Is there a way to estimate billing for a GKE app install?

[danielrosehill]

Hi guys,

I am looking into deploying Apache Sunset which is available through GKE as a preconfigured Kubernetes 'app' (link).

To see if it might be able to fit within my budget it would be enormously helpful (make that essential) to get some price points around how much an ongoing deployment might cost.

The app itself has no ongoing costs (for licensing etc). But obviously the resources the workload consumes do.

Some questions I would be very interested in knowing to assess feasibility would be:

• What's a minimum possible monthly spend to run this workload as an MVP / with minimum system configuration?
• What would be an expected monthly spend for a particular usage and traffic volume?
• What configurations can be changed (if any) to alter the likely spend?

Can any of these things be deduced using self-service / discovery options (and more preferably, all of them)?

If not, let me make a feature request that some kind of predictability be considered for deploying GKE workloads so that companies, individuals, and non-profit users can assess the feasibility of running a particular deployment in advance (rather than finding out retrospectively that an app is beyond budget or attempting to constrain workloads in a futile attempt to "make it affordable" etc).

Prevention is better than cure and ... surely getting a ballpark figure is necessary for those looking to deploy production workloads.

TIA!

[Willbin]

Hello @danielrosehill,

Welcome to Google Cloud Community!

To estimate the cost of deploying Apache Sunset on Google Kubernetes Engine (GKE), several factors need to be considered based on the pricing models:

• Minimum possible monthly spend for MVP
• Expected monthly spend for specific usage
• Configurations to Alter likely spend
• Self-service options for cost prediction

By leveraging the pricing details for GKE, you can estimate the cost of deploying Apache Sunset by considering factors like the mode of operation (Autopilot or Standard), potential discounts through CUDs, and utilizing cost optimization tools for efficient resource management. This approach allows for better cost predictability and feasibility assessment before deploying workloads on GKE.

See this document for additional info