Controlled Chaos

Managing multiple cloud platforms sounds smart—vendor flexibility, geographic redundancy, better pricing.

But in reality? It’s often just juggling chaos.

Multi-cloud isn’t just about spinning up services on AWS, GCP, and Azure at the same time. It’s about dealing with their wildly different APIs, networks, tools, and ways of thinking. It’s not “run everywhere”—it’s “wrangle everything.”

A lot of teams start off with good intentions and end up buried in complexity. This post looks at two companies that tried the multi-cloud path—what worked, what backfired, and what they learned the hard way.

Lessons from the Field

📍 Company A: All the Clouds, None of the Control

This startup jumped into AWS and GCP from day one. The idea? Avoid vendor lock-in. Use the best tools from each provider.

The result?

• Fragmented systems
• Duplicate work
• Nearly 30% spike in operating costs

The biggest mess? Cross-cloud networking. They needed their services to talk to each other—but ended up manually stitching VPNs together and bouncing between dashboards just to stay online.

What saved them? Terraform.

It let them define and manage infrastructure in one place, with one language. No more clicky dashboards. No more chaos.

Here’s a stripped-down version of their setup:

provider "aws" {
  region = "us-east-1"
}

provider "google" {
  project = "my-gcp-project"
  region  = "us-central1"
}

resource "aws_vpc" "main" {
  cidr_block = "10.0.0.0/16"
}

resource "google_compute_network" "default" {
  name                    = "vpc-network"
  auto_create_subnetworks = true
}

What they learned: You can’t wing it. You need a clear system for infrastructure. Otherwise, things break fast—and no one knows why.

📍 Company B: Azure All-In... Until It Wasn’t

This mid-sized tech company went all-in on Azure. The CTO wanted one ecosystem, fewer variables, tighter control.

For a while, it worked.

Then their data processing loads exploded—and Azure Functions couldn’t keep up. Throughput dropped. Delays got worse. Customers started to notice.

Their fix? Offload the heavy stuff to AWS Lambda. It wasn’t part of the plan. It took weeks to set up. But in the end, it worked.

Here’s the kind of command they used to deploy:

aws lambda create-function \
    --function-name MyFunction \
    --runtime nodejs14.x \
    --role arn:aws:iam::account-id:role/service-role/MyRole \
    --handler index.handler \
    --zip-file fileb://my-function.zip

Their takeaway: Betting on one cloud is simpler—until it isn’t. Sometimes, the right tool lives somewhere else.

🛠 Tools That Actually Helped

Here’s what teams leaned on to bring order to the cloud circus:

• Terraform – One config language to rule them all.
• Kubernetes – Run containers wherever you like.
• Consul – Service discovery that works across clouds.
• CloudFormation / Deployment Manager / ARM – Great if you’re staying in one cloud. Less portable.
• Pulumi – Infra-as-code, but with full programming languages.

Pro tip: No single tool solves everything. What matters is picking a small, reliable set—and building your process around them.

🎯 Real Talk: Multi-Cloud Isn’t Just Tech. It’s Triage.

If your team doesn’t share a strategy—if you don’t have naming rules, tagging standards, or access controls—it all falls apart.

But when you do it right? Multi-cloud gives you:

• More performance options
• Better global reach
• Stronger negotiation power

Just know this: it’s not a one-off project. It’s a long game. You’re building a system that needs to last.

So plan like it.

Use Terraform or Pulumi. Write everything down. Automate as much as you can. Hold regular architecture reviews. And make sure every engineer knows the why behind your setup—not just the how.

You won’t make it perfect.

But you can make it manageable.