How to Escape the Bioinformatics 40% Cloud Overspend Trap: Three Ways to Reduce Cloud Costs

const metadata = ; Summary - 40% of cloud budgets are wasted – mostly because teams can't see where the money’s going - A single server choice cost us $2,000 in two days. This risk to cloud performance could happen to anyone - Our engineers share three proven ways to reduce spend – without slowing research down A $2,000 Mistake: The Real Cost of Cloud Blind Spots Over a single 48-hour period, our team at Tracer unintentionally spent 45% of our weekly cloud budget, wasting $2,000 on AWS. Had we not caught it, this rate of spend would have escalated to over $1 million annually. Our story is far from unique. In the pharma and bioinformatics space, cloud resource optimization is a rapidly growing challenge. As these industries expand, and the complexity of their activities in tandem, a rise in costs is natural – the sector's 40% budget waste is not. According to research, 78% of companies estimate that 20–50% of their cloud spend is wasted each year. We’ve observed similar patterns in pharma and biotech, where interviewees estimate waste between 25% and 55%. The common key word is estimated; most companies are unable to provide clear statistics because they do not know the extent of their waste. Overspending is not driven by negligence or mismanagement – it’s caused by a systemic lack of visibility into cloud-based infrastructure. This prevents teams from rightsizing their workloads, causing waste. “We observe costs by jumping into people's accounts and checking the numbers ourselves, or we don't know our costs at all. That's the bar right now.” Cloud Waste Starts with a Click: A Tale of Two Instances Choosing between server sizes often happens under pressure, with limited visibility into actual workload requirements. In the example below, a researcher needs to select between two AWS EC2 instances. Illustration of CPU and memory usage for two EC2 instance types. Green: used capacity. Gray: unused resources. On paper, the larger instance looks safer. In practice, it leads to overprovisioning. This is why clear, real-time cost and usage data is essential for making informed decisions and identifying opportunities to rightsize. In its absence, cloud waste quietly grows across the industry. Why Cloud Budgets Leak: Rightsizing, Visibility & Failures Three Factors Driving Cloud Waste “Last month, we estimated $200K of cloud waste issues we weren’t even aware of. It could be more, it’s hard to know for sure. Our total cloud spend is between $500K and $900K, but even that’s just an estimate.” - Poor pipeline visibility Pipelines are duplicated, instances are left running, and crashes go unnoticed. - Rerunning crashed pipelines The same cloud resources are consumed multiple times to produce a single result. 5–35% of pipelines crash. 75% of these failures are preventable with better monitoring. - Lack of rightsizing in cloud resource management Occurs when teams over-provision compute, memory, or storage as a safeguard, leading to unused capacity. 20% of cloud resources can be reduced by rightsizing, even whilst leaving a buffer for usage spikes. Engineering-Led Solutions to the Bioinformatics Cloud Cost Problem We’ve experienced cloud waste firsthand – every team we’ve spoken to has as well. So, we asked our engineers for their top three tips to start optimizing cloud spend. Fix #1: Lifecycle Automation John Didion, VP of Product Engineering "One of the more persistent sources of cloud waste I've seen is from resources that stay allocated longer than they are used. A typical example is high-cost instances – like GPUs or high-memory nodes – running idle because a job finished early or a notebook was left open overnight." One solution is to build an internal dashboard that compares the provision, the request and actual usage. Combine data from cloud providers usage metrics (like CloudWatch) with job-level metadata from a workflow engine Connect to logs from your scheduler or container platform Plot usage time and identify trends With enough engineering effort, this setup can be extended to trigger alerts or inform scheduling policies. Fix #2: Job-level Tagging on AWS Batch Michele Verriello, Staff Software Engineer “One way to get visibility into each job and tool is combining AWS Batch with job-level tagging and AWS cost explorer." Run pipeline tasks – like alignment, QC or variant calling – as separate batch jobs Apply metadata tags to each job Export and join the metadata with billing reports e.g. via Athena Tagging standards need to be enforced across your team, and exit codes and instance types should be logged per job, so the setup can feel impractical. However, the insight it will generate is essential when trying to control cloud spend proactively. Fix #3: Workflow Reconfiguration Michele Verriello, Senior Software Engineer "Another helpful way to reduce cloud spend is to revisit workflow design. Engineers can redesign pipelines to use spot instances or containerized jobs." - Review default resource requests; many jobs over-allocate CPU and memory as a precaution - Identify opportunities to break pipelines into smaller steps; run only what’s necessary for each task - Experiment with more cost-effective compute options: Use spot instances or smaller machine types for non-critical steps These steps can help gain partial visibility, enabling rightsizing. However, the latter relies on the former: if visibility data is scattered across multiple systems, then the precision of rightsizing will be negatively impacted. Why Today’s Cloud Monitoring Tools Fall Short for Bioinformatics The figure below breaks down how each cloud resource allocation monitoring software prioritizes different capabilities. Across these tools, a clear pattern emerges: visibility is fragmented, limiting the ability to confidently rightsize workloads. Current Observability Tools: Strengths and Gaps Provider Includes ✅ Key Lacking Feature ❌ Monthly Spend Breakdown by environment, team, project or pipeline Unit cost breakdown and Kubernetes Designed for finance and engineering, making it difficult to attribute costs to specific bioinformatics steps or tools without manual setup Broad cost monitoring No specific attributions unless custom instrumentation and tagging is used per step Job-level metrics like runtime & resource usage Costs per job or any pipeline level context. Lacks built-in safeguards for idle or oversized instances Granular insight into pipeline costs on Nextflow Lacks forecasting, business costs (i.e. per lab) and anomaly detection such as unexpected cost spikes. Doesn’t include automated idle shutdowns or cost enforcement policies Too Many Metrics, Too Little Meaning Because no single monitoring software provides sufficient visbility into cloud performance, teams face two mediocre options for managing cloud costs: 🐌 Path 1: Manually consolidate logs and metrics across different systems Limitations: - Process is prone to error and time-consuming - With organizations often spread across multiple environments (e.g., AWS accounts), attributing and calculating costs becomes even harder Outcome: Limited insight and increased risk of human error undermining data-driven decisions 🛠️ Path 2: Building in-house programs Limitations: - Teams stitch together product logs and billing data to DIY cost tracking - These are challenging to set up and even harder to maintain Outcome: Significant time and money spent creating a fragile, temporary fix to a long-term problem Both paths leave teams struggling to understand metrics across a mix of DIY and other tools — and continuing to experience cloud waste. “Honestly, knowing where exactly our cloud spending is going and where our attention should be, would be the single largest benefit for our teams at the moment.” Building Tracer: One Platform to See, Attribute, and Take Control of Costs We've faced these frustrations ourselves more than once, and we've heard the same from over 100 people across the industry. So, we built a system that lets you start saving right away, with cost reporting that's simple to set up and genuinely easy to use. Our goal isn't to add another tool to your workflow - it's to remove the common headaches bioinformatics teams face every day, all in one place. No more juggling multiple dashboards or relying on tagging workarounds. With Tracer, you can see cost and performance over time, broken down by pipeline, run, and tool—so you're not left guessing where the spend is coming from. By combining cost attribution and rightsizing guidance into a single view, Tracer helps reduce the friction that slows down life science progress. _If the challenges discussed above resonate with you, we’d love to talk._ Connect with us [here](https://www.tracer.cloud/demo).