The State of AI Workloads in 2025
With AI models becoming increasingly complex, organizations face challenges balancing compute demand, data privacy, and cost efficiency. This post explores practical strategies for deploying AI workloads across hybrid environments.
Modern AI workloads require a balanced approach. By dynamically offloading specific inference tasks to cloud providers while maintaining sensitive data on-premises, organizations can optimize costs while maintaining low latency and compliance.
Three Optimization Strategies
Model Orchestration
Use Kubernetes-based AI orchestrators to dynamically route requests to the most efficient resource: on-prem for sensitive data, cloud for scale.
Data Pipelines
Build hybrid pipelines using secure transfer protocols like TLS 1.3 and zero-trust data encryption across multi-cloud environments.
Sample Terraform Configuration
// Sample configuration for hybrid model deployment
resource "aws_sagemaker_endpoint" "endpoint" {
endpoint_name = "model-orchestration"
deployment_config {
strategy = "blue_green"
}
}
resource "kubernetes_horizontal_pod_autoscaler" "k8s_scaling" {
name = "ai-infrastructure"
max_replicas = 20
min_replicas = 2
scale_target_ref {
api_version = "apps/v1"
kind = "Deployment"
name = "inference-service"
}
target_cpu_utilization_percentage = 75
}
This infrastructure-as-code example shows hybrid deployment configuration with automated scaling for both cloud and on-prem resources.
Security Considerations
- Implement mutual TLS between on-prem and cloud components
- Use hardware-level encryption between data centers
- Maintain data residency in regions with strict compliance frameworks
- Monitor with AI-powered anomaly detection tools
