As a data leader, I’ve seen firsthand how quickly cloud data costs can spiral out of control if left unchecked. I’ve worked with engineering teams that, despite building efficient solutions, ended up facing unforeseen cost spikes. I have also encountered engineering teams that told me they hadn’t considered costs in the design phase of their project. While building solutions with functionality in mind is key, ignoring costs from the start can lead to painful consequences down the road.
In this guide, I’ll share insights from our experiences with managing data infrastructure, how we’ve helped organizations cut costs, and some of the most common issues we’ve encountered. By taking a strategic and proactive approach, you can reduce your data stack costs and avoid those dreaded budget-busting surprises.
Step 1: Identify Quick Wins for Immediate Cost Reductions
One of the most effective strategies I’ve found for controlling costs is to start by identifying quick wins — those easy-to-fix issues that can lead to immediate savings without major disruptions. When I first work with a company struggling with cloud costs, I always look for opportunities where simple changes can have a big impact.
A quick win I often recommend is turning off idle resources. During an audit of a data warehouse for another client, we found that their virtual warehouses were running 24/7, even though usage dropped to almost zero during off-peak hours. The simple step of setting up auto-suspend features in Snowflake and optimizing their warehouse sizes saved them thousands of dollars a month.
Actionable Tip: Look for low-hanging fruit — inefficient query schedules, over-provisioned resources, or processes running far more frequently than necessary. By adjusting these small details, you can see an immediate reduction in cloud costs with minimal effort or disruption.
Step 2: Batching Over Real-Time Processing
In our experience, real-time data processing can be one of the biggest contributors to cost spikes. I’ve seen this in nearly every company I’ve worked with, and the issue often comes down to one simple fact: real-time is expensive.
One project I worked on involved a company running ingestion processes every minute. The problem was that their data volumes didn’t require such frequent updates — batching the same process every hour would have sufficed. After switching to an hourly batch, we reduced their compute costs by 40%.
Here’s a real-world example: one of our clients was spending around $50,000 annually on real-time data ingestion for their analytics platform. By moving to an hourly batch process, that cost dropped to around $30,000. Not only did it save money, but it also freed up resources for more critical real-time processes, such as customer transaction tracking.
Actionable Tip: If your use case can tolerate slight delays, consider batching data loads rather than relying on continuous real-time updates. For many companies, the move from real-time to batch has provided substantial cost savings without sacrificing performance.
Step 3: The Hidden Costs of Dashboards
Dashboards are critical to decision-making in today’s data-driven businesses.
However, they can also be a significant source of hidden costs, especially if not carefully managed. Over the years, I’ve seen dashboards become bloated with redundant queries, inefficient views, and overly frequent refresh rates, all of which drive up costs without adding much value.
With Seemore Data, we’ve built a solution specifically designed to help data teams manage and reduce these hidden costs. Our platform provides complete lineage visibility, allowing you to track how each dashboard is built and where costs are accumulating. Using Sthese tools, I have helped data leaders to:
- Analyze dashboard usage: Identify which dashboards are used frequently and which are redundant, helping you to prioritize optimization efforts.
- Optimize refresh rates: Automatically adjust refresh rates based on actual usage patterns, ensuring that you’re not paying for updates that no one is using.
- Monitor view dependencies: Pinpoint which views are driving up costs and replace unnecessary view stacks with more efficient tables.
Actionable Tip: Regularly audit your dashboards. Ensure that they are being used and that their refresh rates align with actual business needs. Look to automate much of this process and keep your dashboard costs under control.
Step 4: The Hidden Costs of Rapid Growth
One of the biggest cost drivers I’ve encountered as a data leader is the complexity of growing data pipelines. When companies scale quickly, they often take shortcuts to meet tight timelines, which leads to poorly optimized pipelines and inefficiencies that are hard to identify. This was especially evident for one client using tools like Tableau, Airflow, and Snowflake. Due to a lack of proper data lineage tools, teams struggled to understand their pipelines, transformations, and business metric calculations.
As their Data Engineering Team Lead explained, “Rapid growth meant more and more pipelines were being added, and more models were built, so we needed some sort of basic understanding of what was going on.” Without the right visibility into these processes, non-technical users, such as analysts, had to constantly rely on the engineering team for clarity. This dependency on engineering led to bottlenecks, delayed decision-making, and rising costs.
In one case, we helped a client reduce their engineering dependency by 30% while cutting their pipeline costs by 25%, simply by providing them with the tools to understand their own data flows.
Actionable Tip: Implement data observability tools like Seemore Data to gain full visibility into your pipelines. This will reduce bottlenecks, improve decision-making, and prevent the inefficiencies that lead to cost spikes.
Step 5: Cost Optimization is an Ongoing Process
In our experience, cost-cutting is not a one-time activity — it’s an ongoing process. A solution that works today may not be optimal tomorrow as data volumes grow and processes evolve. I’ve learned that regularly reviewing costs and performance metrics is the best way to ensure long-term efficiency.
I recommend having the ability in real-time to review your most expensive processes and explore opportunities for improvement. For example, you might find that a query running efficiently last quarter is now taking up significantly more compute resources due to changes in data volume. A simple optimization here can save thousands in the long run.
Actionable Tip: Make it easy for your data teams to track their data product costs in real-time, with features like lineage visibility and cost attribution, allowing you to pinpoint inefficiencies quickly.
Final Thoughts: Cost Savings Are Within Reach
Cutting data stack costs doesn’t have to be an overwhelming process. By taking a systematic approach — focusing on visibility, choosing the right tools, and optimizing your models — you can reduce costs significantly while still maintaining performance.
If you’re facing rising data infrastructure costs and need help finding solutions, reach out. I’ve spent years helping companies like yours optimize their data stacks and save money. With the right strategies, your data stack can be both high-performing and cost-effective.
Are you interested in continuing this discussion directly with Guy? You can message him at guy@seemoredata.io to delve deeper into eliminating your data cost spikes.
