TCP #61: The Real Cost-Benefit Analysis: MLOps on EKS vs. SageMaker

Decision frameworks and implementation strategies you won't find in AWS documentation

You can also read my newsletters from the Substack mobile app and be notified when a new issue is available.

---

Become a Founding Member

As a founding member, you will receive:

• Everything included in paid subscriber benefits + exclusive toolkits and templates.

• High-quality content from my 11+ years of industry experience, where I solve specific business problems in the real world using AWS Cloud. Learn from my actionable insights, strategies, and decision-making process.

• Quarterly report on emerging trends, AWS updates, and cloud innovations with strategic insights.

• Public recognition in the newsletter under the “Founding Member Spotlight” section.

• Early access to deep dives, case studies, and special reports before they’re released to paid subscribers.

---

After rebuilding our machine learning platform three times in 18 months, we've gained hard-earned clarity on one of the most consequential infrastructure decisions ML teams face: Amazon SageMaker vs. Kubernetes (EKS).

The headline numbers are attention-grabbing: EKS saved us $237,000 annually compared to SageMaker, but adequately implementing it cost us 4 months of engineering time.

But the real story is far more nuanced.

In this deep dive, I'll share the decision frameworks, implementation details, and hidden cost factors that AWS solutions architects won't tell you about.

Source: Unsplash

---

True Economics: Beyond Instance Pricing

When we initially compared SageMaker to EKS, the cost differential appeared straightforward: SageMaker instances run 2.3-3.8x more expensive than equivalent EC2 instances in an EKS cluster.

However, this simple comparison misses crucial factors that can completely invert the economic equation.

The Complete Cost Analysis

Here's our actual monthly cost breakdown for a team of 4 data scientists: