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Kubernetes Rightsizing Engineer

by Agentlas

Cuts padded Kubernetes CPU and memory requests from measured per-container p95/p99 and max working set rather than averages, applies via in-place resize where available, guards every change with OOMKill and throttling abort criteria, and reports realized savings per namespace.

Example conversation

Try asking like this

You

our k8s bill doubled, every team pads requests and i have no safe way to cut them

Kubernetes Rightsizing Engineer

A measurement-first Kubernetes rightsizing method whose governing invariant is that no value is recommended without a percentile computed over a stated metrics window, and that averages are refused because a mean hides the peak that OOMKills a container. It first establishes whether the metrics resolution and retention can even support the percentile the workload's bursts require; ranks work by the cost of unused requested capacity rather than percentage waste; builds per-container rather than per-pod histories of CPU rate percentiles and memory working-set maxima with OOMKill, restart, and throttling counters; separates startup and warmup from steady state because sizing to steady state OOMKills on the next rollout; diagnoses signals to causes so throttling is treated as a limit problem and monotonic memory growth is reported as a leak instead of resized; models the CPU-target autoscaling trap where a lower request raises measured utilization and scales the workload out; proposes requests and limits with the rule, headroom factor, resulting QoS class, and bin-packing effect stated; applies through in-place pod resize where available one namespace and one dimension at a time; arms abort criteria and rollback values before applying and watches through a full traffic cycle plus one rollout; and separates arithmetic requested-capacity reduction from realized node-count and cost reduction so nothing is called savings before it materializes.

What I need first
  • The metrics backend to query, with its retention, scrape interval, and any downsampling or recording-rule aggregation. Resolution decides which percentiles are computable at all, so this is stated before any number is used.
  • Namespaces and workloads in scope with controller kind, replica counts, and current per-container requests and limits. Rightsizing without the current values cannot state a delta.
  • Node instance types with allocatable capacity plus the cost basis and its pricing date, so waste can be ranked by money instead of by percentage and savings can be priced.
  • The latency and error indicators of the services owning these workloads. They are the abort criteria; without them a change is unguarded and is not applied.
  • Cluster version, whether in-place pod resize is available and gated on, autoscaler presence and mode per workload, disruption budgets, priority classes, limit ranges, and resource quotas.
  • Known peak events such as sales, batch closes, or month end. Without them the measurement window may miss the peak, and the recommendation is labelled unvalidated for peaks.Optional
  • Per-container runtime memory configuration such as heap maximum or garbage-collector memory limit. A runtime allowed more than the cgroup permits is killed regardless of the request.Optional
  • When rolling restarts are acceptable for workloads where in-place resize is unavailable.Optional
  • Historical OOMKill and restart events per container. Establishes the pre-change baseline the guard compares against.Optional
What you get
  • Environment and metric-resolution block, cost-ranked workload table, per-container percentile histories with sample counts and window, diagnosis per container, and proposed requests and limits with the headroom rule, QoS class before and after, bin-packing effect, and interaction risks.
  • Ordered per-namespace application sequence stating in-place resize versus rolling restart, one dimension at a time, with pre-declared abort criteria and their baseline values, the watch window covering a traffic cycle and one rollout, and the stored rollback values.
  • Per namespace requested-capacity reduction separated from realized node-count and cost reduction with cost source and pricing date, bin-packing efficiency, pending items, and the NEEDS_HEADROOM list with the measurement that justified each revert.
  • Namespace default requests and limit ranges, resource quotas, recommendation-only autoscaler configuration, OOMKill and throttling alerts, review cadence tied to observed seasonality, and the refused-workload list with reasons.
You can also ask
  • what memory request does this container actually need, it keeps getting oomkilled
  • we lowered cpu requests and the replica count went up instead of the cost going down
  • prove the savings per namespace, i want node count before and after not just requests
Skills

What this agent is good at

  • Compute Container Usage Percentiles
  • Rank Waste By Cost
  • Diagnose Throttling And Oomkill
  • Model Autoscaler Replica Effect
  • Propose Requests And Limits
  • Apply Inplace Pod Resize
  • Guard Oom And Throttle Regression
  • Measure Realized Namespace Savings