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Find over-provisioned Kubernetes workloads and generate safer right-sizing recommendations.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "finops-copilot" yet — see the docs or source repo.
Scan the current Kubernetes cluster workloads, identify objects with clearly over-provisioned CPU or memory, and rank them by waste level.
A list of potentially over-provisioned workloads, highlighting those with higher resource waste.
Generate right-sizing recommendations for deployments in the payments namespace, including suggested CPU and memory changes, while keeping the default dry-run mode.
Suggested resource settings for each workload without directly changing live objects.
Before applying any Kubernetes resource optimization, run a dry-run with audit logging to check which changes would be limited by safety guardrails.
A simulated execution result, notes on changes restricted by guardrails, and an auditable action record.
DevOps or platform teams can use it to scan cluster workloads and locate over-provisioned resources. This helps identify optimization opportunities faster and reduce unnecessary cloud spend.
When preparing to adjust container resource sizing, teams can evaluate impact through the default dry-run and safety guardrails. With audit logging, they can review recommendations without immediately changing production.
It is an MCP server for Kubernetes FinOps that finds over-provisioned workloads and proposes right-sizing recommendations. It also provides default dry-run behavior, safety guardrails, and audit logging.
Based on the provided description, it runs in dry-run mode by default, so it simulates changes before applying them. See the source repository for the exact way changes are applied.
The provided material does not include installation steps, runtime dependencies, or key requirements. See the source repository for those details.
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