Alibaba Auto Scaling Ratings (September 2025)

Alibaba Auto Scaling

Auto Scaling: Automatically Adjusts Computing Resources - Alibaba Cloud

https://www.alibabaclou.com/en/product/auto-scaling/

This page breaks down Alibaba Auto Scaling ratings from all verified review sources to give you a clear, unbiased view of what real users think in September 2025.
We’ve aggregated the latest scores from all major platforms like Trustpilot, G2, Capterra and many more to help you understand Alibaba Auto Scaling’s overall reputation. This data-driven overview will show you exactly how Alibaba Auto Scaling is rated - including how it compares to competitors.

Alibaba Auto Scaling's Overall Rating Summary

  • Average Rating:1.7/5
  • Total Reviews:83 reviews
  • Sources:G2, Trustpilot
  • Last updated:September 1, 2025

Alibaba Auto Scaling Ratings by Source

PlatformRatingReviewsLast Update
G2 logoG2
3.8/5
2+ reviews01.09.25
Trustpilot logoTrustpilot
1.6/5
81+ reviews05.08.25

What is Alibaba Auto Scaling?

Alibaba Cloud's Auto Scaling service automatically adjusts computing resources based on user requests, adding ECS instances during peak periods and releasing them when demand decreases. Key features include intelligent scheduling, automatic health monitoring, and configuration of load balancers and RDS whitelists. The service supports various scaling modes such as scheduled, dynamic, and fixed, providing flexibility and cost efficiency.

🤔 Alibaba Auto Scaling vs Competitors: How Do Ratings Compare?

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Frequently Asked Questions

How many stars is Alibaba Auto Scaling rated?

Alibaba Auto Scaling has an average star rating of 1.7 out of 5 stars based on average of 83 reviews as of September 2025.

Where does the rating data come from?

The ratings are pulled from Alibaba Auto Scaling public reviews pages on platforms: G2, Trustpilot. We update these ratings monthly to ensure they reflect the most current customer feedback.

How overall average rating for Alibaba Auto Scaling is calculated?

The overall average rating is calculated by taking the weighted average of all ratings, using the total reviews count from each platform as the weight.