DeepCost
Demo
K8s
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DC

Native Kubernetes Integration

Deploy the DeepCost operator directly in your Kubernetes cluster for automated cost optimization with custom resources and policy-driven management.

Native Operator

Kubernetes-native operator that runs in your cluster

Custom Resources

CRDs for declarative cost optimization policies

Automated Optimization

Continuous optimization without manual intervention

Safe Operations

Built-in safety checks and rollback capabilities

Quick Installation

1

Install Operator

kubectl apply -f https://deploy.deepcost.ai/operator.yaml

Deploy the DeepCost operator to your cluster

2

Create Secret

kubectl create secret generic deepcost-config --from-literal=api-key=your-api-key

Configure your DeepCost API credentials

3

Apply Policy

kubectl apply -f cost-policy.yaml

Create your first cost optimization policy

4

Monitor Results

kubectl get costpolicies

Watch automated optimization in action

Custom Resource Definitions

CostPolicy

Define cost optimization rules and constraints

apiVersion: cost.deepcost.ai/v1
kind: CostPolicy
metadata:
  name: webapp-optimization
spec:
  selector:
    matchLabels:
      app: webapp
  optimization:
    rightsizing: enabled
    spotInstances: preferred
    autoscaling:
      minReplicas: 2
      maxReplicas: 20
      targetCPU: 70
  budget:
    monthly: "$5000"
    alerts:
      - threshold: 80%
        action: notify
      - threshold: 95%
        action: scale-down

CostAlert

Configure budget alerts and notifications

apiVersion: cost.deepcost.ai/v1
kind: CostAlert
metadata:
  name: budget-alert
spec:
  budget: "$10000"
  period: monthly
  thresholds:
    - percentage: 50
      severity: info
    - percentage: 80
      severity: warning
    - percentage: 95
      severity: critical
  notifications:
    slack: "#cost-alerts"
    email: ["team@company.com"]

SpotPolicy

Manage spot instance usage and failover

apiVersion: cost.deepcost.ai/v1
kind: SpotPolicy
metadata:
  name: spot-optimization
spec:
  nodeGroups:
    - name: workers
      spotPercentage: 80
      onDemandBase: 2
      fallbackStrategy: immediate
  workloads:
    - selector:
        app: batch-jobs
      spotTolerance: high
    - selector:
        app: web-frontend
      spotTolerance: medium

Optimization Capabilities

Pod Resource Optimization

Automatically optimize CPU and memory requests/limits

Historical usage analysis
Right-sizing recommendations
Gradual optimization rollout
Performance monitoring

Horizontal Pod Autoscaling

Intelligent HPA configuration based on multiple metrics

Multi-metric scaling
Custom metrics support
Predictive scaling
Scale-down optimization

Node Pool Management

Optimize node pool configuration and instance types

Instance type recommendations
Node pool rightsizing
Multi-zone optimization
Cost-performance balance

Spot Instance Integration

Intelligent spot instance usage with automatic failover

Spot price monitoring
Automatic failover
Workload spot tolerance
Mixed instance policies

Advanced Kubernetes Features

Policy as Code

Define cost optimization policies using YAML and manage them through GitOps workflows.

Cluster Autoscaling

Intelligent cluster autoscaling that considers both cost and performance requirements.

Safe Operations

Built-in safety mechanisms, rollback capabilities, and performance monitoring.

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