As a data platform engineer, you’re tasked with running complex workloads—Apache Spark jobs, AI/ML pipelines, batch ETL—across dynamic Kubernetes environments. Performance matters. Time spent tuning matters. And so does cost.
But if you’re still relying on manual resource tuning to optimize your workloads, you’re playing a losing game.
Sure, you can tweak CPU and memory requests by hand. You can comb through Prometheus metrics, look at job logs, estimate peaks. But Kubernetes doesn’t make that easy—and at scale, it’s practically impossible to stay ahead.
Here’s why manual tuning doesn’t work—and what actually does.
The Nature of Data Workloads Running on Kubernetes
Let’s get one thing straight: Kubernetes wasn’t originally built with data-intensive workloads in mind. It’s come a long way, but jobs like Spark, Presto, Ray, or TensorFlow training still introduce unique challenges:
- They’re bursty — CPU and memory usage can spike 10x for a few minutes, then idle.
- They’re variable — The same job can use vastly different resources depending on data size.
- They’re ephemeral — Jobs spin up and down quickly, making it hard to collect historical performance data.
Now try rightsizing that by hand. It’s like trying to tune an engine that rewires itself every time you turn the key. While developers or platform engineers try to manually manage levels of compute and memory needed to power their applications, too many factors exist for manual resource tuning to be effective.
Developers and Platform Engineers Trying to Allocate Resources
Figure 1: Developers try to reduce the allocated resource level, but it’s impossible to adjust in real time for the actual peaks and valleys of varying resource utilization.
The Limitations of Manual Tuning
1. You’re Tuning Based on Averages, Not Spikes
Most developers and data platform engineers tune based on average usage metrics. But with Spark, a shuffle stage or join can blow past “normal” usage for just a few minutes—and crash the job if you’re not prepared. So, what do they do? Overprovision. A lot.
2. You Waste Time and Still Miss the Mark
Even if you have time to analyze metrics from kube-state-metrics, Prometheus, or Spark UI logs, you’re still reacting after a failure or bottleneck. And those tuning changes you just made? They’re static. Next week’s data volume could make them irrelevant.
3. You’re Paying for Headroom You Rarely Use
To avoid OOM kills, throttling, or failed jobs, teams pad their resource requests. That leads to inflated CPU/memory reservations, poor bin packing, and inefficient autoscaler behavior. Kubernetes schedules based on requests, not usage—so overprovisioned jobs hog space and trigger unnecessary node scale-ups.
This is especially painful when you’re orchestrating hundreds of short-lived jobs across shared clusters.
You’re Not Alone: Most Data Teams Overprovision
In most Kubernetes environments we analyze, 50–70% of requested CPU and memory is never used. And data-intensive workloads such as AI/ML tend to be the worst offenders.
For example:
- A Spark executor requests 8Gi memory and 2 vCPU.
- Actual peak usage: 3Gi and 1 vCPU.
- Multiply that by 100 pods in a busy cluster—and you’ve got serious underutilization.
You’re not just wasting cloud dollars from overprovisioned resources—you’re constraining job throughput and reducing cluster efficiency.
So What’s the Fix?
Real-Time, Automated Resource Optimization
The answer isn’t more dashboards or YAML tweaking—it’s real-time, automated optimization that adapts to your workloads’ needs.
That’s where Pepperdata comes in.
How it Works:
Pepperdata Capacity Optimizer is a real-time, automated Kubernetes resource optimization solution that increases utilization levels by up to 80 percent and delivers an average 30 percent cost savings automatically, continuously, and in real time with no application code changes.
Developers are freed from manual tuning with an automated solution that pays for itself—saving them time to focus on revenue-generating innovation and helping companies reclaim resources to maximize ROI for their spend in the cloud.
Pepperdata Capacity Optimizer intelligently works with your scheduler and autoscaler in real time to run more workloads—resulting in increased utilization and lower cost.
- WORKLOADS ON NODES are scheduled based on real-time physical utilization via Pepperdata Extended Resources.
- PODS are then launched with Pepperdata Extended Resources and optimized resource requests.
- THE SCHEDULER can now make more accurate and efficient resource decisions with Pepperdata-provided data.
- THE AUTOSCALER can scale up more efficiently based on actual utilization.
You Get:
- Improved utilization by up to 80% for GPU, CPU, and memory with real-time resource optimization
- Time saved and higher job throughput without manual tuning, applying recommendations, and application code changes
- 30% or more average cost reduction for data workloads on Kubernetes
And unlike Vertical Pod Autoscaler, which uses trailing averages and reacts slowly, Pepperdata makes decisions based on real-time conditions, not assumptions.
Focus on Throughput, Not Tuning
As a developer or data platform engineer, your goal isn’t to guess CPU limits—it’s to deliver high-performing applications and reliable, cost-efficient infrastructure that scales.
Manual tuning won’t get you there. It’s reactive, brittle, and fundamentally flawed for fast-changing, data-intensive workloads. Automated optimization puts tuning on autopilot—so you can focus on enabling your teams and revenue-generating innovation, not firefighting misconfigurations.
Ready to see how much your workloads are overprovisioned—and what you can save with Pepperdata Capacity Optimizer?
