{"id":54234,"date":"2026-08-26T10:28:28","date_gmt":"2026-08-26T09:28:28","guid":{"rendered":"https:\/\/www.intelligentcio.com\/north-america\/?p=54234"},"modified":"2026-08-26T10:28:28","modified_gmt":"2026-08-26T09:28:28","slug":"why-most-enterprises-are-running-multiple-clouds-but-arent-actually-multi-cloud-ready","status":"publish","type":"post","link":"https:\/\/www.intelligentcio.com\/north-america\/2026\/08\/26\/why-most-enterprises-are-running-multiple-clouds-but-arent-actually-multi-cloud-ready\/","title":{"rendered":"Why most enterprises are running multiple clouds but aren’t actually multi-cloud ready"},"content":{"rendered":"\n

Running workloads on AWS, Azure, and GCP is not a multi-cloud strategy. You have three single-cloud strategies sharing an org chart, says Sharad Kumar, Co-Founder & CEO, FluidCloud.<\/em><\/p>\n\n\n\n

Ask a CIO whether their organization is multi-cloud and the answer is almost always yes. Ask that same CIO whether they could move a production workload from AWS to Azure next quarter and the answer is almost always no. That gap is the whole story.<\/p>\n\n\n\n

Running workloads on AWS, Azure, and GCP is not a multi-cloud strategy. You have three single-cloud strategies sharing an org chart. Different teams picked different providers for different reasons, and the estate that results looks diversified on a slide and behaves like a stack of single-cloud lock-ins piled on top of each other. On paper you have options. In practice you have three places you can’t leave.<\/p>\n\n\n\n

Here’s the part that gets missed. Multi-cloud workloads that are actually distributed across providers are close to non-existent today, and it’s not because nobody wants them. The demand is real. What’s missing is the operational layer to execute on it. Teams can stand up an account on a second provider in an afternoon. What they can’t do is run the same workload across two providers and move it between them when cost, performance, or a renewal negotiation says they should. Nobody built the layer that makes that a routine operation instead of a project.<\/p>\n\n\n\n

I’m not describing this from the outside. We lived it. My last company was acquired by Tenable, and moving our accounts from Accurics into Tenable took us 8 to 9 months. That was inside AWS. Same provider, just different accounts, and it still swallowed most of a year. We had strong engineers and years of experience and it was still a herculean task. Because once you go through that kind of pain, you don’t forget it. So, when people tell me they’re multi-cloud, my first question is simple: could you actually move?<\/p>\n\n\n\n

The 90% problem<\/strong><\/p>\n\n\n\n

Here’s what I see in most multi-cloud environments. A company is on three providers, and 90 to 95 % of the workloads sit on one of them. The other two hold a handful of things that got started on free credits and never grew. Nobody moved the main stack. They just built the next new thing wherever it landed and called the collection multi-cloud.<\/p>\n\n\n\n

Real multi-cloud means you can rebalance that portfolio. You can move, clone, and redistribute workloads across providers without turning it into a six-month engineering project. Almost nobody can do that today, and the reason isn’t that teams are lazy or the tooling is bad. It’s that the tooling was built to solve a different, easier problem.<\/p>\n\n\n\n

Where the standard tools stop<\/strong><\/p>\n\n\n\n

Migration and infrastructure-as-code tools do the easy part well. They move the VM, they move the storage. Then they stop at the boundary of the thing that’s actually hard, which is everything attached to the workload.<\/p>\n\n\n\n

A workload doesn’t stand alone. It has IAM policies granting it access to other services, security groups controlling what can talk to it, encryption keys letting it read its own data, Kubernetes manifests telling it how to scale, and database schemas it depends on. Get any one of those wrong in the destination cloud and the workload either fails to start or, worse, fails silently. The silent failures are the ones that ruin your quarter, because you don’t find out until production traffic hits.<\/p>\n\n\n\n

These tools were designed when ‘migration’ meant moving a VM and the surrounding infrastructure was treated as cleanup. That’s how a project that should take six weeks turns into twelve months. The VMs map in minutes. The policy layer, the networking, the identity mapping, that’s where teams burn their consulting budgets. And that layer is exactly the part nobody automated.<\/p>\n\n\n\n

Why generic AI doesn’t rescue you<\/strong><\/p>\n\n\n\n

The obvious move is to throw AI at it. We tried. We spent about four months getting top LLMs to generate our Terraform and infrastructure plans automatically, and I’ll say plainly that it didn’t work.<\/p>\n\n\n\n

The models produced output that referenced AWS APIs that don’t exist. Syntactically valid, completely wrong. And on infrastructure, a wrong answer isn’t an inconvenience you edit later. Hallucinating on a Terraform plan or an IAM policy is a production incident. Infrastructure is deterministic. LLMs are probabilistic. A general-purpose model trained on text corpora can be creative when you want an email drafted, but it can’t reason about the exact state of your environment, and infrastructure demands absolute accuracy on the configuration. That’s why we ended up building a model trained on infrastructure state and topology rather than language. Text models guess. Infrastructure can’t afford a guess.<\/p>\n\n\n\n

A readiness test you can run this week<\/strong><\/p>\n\n\n\n

If you want to know whether you’re actually multi-cloud or just multi-cloud on paper, put four questions to your own team and watch how long the answers take:<\/p>\n\n\n\n