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Google Cloud autoscaler

Paid#12 of 24 in Auto Scaling Software

Google Cloud autoscaler: A Google Cloud feature for scaling managed instance groups to match changing demand. Ranked #12 of 24 in Auto Scaling Software by our editors (7.9/10); pricing: Paid; best for google Cloud users scaling managed instance groups.

7.9/10Editor score

Visit Google Cloud

Google Cloud autoscaler7.9 Visit Google Cloud

At a glance

Editor score
7.9 / 10
Pricing
Paid
Best for
Google Cloud users scaling managed instance groups
Free plan
No
Scaling targets
Instances
Facts checked
24 Sep 2026

Where it wins
- Scales on CPU, load-balancing capacity, Monitoring metrics, or schedules
- Predictive autoscaling can add instances ahead of forecast demand
- Minimum, maximum, and scale-in controls shape instance changes
Where it doesn't
- Limited to managed instance groups running in Google Cloud
- VM resource usage is chargeable even though autoscaling policy setup is not
- Support channels are community and documentation

Our verdict on Google Cloud autoscaler

Google Cloud autoscaler adjusts the number of virtual machine instances in a managed instance group (MIG) as workload demand changes. It is aimed at teams running workloads on Compute Engine who want instance counts to respond to demand without manually changing them. The feature supports scaling both out and in, with configurable minimum and maximum instance counts and controls over scale-in behavior.

Scaling policies can use average CPU utilization, HTTP load-balancing serving capacity, Cloud Monitoring metrics, or schedules. Predictive autoscaling uses historical data to forecast load and can add instances ahead of anticipated demand. Cloud Monitoring and Cloud Load Balancing are the listed integrations, making those signals relevant to teams already using Google Cloud’s monitoring and load-balancing services. This is a focused choice for managed instance groups rather than a cross-cloud autoscaling tool or a general-purpose scaling system for other targets.

According to Google Cloud, there is no additional charge for configuring an autoscaling policy; charges apply to the VM resources used. That means the cost consideration is the capacity the policy provisions, not a separate autoscaler plan. The published support channels are community and documentation. Choose this feature if your workloads run in Google Cloud MIGs and you need policy-driven instance scaling, including predictive or scheduled behavior. Look elsewhere if you need autoscaling across multiple cloud providers or for targets other than instances in managed instance groups.

Your product? Our editors can test Google Cloud autoscaler in depth and publish a review, labelled as paid · from $149/mo →

Google Cloud autoscaler pricing

Plans PaidPaid No free plan — trial or paid only. Prices checked 24 Sep 2026.

See plans on cloud.google.com

Google Cloud autoscaler fact sheet

Free plan	No
Paid from	Not verified
Scaling targets	Instances
Scaling triggers	Cpu
Cloud support	Single-cloud
Scale direction	Both
Deployment	Cloud
Deployment	Cloud
Platforms	Web
Support	Community, Docs
Built for	Small business, Mid-market, Enterprise (editorial estimate)
Integrations	2 integrations: Cloud Monitoring, Cloud Load Balancing
Pricing	Paid (source)
Website	cloud.google.com
Facts checked	24 Sep 2026

Google Cloud autoscaler integrations

Google Cloud autoscaler lists 2 integrations on its own site.

Cloud Monitoring
Cloud Load Balancing

Alternatives to Google Cloud autoscaler

See all Google Cloud autoscaler alternatives →

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Featured on iTechGuides

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Reviewed by iTechGuides Editors · Editorial team · Updated Oct 2026

Advertiser disclosure: iTechGuides is reader-supported. Vendors can pay for top positions in our rankings and for a place on other products' pages, and we may earn a commission when you click some links. Bought places stand at the number bought; this page and our disclosure say so. Paying vendors may get a fuller, more detailed review from our editors, and that review can change their score. How we rank.

Last updated 7 Oct 2026 · How we research and update