For years, cloud technologies have been promoted as the smarter, cheaper, more flexible alternative to on-premises infrastructure. The promise is clear: you only pay for what you use, costs are fully transparent and scalability is just a click away. So why do so many organisations still struggle with understanding and optimising cloud data platform costs and their usage, and why does uncertainty remain so high?
The Gap Between Promise & Reality
At face value, cloud pricing is simple. Every major provider publishes clear pricing pages showing exactly what they’ll charge, but the issue isn’t transparency, it’s usage. The pricing unit: per minute, per hour, per GB, per query, is mostly unknown.
Most businesses simply don’t know what they’re using. And because cloud services bill on consumption, this lack of clarity can become a problem very quickly. We’ve seen organisations accidentally provision services without usage limits, causing costs to spiral, and others rack up unexpected bills, like $10,000 in a single month from testing AI models.
In fact, pay-per-unit rates for cloud are often higher than equivalent on-premises costs. The value of cloud data platforms comes not from cheaper units but from the flexibility to optimise usage, if you can control it.
Where The Value In Cloud Comes From
Optimising cloud data platform costs is about providing value in two main ways:
1. Shutting down resources when they’re not needed
Development and test environments don’t need to run 24/7. Shutting them off automatically outside of working hours can save significant costs.
2. Auto-scaling to meet demand
Cloud data platforms allow both vertical and horizontal scaling. When configured properly, they can scale up to handle peak workloads and just as importantly, scale down to reduce costs during quieter periods.
The challenge is that many IT teams aren’t doing either of these things well. Storage usage is sometimes tracked, but compute, memory and overall capacity often remain unclear, especially when migrating from on-premises or self-hosted data centres.
Enter FinOps: Bringing Discipline To Cloud Costs
Just as DevOps transformed development and RevOps transformed revenue operations, FinOps has emerged to operationalise financial management in IT.
FinOps is about embedding cost awareness into cloud operations. This means not only knowing what drives costs, such as queries and warehouses in Snowflake or data movement in Azure Data Factory, but also tracking those drivers at a detailed level.
And here’s the catch: your cloud provider is already tracking every bit of consumption. It’s how you’re billed. The question is whether you’re measuring, reporting and acting on that data internally.
Practical Steps To Gain Cost & Usage Certainty
Understanding and optimising cloud data platform costs involves analysing charges for resources like compute, storage and data transfer, which vary by usage, service tiers and provider regions. Costs are influenced by factors such as workload intensity (especially for AI/ML), storage volume, access frequency and data transfer volumes.
To get real visibility and control, organisations need to build stronger practices around cloud usage monitoring and automation. If there is a problem with cloud operations the first thing is to identify the problem (which you’ve probably already done if you’re reading this). The second thing is to start measuring the problem with cloud cost analysis which is the practice of continuously monitoring, tracking and analysing cloud spending to identify cost-saving opportunities. In particular:
- Report on usage from your cloud provider. Can you break this down by resource group (Azure) and whatever AWS or GCP uses? What about resource type?
- Tag your resources. Without tagging, costs are just a lump sum. Are you tagging resources so you can track costs? With tagging you can trace spend back to teams, projects or environments.
- Leverage tools. Cloud providers offer built-in services to help manage and optimise costs. For Microsoft Azure users, the Pricing and TCO Calculators assist with estimating costs before scaling workloads, while Azure Advisor offers cost-saving insights aligned with FinOps practices. Azure Cost Management supports budgeting, forecasting and alerts and Power BI dashboards can provide detailed cost analysis by resource or business unit. Platforms like Snowflake offer built-in tools such as Streamlit in Snowflake for cost visibility, while third-party solutions like nOps and Harness provide advanced capabilities including multi-cloud visibility, granular cost allocation and automated optimisation.
- Automate Infrastructure Deployment. Manual provisioning of cloud resources is slow, error-prone and costly. By deploying infrastructure programmatically, you can spin environments up and down on demand, matching resources to actual business needs. For example, if scaling an application takes hours of manual effort, you’ll either over-allocate resources (waste money) or risk under-allocating during peak periods (hurt performance). Automation removes this trade-off, ensuring both efficiency and agility.
The Path Forward
Getting certainty around cloud data platform usage and costs isn’t about chasing cheaper units, it’s about visibility and control. By embracing FinOps practices, implementing continual cloud cost analysis and automating deployment of infrastructure and resources programmatically, businesses can finally bridge the gap between cloud’s promise and reality.
The organisations that succeed in optimising cloud data platform costs will be the ones that treat cloud costs not as a mystery, but as a measurable, manageable part of their data and analytics strategy.
