Customer Spotlight: Minga
Minga is the campus management platform K–12 schools use for digital hall passes, tardy management, and rewards for positive behavior (PBIS). It is trusted by over 1,500 schools in 600 districts across 47 states. The company has over 100 employees and recently secured a $65M investment from Riverwood Capital to continue its growth trajectory of doubling nearly every year.
Like most modern engineering teams, Minga's developers have leaned heavily into AI-assisted development. AI tools are in daily use for application code, and the team has been able to ship features faster than ever. But infrastructure couldn't move at the same pace.
Before Control Plane, Minga ran on Google Kubernetes Engine, with container-based Node.js services on the back end, Angular on the front, and MySQL on Cloud SQL. Matt Zytaruk, Minga's VP of Technology, maintained the cluster with the help of two other engineers — but it was becoming more and more of a burden. No matter how fast the team could write and ship new code, getting it deployed and keeping it reliably available on GKE slowed everything down.
GKE is service-oriented, not solution-oriented. Standing up a single workload requires touching many different places: the load balancer, the ingress, the SSL certificate, cert-manager, and the security mounts. The cluster's actual state drifted from what was committed in Git, and hiring help proved difficult — anyone Minga brought on needed months in the GKE world before they could be useful.
“Whenever you want to build something, you have to go to five different places in GCP. If you're only doing it occasionally, it's confusing and slow.”
Today Minga runs its workloads on Control Plane in GCP US-West while connecting to Cloud SQL, Redis, an SFTP server and the rest of its GCP-native services exactly where they were, inside a VPC accessed using Control Plane's Cloud Wormhole technology. Underneath, Control Plane runs on Kubernetes, but Minga doesn't have to manage Kubernetes directly. Cluster upgrades, kubelet compatibility, and node-pool surge settings are handled by the platform.
AI-Native Cloud for AI-Native Teams
AI-assisted development now extends into the infrastructure layer. Control Plane's MCP server is hooked into the SRE workflow, so engineers can diagnose issues and generate the changes they need in the same flow. Application engineers self-serve secrets and environment variables for their own workloads.
“It's literally three or four clicks to get a workload running in Control Plane.”
Cloud Costs Conforming to the School Day (and Year)
Minga's traffic profile is brutal — quiet at 5am, peaking at 11am, with seasonal swings from 5% of normal in summer to a new annual baseline every August.
On GKE, autoscaling required hand-tuning the team didn't have time for, so they over-provisioned, keeping a floor of 10 pods overnight.
On Control Plane, Minga scales aggressively — through 25, 40, 60, and 80 pods as load comes in, dropping to just 3 overnight. And thanks to Control Plane's Capacity AI, the team isn't trading availability or performance for the discount.
Choosing the Right Cloud for the Job
Some products Minga plans to build would benefit from AWS-only services. The next phase of growth includes migrating performance-sensitive workloads from GCP to AWS while keeping the rest in place — a project that would have been a non-starter on traditional infrastructure.
Control Plane's Virtual Cloud changes that. Workloads can run in GCP and AWS at the same time and talk to each other through the platform's networking. As Minga expands into new geographies, the operational lift is, in Matt's words, "basically already done for us."
Surprisingly Great Support
There was one thing the team at Minga didn't expect. "Most cloud providers, unless you pay big money, you don't really get any support," Matt says. "But the Control Plane team has been right there with us. If I'm having an infrastructure issue, they'll jump in and help go through logs — even when it's not their fault."
“When we initially migrated to Control Plane, our cost savings were probably 30 to 40%. Possibly even more.”
Minga, like many hypergrowth SaaS platforms, needs infrastructure its customers can't break — even as usage fluctuates wildly across the day and across the season. With Control Plane, Minga can keep growing without worrying about whether the platform underneath will keep up. The next chapter for Minga is multi-cloud, multi-region, more workloads, and new products. The team can now take those moves on as projects worth doing rather than risks that get deferred.