70% Infrastructure Cost Cut Without Downtime
FlowOps
We migrated FlowOps from a single-server setup to a fully containerised cloud architecture — cutting infrastructure costs 70% and achieving 99.97% uptime across their first year on the new stack.
The Results
Measurable outcomes delivered within the first six months post-launch.
The Challenge
FlowOps ran their entire SaaS platform on a single beefy EC2 instance. It worked — until it didn't. A failed deployment in Q3 caused 6 hours of downtime, costing them three enterprise accounts and triggering an SLA penalty clause. Their team knew they needed containerisation and proper CI/CD, but lacked the internal expertise to design and execute the migration without risking more outages.
Our Solution
We designed a zero-downtime migration to Kubernetes on AWS EKS. Services were extracted from the monolith one at a time — starting with the lowest-risk, highest-isolation candidates. Each service got its own Helm chart, GitHub Actions pipeline, and resource autoscaler. We set up Datadog for observability before touching any production traffic, so the team had full visibility before, during, and after each phase of the migration.
FlowOps builds workflow automation software for logistics companies. Their platform processes millions of shipment events daily — reliability isn’t a nice-to-have, it’s the product.
Extraction before containerisation
We resisted the urge to containerise the monolith as-is. Instead, we spent the first four weeks identifying natural seams in the codebase — places where services could be extracted with minimal shared-state risk. This gave us smaller, more testable units and avoided the classic “distributed monolith” antipattern.
Blue-green at every layer
Every service ships with a blue-green deployment strategy. Load balancer weights shift gradually — 5%, 25%, 50%, 100% — with automated rollback triggers wired to error rate and latency SLOs. No engineer needs to babysit a deployment.
Cost engineering
The 70% cost reduction came from three places: right-sized instances (replacing one 32-core server with autoscaling groups of smaller nodes), spot instances for non-critical batch workloads, and S3 lifecycle policies on log retention. We documented every saving so the team can continue optimising.
"We went from dreading deploys to shipping three times a day. The operational confidence Netronk gave us is worth more than the cost savings."
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