To avoid vendor lock-in and implement the most suitable solutions, companies increasingly deploy applications across multiple cloud providers. But while multi-cloud strategies offer flexibility, they also multiply the complexity of cost management – forcing FinOps leaders to find a consistent way to compare cloud provider pricing and avoid hidden cost gaps.
This guide takes you through the complexity of multi-cloud deployments and explains why targeted, vendor-neutral cost monitoring is essential.
Why is multi-cloud deployment so difficult?
Deploying cloud platforms and services from multiple vendors is a complex process riddled with technical challenges. It’s common to experience difficulties already at the stage of choosing between public cloud alternatives.
Moreover, once you make a choice, it’s very difficult to migrate or roll back to another provider. And while you’re trying to balance between cost and performance, the provider is constantly evolving. Whoever is at the top today will not be the best option for you in two years.
Here are a few common problems you might experience at this point:
1. Unclear cost implications
Comparing providers and understanding the cost implications of each option is challenging. One reason is the lack of visibility in offers (more on that in the next point). But that’s just the tip of the iceberg. Consider that costs aren’t static and tend to change over time – and so do your business needs.
2. Lack of visibility
When comparing different cloud providers, expect to face massive differences in hosting and services. Pricing pages are confusing and sometimes outdated. Even when you understand the sticker price, the actual cost of running a workload often depends on variables that are difficult to predict upfront – think egress charges, inter-zone traffic, storage IOPS, and API call volumes that can all inflate your bill in ways that only become clear after deployment.
3. Hidden details
Then there are all the things that surprise you, even when you thought you knew everything. One vCPU core might not work similarly to the same core listed on a different plan. Engineers may struggle to select the right VM shapes due to the multiple options available or even multiple options within the same shape.
Also, expect default trade-offs in terms of the software provided by cloud vendors. Each provider comes with a set of software solutions that are generally similar but still unique in their own way. What you get from AWS might not be available in Google Cloud, and vice versa.
So what’s the conclusion to all this?
Comparing various cloud providers in detail reveals a very different story from what their salespeople and marketers tell us.
This is where cost monitoring comes in
Given these challenges, navigating a multi-cloud environment without visibility is inefficient and costly. Cost monitoring provides the visibility and control that pricing pages and sales conversations never will.
Effective cost monitoring goes beyond simply aggregating invoices. It means tracking resource consumption at a granular level, correlating spending with actual workloads, and surfacing anomalies before they become budget disasters. When you can see exactly which services, teams, or applications are driving costs across all your providers, you move from reactive bill-paying to proactive optimization.
Here’s an example of a reporting solution that shows the overprovisioning percentages as well as a breakdown of average hourly cost across provisioned, requested, and used resources:
For multi-cloud deployments specifically, vendor-neutral monitoring is essential. Native tools from AWS, Google Cloud, or Azure only show you a slice of the picture – and each presents data differently, making cross-provider comparisons nearly impossible.
A unified view lets you answer questions that matter:
- Which provider is actually cheaper for this workload type?
- Where are we overprovisioned?
- Are our commitments being fully utilized?
Cost monitoring also closes the feedback loop between engineering decisions and financial outcomes. When developers can see the cost impact of their resource requests or architectural choices, they make more informed decisions. When FinOps can tie cloud spending to specific products or customers, they can accurately model unit economics.
The bottom line: in a multi-cloud world where pricing is opaque, costs shift constantly, and hidden variables lurk everywhere, real-time visibility is no longer a nice-to-have; it’s a must-have. It’s the foundation for every optimization decision you’ll make.
Real-life example: Flowcore
Flowcore is the startup behind a developer-first platform that makes it easy to collect, process, and act on data in real-time. As the company scaled its platform and onboarded new customers, the rise in data volumes made the challenge of cost control more pressing.
Flowcore sought a solution that could improve cost visibility and optimize costs in a dynamic, high-traffic environment, ensuring efficient scaling without overspending.
The team tested several solutions, including OpenCost, but only Cast AI delivered the required level of cost visibility.
But there’s more to the story. Cast uses automation to maintain optimal compute cost by running a nightly scheduled rebalancing job. During rebalancing, Cast bin-packs workloads into fewer nodes and then moves them to more cost-effective compute resources.
“For me, the key difference between Kubecost and Cast is how they handle costs. With Kubecost, you dive straight into workload-level costs, which is useful, but with Cast, you get visibility into both the workloads and the underlying infrastructure – like machine and memory costs.
This dual-layer visibility in Cast is a significant advantage because it gives you a more complete overview of everything, not just the workloads. This is a very important difference when you’re managing costs across the entire environment.”
Julius á Rógvi Biskopstø, CTO and Co-founder at Flowcore
Let automation compare and select the best resources
In a multi-cloud world where pricing is opaque and hidden variables drive up costs, visibility is the foundation for every optimization decision. But visibility alone doesn’t solve the problem of ongoing optimization.
Cloud pricing shifts, your workloads evolve, and what’s optimal today won’t be optimal next quarter. Manually reevaluating options across providers is unsustainable. This is where automation becomes essential – continuously analyzing your requirements, comparing available resources across clouds, and selecting the best fit without requiring constant human oversight.
Kubernetes cost optimization
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FAQ
Why is cost monitoring essential for multi-cloud deployments?
Native tools from each provider only show their data, making cross-provider comparisons impossible. Vendor-neutral monitoring provides a unified view, enabling you to identify which cloud is actually cheaper for specific workloads and catch cost anomalies early.
How can automation help reduce multi-cloud costs?
Cloud pricing shifts and workloads evolve constantly. Automation continuously compares options across providers and selects the best fit for cost and performance – keeping your deployments optimized without manual oversight.
