Introduction

Welcome to Vega Cloud

The Vega Platform helps enterprises monitor, control, and optimize their multi-cloud resouce spending.

Enterprises often find their cloud costs escalating due to various factors like over-provisioning, underutilization of resources, or lack of visibility into the spending breakdown. The Vega Platform provides the platform to ingest, normalize, and curate your cost and usage data across AWS, Azure, GCP and more as well as people, FinOps experts in fact, to rationalize, prioritize, and sequence recommendations to unlock maximum savings.

How Information in Vega is Organized

The Vega Platform is flexible to fit the unique FinOps needs in an enterprise organization. At a high level, functionality is grouped into 3 tiers, which align to the FinOps Phases defined by the FinOps Foundation.

V Inform: Gain multi-cloud portfolio cost visibility with reporting, budgeting, and forecasting. Detect anomalies and build insights from your Cloud cost data.

V Optimize: Add Vega FinOps SMEs to engage with your teams to rationalize usage, layer in business context from executives, engineers, and finance, making prioritized action plans for optimization.

V Operate: Execute on optimization opportunities to realize savings using automation and workflow management with gamification to unlock more savings than before.

How to update image in steps

Currently the docs site is running in AppRunner in AWS 513971506177 account, below will be the process to update the AppRunner image

1. Get AWS access credentials to the aforementioned 513971506177 account in order to perform the following steps
2. Merge any changes desired into develop branch in Git and update your local Git to align with these commit updates (The remainder of this procedure assumes you're running from an up-to-date version of the develop branch in your local Git repo).
3. Using AWS ECR commands, push a new build with the following
   1. Retrieve an authentication token and authenticate your Docker client to your registry. Use the AWS CLI: aws ecr get-login-password --region us-west-2 | docker login --username AWS --password-stdin 513971506177.dkr.ecr.us-west-2.amazonaws.com Note: If you receive an error using the AWS CLI, make sure that you have the latest version of the AWS CLI and Docker installed.
   2. Build your Docker image using the following command. docker build -t vegadocs .
   3. After the build completes, tag your image so you can push the image to this repository: docker tag vegadocs:latest 513971506177.dkr.ecr.us-west-2.amazonaws.com/vegadocs:latest
   4. Run the following command to push this image to your newly created AWS repository: docker push 513971506177.dkr.ecr.us-west-2.amazonaws.com/vegadocs:latest
4. After confirming the ECR image has been properly updated, redeploy the AppRunner instance (arn:aws:apprunner:us-west-2:513971506177:service/vegadocs/5b17f0f5a0344b4a98b71a9a69808a00), navigate to the AppRunner image and click the "Deploy" button, since the AppRunner instance is pointing to the latest ECR image tag this should redeploy pointing to the new image.