← AI Recommendation IndexOpen Data · Rolling 4-week window

AWS vs Kubernetes

A fact-by-fact comparison of AWS and Kubernetes — sourced and dated — plus the part no generic comparison shows: which one AI assistants actually recommend more, engine by engine.

Measured by Orbatormethodology owner Henrik Tellewindow ends Sep 5, 2026

What AI recommends — by engine

Recommendation share = the % of sampled AI answers, over a 4-week rolling window, that recommend each product. Higher is better.

CLAUDE

AWS

2.4%

Kubernetes

GEMINI

AWS

4.1%

Kubernetes

GROK

AWS

4.4%

Kubernetes

7.7%

CHATGPT

AWS

2.1%

Kubernetes

3.4%

PERPLEXITY

AWS

1.9%

Kubernetes

2.1%

The facts — sourced & dated

AWS

aws.amazon.com

Features

pay-as-you-go approach for pricingSource · Verified Sep 5, 2026
Savings PlansSource · Verified Sep 5, 2026
volume based discountsSource · Verified Sep 5, 2026
Private PricingSource · Verified Sep 5, 2026
AWS Pricing CalculatorSource · Verified Sep 5, 2026
over 240 comprehensive servicesSource · Verified Sep 5, 2026

Kubernetes

kubernetes.io

Features

Automated rollouts and rollbacksSource · Verified Sep 5, 2026
Service discovery and load balancingSource · Verified Sep 5, 2026
Storage orchestrationSource · Verified Sep 5, 2026
Secret and configuration managementSource · Verified Sep 5, 2026
Automatic bin packingSource · Verified Sep 5, 2026
Batch executionSource · Verified Sep 5, 2026
Self-healingSource · Verified Sep 5, 2026
Horizontal scalingSource · Verified Sep 5, 2026
Vertical scalingSource · Verified Sep 5, 2026
IPv4/IPv6 dual-stackSource · Verified Sep 5, 2026

AWS vs Kubernetes — FAQ

Does AI recommend AWS or Kubernetes more?

Kubernetes is recommended more often: it appears in 3.1% of sampled AI answers versus 2.8% for AWS, measured across the live AI engines over a rolling 4-week window.

Which AI engines were used to compare AWS and Kubernetes?

Recommendation share is measured on CLAUDE, GEMINI, GROK, CHATGPT, PERPLEXITY using neutral, buyer-shaped prompts (no product names), aggregated over a 4-week rolling window. See the methodology for prompt design and sample sizes.

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Recommendation share and entity resolution follow the Index methodology. Facts are sourced from each vendor's own pages and re-verified periodically. Customer status does not affect measurement. Free to use with attribution to orbator.io.

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