How do we help with DevOps outsourcing?
Your team needs to develop, deploy, and scale - but there aren’t enough hands to do it.
Developers are stuck handling deployments, monitoring, and troubleshooting instead of building new features. DevOps engineers? If you have them, they’re overloaded. If you don’t, hiring takes months. And when key people go on vacation or get sick, everything grinds to a halt.
At CloudPanda.io, we solve these problems by offering technical support, monitoring, and a full DevOps team-ready when you need us. No gaps, no internal conflicts, no hiring headaches.
Check what we’ve done for our clients
Who do we help?
Scaling startups & MVPs
Growing SaaS & tech companies
Software houses & agencies
Enterprise & regulated industries
Can’t find your exact case? Check our use cases!
Smart monitoring = Peace of mind
Problem
Our client had unpredictable traffic peaks. A new API instance once failed silently on AWS – no alerts, just downtime.
Solution
We implemented Prometheus + Grafana with real-time Slack alerts. Alerts fire when any resource usage crosses 80%, labeled as either warning or error depending on severity.
Result
No more blind spots. Their team gets instant, actionable notifications and can fix issues before users even notice.
Our client saved $4,762.54 in one month
Problem
Client’s internal team was overwhelmed with infrastructure tasks, but didn’t have the budget for a full-time DevOps hire.
Solution
They subscribed to our DevOps-as-a-Service model. It scales with workload - high-intensity weeks mean a higher plan, quieter months drop to a basic let’s stay in touch plan.
Result
They outsourced 50% of their DevOps work to two of our engineers, and saved $4,762.54 in one month.
CI/CD pipeline that just works
Problem
One of project's deployment was manual, slow (4 days on average), and inconsistent.
Solution
We built a CI/CD pipeline using GitHub Actions, shared workflows, and secure automation. How does it work?
- Code changes → Pull request
- Automated unit tests (PHPUnit), security scans with Trivy & Hadolint
- If green: auto-build and deploy to UAT (Kubernetes)
- Manual acceptance → same flow runs for production
Result
Deployment time dropped from 4 days to 3 hours, with full traceability and security built in.
Scalable architecture in Microsoft Azure
Problem
Client needed a cloud environment that could scale automatically based on current demand (various application traffic).
Solution
We designed and deployed the full architecture in Azure using KEDA for event-driven auto-scaling. Monitoring was a key part of the setup (see Smart Monitoring use case).
Result
The system scales up during traffic spikes and down when demand is low — saving both performance headaches and cloud costs.
Cloud infrastructure solutions
- CloudPanda.io S.C.
- VAT ID: PL5842818011
- REGON: 522033432