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Calculadora de Recursos de Contenedores Docker

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APORTE

Proceso automático

PROD.

Lado cliente

Propiedad	Valor
CPU Request	-
CPU Limit	-
CPU Request (millicores)	-
CPU Limit (millicores)	-
Memory Request	-
Memory Limit	-
Total CPU (all containers)	-
Total Memory (all containers)	-

Docker Run Command

Docker --memory and --cpus flags

YAML de Kubernetes

Kubernetes resources.requests / limits

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Guía

Calculadora de Recursos de Contenedores Docker

Calculate optimal CPU and memory resource limits for Docker containers and Kubernetes pods based on your host capacity and workload type. Enter your available CPU cores and RAM, select a workload preset, and get ready-to-use Docker run flags, Kubernetes resource YAML, and Docker Compose snippets with copy buttons. Handles multi-container scenarios with configurable overcommit ratios and host OS reserve.

Cómo utilizar

Enter your host CPU cores and available RAM in GB, then select a workload type preset or define custom resource ratios. Set the number of containers, adjust the host OS reserve percentage, and configure the overcommit ratio. The calculator generates Docker run flags, Kubernetes resources YAML, and Docker Compose snippets for each container. Warnings appear if your total resource allocation exceeds host capacity.

Características

7 Workload Presets – Web Server, Application Server, Background Worker, Database, Cache, ML Inference, and Custom with recommended CPU and memory ratios
Multi-Container Support – Split resources across multiple containers with even distribution
Docker Run Flags – Ready-to-copy –memory, –memory-reservation, –cpus, and –cpu-shares flags
YAML de Kubernetes – Complete resources block with requests and limits in millicores and Mi/Gi
Docker Compose Snippet – deploy.resources block formatted for docker-compose.yml
Overcommit Ratio – Configure requests vs limits ratio (1.0x no overcommit to 2.0x aggressive)
Host OS Reserve – Adjustable percentage reserved for the host operating system (default 10-15%)
Capacity Warnings – Visual alerts when allocations exceed available host resources

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 Preguntas frecuentes

What is the difference between Docker memory limits and Kubernetes resource requests?

Docker memory limits (--memory flag) set a hard cap on container memory usage. If a container exceeds this limit, Docker kills it with an OOM error. Kubernetes has two settings: resource requests and resource limits. Requests are the guaranteed minimum resources the scheduler uses for pod placement. Limits are the maximum a container can use. Setting requests lower than limits allows overcommitting resources across pods, which improves cluster utilization but risks contention under load.
What are Kubernetes millicores and how do they work?

Kubernetes measures CPU in millicores where 1 CPU core equals 1000 millicores (1000m). This allows fine-grained CPU allocation. For example, 250m means 25% of one CPU core, 1500m means 1.5 cores. You can also specify CPU as decimal values like 0.25 or 1.5. Millicores are particularly useful for small containers that only need a fraction of a CPU core, and they map to Linux CFS (Completely Fair Scheduler) quota settings under the hood.
How much host resources should I reserve for the operating system?

A typical recommendation is to reserve 10-15% of CPU and memory for the host operating system, system services, and container runtime overhead. For production Kubernetes nodes, the kubelet has built-in flags (--system-reserved and --kube-reserved) to set aside resources. On a 16 GB host, reserving 10% means 1.6 GB for the OS, leaving 14.4 GB for containers. Under-reserving can lead to system instability, while over-reserving wastes capacity.
What is resource overcommitting and when should I use it?

Resource overcommitting means setting resource requests lower than limits, allowing the scheduler to pack more pods onto a node than the node could handle if all pods used their maximum resources simultaneously. An overcommit ratio of 1.5x means requests are set to 67% of limits. This works well for bursty workloads like web servers that rarely hit peak usage simultaneously. Avoid overcommitting for databases and stateful services where consistent performance is critical. Start with 1.0x (no overcommit) for production and increase cautiously.