Welcome! Today, we kick off a blog series dedicated to multi-cloud Kubernetes installations and  will highlight the problems we’ll solve along the way, as well as some trade-offs. Throughout this series, we'll craft a multi-cloud solution that provides higher availability, better disaster recovery, improved data architecture, accelerated development speed, reduced operational costs, and a decrease in bugs stemming from snowflake infrastructure. While some of these wins will come for free as an outcome of the process, others will be strategic choices to preempt future issues.

In this series we will cover the following topics

Intro and Pre-requisites

CI/CD and monitoring considerations

Cluster management and cost solutions

Networking challenges

Data constraints

Trade-offs and conclusion

Without further ado, let's dive headfirst into the realm of advanced Kubernetes: multi-cloud Kubernetes. First, we'll explore the reasons why you might consider this approach. If you can't find a compelling answer to this question, multi-cloud Kubernetes might not be the right fit for you, and that's perfectly fine! Here's a brief overview of potential motivations for pursuing a multi-cloud setup:

1. Do you genuinely require higher availability than what each major cloud provider offers today?

2. Does compliance necessitate the ability to restore to a second cloud?

3. Are your SLAs unattainable through standard disaster recovery exercises?

4. Do you need access to cloud-specific features, like GCP's VertexAI, AWS's spot instances, or Azure's OpenAI services?

5. Do you have financial commitments across multiple clouds that would go to waste if you don't distribute costs efficiently?

This list isn't exhaustive but serves as a starting point for your consideration. Making this decision should be a deliberate process. I'll be reiterating a common theme throughout: every advantage comes with its own set of disadvantages. Implementing a multi-cloud approach will introduce additional costs, complexities, and potential latencies into your ecosystem.

So, multi-cloud Kubernetes is the right path for you. What comes next? Well, before we move forward, we actually need to take a step back and talk about multi-cluster Kubernetes. Building Kubernetes clusters quickly and consistently is the first order of business before we can extend them across various clouds.

So, what does it take to rapidly create identical clusters in multiple locations? From my experience, one of the easiest and most common methods involves well-crafted, abstractable Terraform modules. Now, before I incur the wrath of countless DevOps professionals, it's worth noting that technology evolves rapidly, and I don't expect this to remain the most prevalent approach for achieving consistent and repeatable Kubernetes infrastructure indefinitely. Newer releases of the Cluster API have proven highly effective (even Rancher now utilizes Cluster API under the hood). Additionally, with technologies like Crossplane and Pulumi, we might witness emerging contenders in the realm of building repeatable Kubernetes environments.

For Terraform, it's advisable to build a base module that allows you to configure variables, enabling you to recreate the same cluster effortlessly. We should not need new Terraform code, only new variables. If executed well, you might even generate clusters dynamically from the command line by injecting variables like this:

```

terraform apply -var cluster_name=foo-bar-1

```

Now, with the capability to construct numerous clusters, the next step is to expand the Terraform modules, Cluster API, or Crossplane to support additional clouds. This phase should progress more swiftly than the initial cloud setup, but it will still require some time. Once completed, we'll be equipped to build nearly identical, empty clusters in all major cloud providers. This is where the real excitement begins.

Next in our series, we'll delve into the extensive array of challenges,

commencing with difficulties of CI/CD in a multi-cloud environment.