How do you handle discrepancies between your organization’s cloud billing and the cost projections provided by your monitoring tools or dashboards?
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Where are you seeing the main cost drivers? Is it consumption, performance, data, or licensing?
It entirely depends on your workloads and how they behave. For database-heavy environments with high growth, storage costs will grow. For data analytics, compute will be a bigger cost. Cost spikes often occur when niche use cases require high-performance storage or when engineers double resources to solve problems, which doubles the cost in the cloud. A strong process is needed to mitigate this.
In my experience, monitoring tools, whether native or third-party SaaS, are all terrible at forecasting. They are good at showing current and past costs, but they can only infer the future based on the metrics you provide. They lack visibility into upcoming business initiatives or seasonal business changes. The gap is best filled by strong partnerships between cloud teams, SREs, application teams, and product owners. Layering human intelligence on top of tool metrics results in better forecasting.
The main challenge is demand forecasting. Most tools only look at historical or current usage and cannot predict future demand, as they do not know your business processes. They do not have a way to forecast demand unless it is explicitly programmed.