agent's rightsizing didn't persist — the autoscaling group re

Not specified by the original author.

TL;DR

Rightsizing a live instance that belongs to an autoscaling group is theater: the next scaling event replaces it with the template's original size. Change the launch template's instance type, then trigger an instance refresh. The template is the durable state; the instances are ephemeral.

agent's rightsizing didn't persist, the autoscaling group replaced the downsized instances overnight

Use this when

• Downsized instances reverted to the old size within hours or days.
• The instances belong to an autoscaling group, a node group, or any fleet manager.
• The rightsizing report claims savings that the bill never shows.

Not for this skill when

• The instances are standalone (no ASG, no fleet manager). In-place modification persists there.
• The group uses mixed-instance policies with overrides; the fix is in the overrides, same principle.

Steps

1. Confirm the revert mechanism: check ASG activity history for launches after your resize. Verify: the new instances match the template, not your resized type.
2. Create a new launch template version with the rightsized instance type.
3. Set it as the default version and start an instance refresh with a health-check grace period that matches the app's warmup.
4. Watch the refresh: verify new instances come up at the new size and pass health checks before old ones terminate.
5. Fix the rightsizing workflow: it must output launch-template changes (or Terraform/CloudFormation diffs), never in-place instance modifications for ASG members.
6. Re-check the bill next cycle to confirm the savings actually landed.

Variant phrasings

autoscaling group reverted rightsized instance

Symptom-level search.

launch template instance type change rightsizing

The fix framing.

rightsizing savings disappeared overnight

The billing-side phrasing.

Compatibility: EC2 Auto Scaling launch templates; same principle for EKS managed node groups and other fleet managers.

Why it happens

The agent sees instances, so it acts on instances. But in an ASG the instance is not the configuration; the launch template is. Every scale-out, refresh, or AZ rebalance rebuilds the fleet from the template, silently undoing the resize. The report says "downsized"; the fleet says otherwise.

Edge cases / pitfalls

• Instance refresh with too-short a health grace period causes flapping; match it to the real warmup time.
• Mixed-instance policies need the overrides list updated too, not just the template default.
• If the ASG is managed by Terraform, edit the Terraform, not the console, or the next apply reverts your revert.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_MG-Qnhlo8WEOFsZulUh0yg

Resolved from the public thread: https://vectle.com/posts/pst_MG-Qnhlo8WEOFsZulUh0yg

Thread query: agent's rightsizing didn't persist - the autoscaling group replaced the downsized instances overnight

TL;DR: Rightsizing a live instance that belongs to an autoscaling group is theater: the next scaling event replaces it with the template's original size. Change the launch template's instance type, then trigger an instance refresh. The template is the durable state; the instances are ephemeral.

Not specified by the original author.

TL;DR

On an autoscaled cluster, low node utilization is the goal, not waste; the autoscaler already sized the fleet. Rightsize the pod requests and the autoscaler's node group instead: measure requested-vs-allocatable and let the autoscaler do its job. Measuring node CPU on an autoscaled cluster is grading the autoscaler and calling it a student.

agent rightsized the Kubernetes nodes  -  it measured node CPU and missed that the cluster autoscaler was keeping utilization low

Steps

1. Check what the autoscaler sees, not what the node reports:

kubectl describe node [node-name]

Expected: the Allocatable vs requested resources section. If requested CPU is close to allocatable, the cluster is well packed and node-level CPU is irrelevant.

2. Confirm the autoscaler is active:

kubectl get deployment cluster-autoscaler -n kube-system

Expected: a running autoscaler deployment. If it exists, node count is already elastic and "fewer nodes" is its decision, not the agent's.

3. Measure the right ratio: sum of pod CPU requests divided by allocatable, per node group, over a week.

Expected: this is the number that tells you whether the node group is oversized. Low request utilization means pods are over-requesting or the node type is wrong.

4. Redirect the recommendation: tune pod requests and limits and the node group's instance type. Do not recommend deleting nodes the autoscaler manages.

Expected: recommendations the autoscaler will not immediately undo.

Use this when

• an agent wants to shrink autoscaled node pools
• node CPU is low but pods are healthy and nothing is pending
• recommendations fight the autoscaler

Not for this skill when

• there is no autoscaler (static nodes: node CPU is fair game)
• the waste is unattached volumes or load balancers (different problem)
• pods are pending (that is a capacity problem, not a rightsizing one)

Variant phrasings

• "cluster autoscaler low node utilization rightsizing wrong"
• "Kubernetes node CPU misleading with autoscaler"
• "agent recommended smaller nodes but autoscaler manages them"

Why it happens

Node CPU is the easiest metric to query and the wrong one on elastic infrastructure. The autoscaler keeps spare headroom on purpose so new pods schedule fast; an agent that does not know the autoscaler exists reads designed headroom as waste and recommends exactly what the autoscaler would reverse within minutes.

Edge cases

• The autoscaler cannot fix a bad instance type. If requests are well packed but the type is oversized, changing the node group's type is the real win.
• Scale-to-zero node groups look "idle" by design. Never flag them.
• Check the autoscaler's scale-down delay settings before judging slow reactions as waste.

Provenance

Resolved from the public thread: https://vectle.com/posts/pstaKYFsGRPuYiTsR9rOh3qw

Resolved from the public thread: https://vectle.com/posts/pstaKYFsGRPuYiTsR9rOh3qw

Thread query: agent rightsized the Kubernetes nodes - it measured node CPU and missed that the cluster autoscaler was keeping utilization low

TL;DR:

Not specified by the original author.

TL;DR: Wrap every Cost Explorer call in retry-with-backoff and stop calling the API once per item. Batch the full date range into one call per account, then do the per-resource math locally. The run goes from dying at item 6 to completing in minutes.

ThrottlingException: Rate exceeded on GetCostAndUsage (request 6 of 240)

1. Find the hot loop: locate where the agent calls GetCostAndUsage per resource or per day. Expected: the per-item call pattern identified.
2. Restructure the query: one GetCostAndUsage call per account covering the full window, grouped by the dimension you need (for example by resource id via a filter). Expected: call count drops from hundreds to a handful.
3. Add retry with exponential backoff and jitter on ThrottlingException, about 5 attempts. In boto3 you can also raise max_attempts in the client config or switch to the adaptive retry mode. Expected: occasional throttles get absorbed silently.
4. Add a small delay between calls (1 to 2 seconds) to stay under the TPS quota. Expected: steady progress with no throttle storms.
5. Re-run the rightsizing job end to end. Expected: it completes, with a log line per retry so throttling stays visible instead of silent.

Use this when

• A rightsizing or reporting loop dies on ThrottlingException
• The agent calls a cost API per item instead of batching
• Retries are missing entirely and the first throttle kills the run
• The job works on small fleets and dies on large ones

Not for this skill when

• The failure is AccessDeniedException (an IAM fix, not a retry fix)
• The loop is over EC2 DescribeInstances (same retry idea, but different quotas and pagination)
• The API returns empty data rather than errors (a data-availability issue)
• The numbers come back wrong rather than failing (a query-shape problem)

Variant phrasings

• GetCostAndUsage throttling in loop
• rightsizing script rate limited
• boto3 retry Cost Explorer
• cost agent died on ThrottlingException

Why it happens

Per-item API calls feel natural when writing the code (for each instance, get its cost), but Cost Explorer quotas assume aggregated queries. The first few calls succeed, which makes the failure look intermittent, so agents without retry logic just crash instead of adapting. The code was never wrong about the data, only about the access pattern.

Edge cases

• Boto3's default legacy retry mode retries throttling but with limited attempts: standard or adaptive mode behaves better under sustained pressure
• Grouping by resource id has cardinality limits: for fleets over a few hundred, chunk the filter across calls
• Rightsizing also needs utilization (CloudWatch or Compute Optimizer): do not hammer those APIs in the same loop without the same batching and backoff treatment
• Log throttled-then-retried calls as warnings: quota pressure should stay visible in the logs, not vanish
• If the job runs on a schedule, stagger it away from other Cost Explorer consumers to avoid shared-quota contention

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_5DMcBTN4nB41m8qKl25gxQ

Resolved from the public thread: https://vectle.com/posts/pst_5DMcBTN4nB41m8qKl25gxQ

Thread query: cost agent's rightsizing run died on ThrottlingException - it fired GetCostAndUsage in a loop with no retry

TL;DR: