More than 90% of organizations use the cloud and the cloud computing market will surpass $1 Trillion by 2028. 

However, 7 out of 10 companies aren’t sure what they spend their cloud budget on. 

Scalability, security and flexibility are the key reasons why companies have increasingly adopted cloud computing. Teams rely on multiple data processing pipelines to fuel machine learning (ML), artificial intelligence (AI), and analytics models. However, if not optimized, using the cloud can lead to unexpectedly high costs.  

A single oversight—like forgetting to shut down an EC2 instance—can add thousands of dollars to your bill. (78% of companies waste 21-50% of their cloud resources, according to a 2024 Stacklet survey) 

According to recent Flexera research, managing cloud spending is the top challenge for organizations using the cloud face. For the second consecutive year, managing cloud costs remains the biggest challenge for teams. 

Building Datatailr, we've seen 100+ data teams struggle with similar cloud usage pitfalls. Here's a concise summary to help you avoid them. 

7 Common Mistakes to Avoid for Effective Cloud Resource Management 

1️⃣ Ignoring Instance Pre-Warming 

AI/ML workloads often require high-performance computing resources. Pre-warming instances ensure they’re ready for intensive computations, minimizing startup delays and preventing idle resources from draining your budget unnecessarily. 

2️⃣ Manually Starting and Stopping Instances 

One of the most basic yet costly mistake? Forgetting to turn off a compute instance. A single forgotten CPU/GPU instance running at full power can cost more per month than a developer's salary. 

Automate instance shutdown using an autoscaler that detects when AI model training or data processing tasks are complete. This ensures you only pay for what you use and prevents runaway costs. 

3️⃣ Not Optimizing Data Storage and Transfer 

Many AI/ML teams focus on compute costs but overlook data storage and transfer fees, which can silently add up. Moving large datasets between cloud regions, using high-performance storage unnecessarily, or failing to archive old data can lead to unexpectedly high bills. 

A better approach is to avoid moving your data entirely—store it in multiple locations and connect to it without requiring migration. 

4️⃣ Not Scheduling System Shutdowns During Non-Working Hours 

Most AI training jobs don’t need to run 24/7. Scheduling your infrastructure to automatically shut down during non-peak hours or working hours can significantly reduce idle cloud costs while ensuring availability when needed. 

5️⃣ Using Incorrect Cloud Instances for Workloads 

We've seen developers using production-level GPUs for simple ML model tests. Creating separate environments with appropriate resource limits can prevent this costly mistake. 

Regularly assess (or automate) and adjust instance types/sizes to match your AI workload requirements. Over-provisioning leads to wasted spending, while under-provisioning can cause performance bottlenecks. 

6️⃣ Not Leveraging Spot Instances 

Training AI models requires substantial compute power, but not all workloads need real-time performance. For flexible tasks, spot instances can be up to 90% cheaper than standard instances. However, since they can be terminated unexpectedly, use them strategically for non-time-sensitive AI model training. 

7️⃣ Lack of Monitoring and Cost Control Alerts 

Without visibility into real-time cloud usage, costs can spiral out of control before anyone notices. AI/ML jobs often run in the background, consuming resources unnoticed. 

Use cloud monitoring tools to track utilization and set up alerts for unusual activity. This enables early intervention before costs get out of hand. 

At Datatailr, we help businesses optimize cloud spending by: 

✔ Automated resource management – Shut down instances automatically after AI jobs complete. 
✔ Real-time monitoring – Get alerts before cloud costs spiral out of control. 
✔ Intelligent scheduling – Ensure your AI infrastructure runs only when needed. 

With these strategies and tools, AI/ML teams can scale without breaking the bank. 
 
Are your cloud costs under control, or are they controlling you? 

Reach out to us directly if you have any questions.