Helping a fast-growing AdTech platform Boostr to gain full control over cloud resources

A successful startup, Boostr grew quickly and saw a great deal of complexity creeping into its operations, with cloud expenses skyrocketing as a result. The company was looking for a solution that would reduce costs by resolving that complexity and allow the team to manage resources better. By implementing CAST AI, Boostr runs applications in an environment that is easy to manage and fully visible, offering many opportunities for eliminating cloud waste and cutting costs.

Company size

~100 employees

Industry

AdTech

Headquarters

New York, NY

Cloud services used

GKE

The cloud is a must-have for every SaaS company

Boostr developed a platform that brings unified visibility to support sales, ad operations, and finance teams, helping them to scale omnichannel ad revenue profitability thanks to custom workflows, actionable insights, and accurate forecasting.

We started off as a small startup with an application that was created very quickly for fast validation on the market. The cloud was essential for making that happen, so we first ran our application on Heroku and later migrated to Google Cloud Platform.

Ryan Upton

Architect at boostr

But the cloud comes at a price

As the company grew, the demand for the so-called sandbox environments created for every new feature increased dramatically, leading to a massive increase in cloud bills. The available resources weren’t being used to the fullest and servers were kept running during the low-demand time (outside of working hours and on weekends).

Overprovisioning of our development and staging environments, constantly spinning up new instances to build new features and having product owners validate them – managing all of that became very challenging. A lot of complexity crept into our process over time and – as a result – costs started increasing drastically. We were looking for a strategy to help us reign in that complexity and manage cloud resources better to control these costs.

Ryan Upton

Architect at boostr

Boostr started thinking about reducing the cloud bill, considering the future scalability of both the team and the product itself. Migrating to Kubernetes seemed to be the right solution but the company lacked the required experience with the container orchestrator. This is where CAST AI came in.

Solution: Migration to managed Kubernetes with CAST AI

Since Boostr used virtual machines, its team had to separate all the elements of the development environment into separate containers to make them manageable via Kubernetes.

The CAST AI team assisted Boostr in separating the code to create a deployment system that was easy to manage and enabled a streamlined CI/CD process. The company started using the CAST AI platform to smoothly deploy the sandbox environments.

The CAST AI team did a tremendous job during the migration by taking the time to understand our environments and making valuable recommendations about how we could run things. Thanks to this, the migration process went very smoothly. It also helped us to understand our deployment process better. We mapped everything needed for an environment to run and can now manage our code and development system with full confidence.

Melina Silva de Souza

DevOps Specialist at Zazmic Inc, partner of Boostr

Result I: Gaining full control over cloud resources

Boostr product teams no longer deal with overloaded sandbox environments. If a developer wants to build a new feature, they don’t need to ask a DevOps engineer to create a sandbox environment and give them access to the server.

The developer can use Jenkins to create it automatically. There’s no need to wait for DevOps to respond or get approval for additional resources because CAST AI takes care of the node provisioning automatically. All the pre-production sandboxes are deployed with CAST AI, which allows the team to burst above the set number (40 sandboxes) whenever necessary.

When sandboxes are not needed anymore, they can be quickly destroyed to save capacity and costs. CAST AI also allows pausing and resuming clusters when nobody is working on sandboxes, saving additional costs without any added complexity.

As we started the migration, CAST AI showed us Kubernetes best practices and helped us to implement special cost monitoring features so that we could limit the use of containers and assess the real utilization of the entire cluster. Based on this experience, we’re planning our next steps to becoming more efficient in cost optimization.

Denys Kozlov

Devops Engineer at Zazmic Inc, partner of Boostr

Thanks to CAST AI, Boostr reduced its database restore time for new slots from 30-60 minutes to just under 30 seconds with the help of Kubernetes PVC snapshots across namespaces.

The setup didn’t disrupt the work of product teams – the implementation of CAST AI was a non-invasive process as it doesn’t add any complexity into the application lifecycle or translate into any extra effort.

Working directly with CAST AI has been instrumental not only in getting things set up but also continuously reinforcing the positive vibe around the new Kubernetes implementation. CAST AI gave us better visibility into our cloud resources and left us feeling in control

Ryan Upton

Architect at boostr

Result II: Automated cost optimization

As Boostr prepares to enter the next stage of automated cost optimization with CAST AI, the new setup has already proven to be more efficient. Since clusters have a fixed number of nodes, the team knows how many sandboxes can be created – thanks to Kubernetes, developers can create more than 2x sandboxes than previously for the same price.

CAST AI is a great solution for anyone that has a fairly sprawling infrastructure that is growing a little out of control and becoming difficult to manage. Turning to containers and running applications in a much more consistent fashion – without ever forgetting about the costs – is a smart move. Looking at the expertise of CAST AI in Kubernetes, we are confident that these cost savings are going to be realized.

Ryan Upton

Architect at boostr

CAST AI features used

  • Spot instance automation
  • Real-time autoscaling
  • Instant Rebalancing
  • Full cost visibility

You’re underway to simplify Kubernetes

  • No more complexity of Kubernetes management
  • 50%+ lower cloud costs without repetitive tasks
  • Predictable cloud bills and performance at all times

4.1/5 – Average rating

5/5 – Average rating

CAST AI is a leader in Cloud Cost Management on G2 CAST AI is a leader in Cloud Cost Management on G2 CAST AI is a leader in Cloud Cost Management on G2

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