In our previous blogs, we discussed two foundational pillars of AI infrastructure: BYOA (Bring Your Own Account) for secure, scalable deployments and Jobs Support for managing bursty and scheduled workloads.
Now, in Part 3, we’re diving into something that’s just as critical for AI workloads: cost controls and infrastructure insights. Because while scaling AI is powerful, scaling it without cost awareness is risky.
💡 Smarter Cost Controls for AI Infrastructure
One of the biggest drivers of unnecessary cloud spend—especially for growing AI teams—is over-provisioned infrastructure. Teams spin up oversized clusters, leave GPUs running idle, or bake in costly configurations just to “keep things moving.”
But this doesn’t scale. Especially when productivity tooling and test environments are manually configured through Terraform scripts or static clusters that are expensive to maintain and difficult to monitor.
Organizations waste an estimated 32% of their cloud spend due to overprovisioning and lack of visibility.¹ In the world of AI—where compute is expensive—that number compounds fast. 📊 Source: Flexera 2023 State of the Cloud Report
🛠 A Smarter Way to Deploy: Templates, Policies & Parametrization With Omnistrate, you can define your infrastructure and service architecture as templates, enforce policies, and expose safe parameters that your developers can tweak for:
- Internal testing
- Developer environments
- Canaries
Your internal teams get self-serve deployment tooling. These templates work seamlessly across clouds, regions, environments, tenant configurations, and infrastructure types—giving you a unified way to deploy while keeping costs under control.
🔄 Dynamic Autoscaling Options for smarter resource usage for AI workloads
AI workloads are often unpredictable—ranging from idle waiting periods to sudden compute bursts. To handle these efficiently, Omnistrate offers deep integration with Kubernetes-native and event-driven autoscaling mechanisms, helping you scale up when needed and scale down to save costs. Here’s how we support it:
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⚖️ Horizontal Pod Autoscaling (HPA) HPA is a built-in Kubernetes feature that automatically adjusts the number of pod replicas in a Deployment, ReplicaSet, or StatefulSet. It uses metrics like CPU or memory utilization, or even custom application metrics, to determine how many pods should be running at any given time—scaling out during load spikes and scaling in during quiet periods.
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📈 Vertical Pod Autoscaling (VPA) VPA automatically adjusts the CPU and memory resource requests/limits of individual containers based on historical usage patterns. Instead of increasing the number of pods, it ensures each pod is properly sized—preventing over-provisioning or underperformance without needing manual tuning.
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⚡ KEDA (Kubernetes-based Event-Driven Autoscaling) KEDA is an open-source project that enables event-driven autoscaling in Kubernetes. Unlike HPA, which relies on traditional metrics, KEDA listens to external event sources like message queues (Kafka, RabbitMQ), cloud services, or custom triggers. When an event backlog grows, KEDA scales your services up automatically—ideal for bursty or asynchronous workloads like AI inference pipelines, training jobs, or task processing queues.
By combining these autoscaling methods, Omnistrate ensures that your AI workloads always have the right resources—no more, no less. And when tied to cost-efficiency metrics, we can help you scale intelligently based not just on performance, but also on cost-to-value optimization.
💸 Additional Cost Controls
In addition, we’ve built several cost optimization controls that you can enable for your control plane:
- ✅ Spot instance support for bursty, non-critical workloads
- ✅ Reserved instance support for predictable usage
- ✅ Auto-stop for idle infrastructure, including dev/test clusters
- ✅ GPU time-slicing support to maximize usage of expensive resources
- ✅ Intelligent scheduling for heterogeneous workloads (containers + jobs)
- ✅ Monitoring & Auto-recovery to reduce MTTR and avoid costly downtime
📊 Cost Visibility
Controlling costs is only part of the story. You need to see where your money is going:
🔍 Fleet-wide cost breakdowns across plans, regions, environments
🔍 Utilization metrics by pod, cloud, region, or workload type
🔍 Per-tenant & subscription insights — with margins tracking built-in
🔍 Infra tagging support for clear categorization and reporting
🔍 Cost-based alerting to flag runaway scaling or budget thresholds
Final Thoughts
In AI infrastructure, speed and scale matter—but not at the expense of cost control and operational visibility.
At Omnistrate, we believe that infrastructure should be smart, self-optimizing, and developer-friendly. Our cost controls and insights are designed to help teams move fast without wasting resources, giving you the power to define guardrails, automate scaling, and understand exactly where every dollar goes.
Whether you're training models, running inference, or managing internal workloads across clouds and regions—Omnistrate gives you the tools to scale intelligently and efficiently.
👉 Ready to see it in action? Book a demo or dive into our docs to learn more.