Practical ways data teams can reduce cloud costs without limiting innovation
Leejay Bell, Senior Data Engineer at PBT Group
Cloud platforms give data teams the freedom to test ideas quickly and scale workloads when demand changes. That flexibility is one of the reasons organisations move to the cloud.
The waste is often less obvious. A team might spin up resources for a proof of concept, move on to something else, and never go back to remove what is no longer needed. In a larger environment, teams may also end up duplicating data or infrastructure simply because they cannot see what is already available.
None of these may look particularly costly in isolation, however the spend builds up. Part of the difficulty is that cloud spend can be difficult to see clearly as it happens. Forecasting gives teams a better view of likely consumption before workloads run, but it is still underused.
Start with the workload, not the bill
Compute is often where the largest cost pressure appears, which makes workload design an important place to start. Teams should first consider whether a process needs to run in the way it currently does.
Where the work can be defined upfront, teams can limit the scope before committing compute. In our own environments, high and low watermarks help identify the range of data that needs to be fetched, while pre-calculating the workload helps establish what needs to be processed before the compute is used. This gives the team more control over how the workload will run and what it is likely to cost.
Trigger-based approaches can also reduce unnecessary activity by allowing one component to start the next when the required work is complete, rather than repeatedly checking the system status to see whether the previous process has finished.
More compute is not always the answer to a slow process. Adding processing power or memory to an inefficient workload can simply consume credits faster. Optimising the process itself should come first. The same principle applies to data extraction. There are often opportunities to reduce unnecessary processing before the workload runs, rather than trying to optimise it after compute has already been committed.
Build housekeeping into the platform
Some cloud costs linger because nobody goes back to clean them up. Test environments are a good example. A team finishes the work, moves on, and the resources remain in place even though they are no longer doing anything useful.
That is why housekeeping needs to be part of the way the environment is managed. If something is no longer needed, it should be removed rather than left sitting there indefinitely. If a team needs the same setup again, the configuration can be kept as a template and redeployed when required.
Platform maturity also plays a role in how storage is managed. A clear archiving strategy should be considered when the environment is designed, with lifecycle policies moving older or infrequently used data to more appropriate storage as part of the normal process. Building this discipline in upfront is far easier than trying to introduce it retrospectively once the environment and its costs have grown.
There is also value in looking at what teams are duplicating. The same tables may be copied or scanned repeatedly when the platform already provides another way to get the information required. Better visibility into shared data and the metadata already available can avoid some of that unnecessary work.
Make cloud consumption visible
Cost optimisation becomes harder when teams do not have a clear view of consumption while workloads are running. Cost information can lag activity, generally by around 24 hours, which makes planning beforehand particularly important.
Forecasting tools can give teams a reasonably accurate view of what a planned workload is likely to cost. That analysis should form part of the specification and planning stage, alongside the technical requirements, rather than only becoming a consideration once the workload is running.
Data Engineers also need visibility into what they are consuming, which workloads are driving cost, and how their activity fits into the broader environment. Thresholds can protect against accidental overspend without discouraging experimentation.
Cost dashboards can help teams see where the spend is going, but the numbers still need context. A workload that uses a lot of compute is not necessarily inefficient. It may simply be doing heavy work for a limited period. The key is understanding what drives that consumption and whether it is expected for the work being done.
Visibility beyond an individual team also helps people reuse resources rather than building the same capability in separate silos.
Keep room for experimentation
A strong focus on cloud cost can easily create an environment where people become hesitant to test new ideas. That defeats much of the value of the cloud.
Sensible boundaries work better than blanket restriction. Thresholds should be configured across the environment so that an errant process cannot continue unchecked and create unexpected costs. Teams can still be given room to explore without leaving that risk open-ended.
The same principle applies to artificial intelligence (AI) workloads. AI adds another layer of consumption, so teams should be deliberate about where it adds enough value to justify the cost rather than treating every routine task as an AI use case.
Cloud cost should be considered as part of the way the platform is engineered and maintained. Workload planning, regular housekeeping, consumption visibility, and ongoing process improvement all help teams control spend without taking away the flexibility they need to experiment.
The goal is more intentional execution. When teams understand the workload before it runs, have a realistic view of the likely cost, and can see how resources are being consumed, they are in a better position to remove waste without making the cloud harder to use.
