits

Capacity per tier.

​What you can measure

​Access

​Production monitoring

​Interpreting alerts

​Common mistakes

​Best practices

​Related pages

Logs

Scaling

Usage

Tiers & Limits

What you can measure

Access

Production monitoring

Interpreting alerts

Common mistakes

Best practices

Related pages