Cloud Storage Cost Management

Over the last 1 year, we helped 15+ companies cut their cloud bills by 30-40% in 45 days (without a single new tool). Here’s what most cloud teams don’t realize: ❌ You don’t have a cost problem. ✅ You have a waste problem hidden in plain sight. We attacked the invisible waste buried deep in their Kubernetes clusters: 1. 𝐑𝐞𝐪𝐮𝐞𝐬𝐭𝐬 𝐚𝐧𝐝 𝐋𝐢𝐦𝐢𝐭𝐬 𝐖𝐞𝐫𝐞 𝐒𝐞𝐭… 𝐚𝐧𝐝 𝐅𝐨𝐫𝐠𝐨𝐭𝐭𝐞𝐧 Developers set inflated CPU/memory limits “just in case” and never revisited them. We ran real-time profiling using Prometheus + Grafana and recalibrated limits based on actual sustained usage. This alone brought down cluster size by 15-20%. 2. 𝐍𝐨𝐧-𝐏𝐫𝐨𝐝 𝐄𝐧𝐯𝐢𝐫𝐨𝐧𝐦𝐞𝐧𝐭𝐬 𝐖𝐞𝐫𝐞 𝐓𝐫𝐞𝐚𝐭𝐞𝐝 𝐋𝐢𝐤𝐞 𝐏𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧 Dev, QA, and Staging environments ran on on-demand instances (24/7). We moved them to spot instances with scheduled shutdowns during non-working hours. That delivered 18-22% savings instantly. 3. 𝐀𝐮𝐭𝐨𝐬𝐜𝐚𝐥𝐞𝐫𝐬 𝐖𝐞𝐫𝐞 𝐌𝐢𝐬𝐜𝐨𝐧𝐟𝐢𝐠𝐮𝐫𝐞𝐝 𝐨𝐫 𝐉𝐮𝐬𝐭 𝐈𝐝𝐥𝐞 Most teams rely purely on CPU-based HPA, which reacts too late. We introduced custom scaling triggers based on business KPIs like request queue lengths, job backlogs, and latency. The result? Clusters scaled proactively, not reactively. 4. 𝐙𝐨𝐦𝐛𝐢𝐞 𝐏𝐨𝐝𝐬 𝐚𝐧𝐝 𝐅𝐨𝐫𝐠𝐨𝐭𝐭𝐞𝐧 𝐑𝐞𝐬𝐨𝐮𝐫𝐜𝐞𝐬 𝐄𝐯𝐞𝐫𝐲𝐰𝐡𝐞𝐫𝐞 One client had 300+ idle pods running outdated builds (nobody knew why). We implemented automated cleanup jobs using lifecycle policies and kubectl prune scripts. That reduced node count immediately. 5. 𝐕𝐞𝐫𝐭𝐢𝐜𝐚𝐥 𝐏𝐨𝐝 𝐀𝐮𝐭𝐨𝐬𝐜𝐚𝐥𝐞𝐫 (𝐕𝐏𝐀) 𝐖𝐚𝐬𝐧’𝐭 𝐄𝐯𝐞𝐧 𝐄𝐧𝐚𝐛𝐥𝐞𝐝 VPA handled unpredictable workloads far better than manual tuning.   For stateful apps with variable patterns, this reduced over-provisioning by up to 25% while maintaining SLAs. 6. 𝐏𝐞𝐫𝐬𝐢𝐬𝐭𝐞𝐧𝐭 𝐕𝐨𝐥𝐮𝐦𝐞 𝐂𝐥𝐚𝐢𝐦𝐬 (𝐏𝐕𝐂𝐬) 𝐖𝐞𝐫𝐞 𝐚 𝐁𝐥𝐚𝐜𝐤 𝐇𝐨𝐥𝐞 Storage costs were silently draining budgets. We audited PVC usage, downgraded unnecessary high-IOPS gp2 volumes to gp3, and cleaned up stale volumes. For one client, this alone saved over $30,000 annually. Before you buy another cloud cost management tool, ask yourself… Have you really optimized what you already own? ♻️ 𝐑𝐄𝐏𝐎𝐒𝐓 𝐒𝐨 𝐎𝐭𝐡𝐞𝐫𝐬 𝐂𝐚𝐧 𝐋𝐞𝐚𝐫𝐧.

After more of a decade of cleaning up Azure enviroments! here are some of my tips (outside of RI and rightsize SKUs which everyone else talks about is the common tips..) ✅ Check for Orphaned Resources (Disks, App Service Plans, SQL, PIP) Use the Orphaned Resources Workbook in Azure Monitor. ) Do some manual checks to see which workloads are actually being used. ✅ Workloads that are stopped but not deallocated? That means that hardware is still reserved and the cost is still running. Either deallocate or delete them. ✅ Check for Old snapshots of disks (So many old test and lab enviroments here) ✅ Check for backup tier (Do you need GRS?) or is ZRS, which is 40% cheaper good enough? ✅ If you do not need lower then 24 hours RTO on Backup, use standard policy and not enhanced. (While enhanced is the only one supported for Premium v2 disks but is a lot more expensive) ✅ What kind of redundancy do you need on your storage? LRS/ZRS/GRS? Adding GRS provides higher redundancy but higher cost and latency. Use LRS on active storage and use higher-level on backup data. ✅ Check for disk type (Many disks can be configured with SSDv2 tier which can be cheaper and faster in many cases) ✅ Check for which logs are actually needed? A AKS Cluster alone can generate close to 23 GB a month without little workloads. Unfortunately few have a good strategy around logs. What is needed and why? Just by disabling kube-admin can save you much. This applies for all services and workloads you have. ✅ Check for which logs are needed for security? By enabling Sentinel on a Log Analytics workspace you are paying for the data ingested and not how many analytics rules you have. So much logs are just being collected without being used. ✅ Do you need all Defender for Cloud SKUs enabled? Defender for Storage has a cost of 10$ per account. Make sure you use Defender for Cloud service where it matters! production workloads) ✅ Can you use Workspaces with API Management instead of having multiple production API Management instances? ✅ DDoS on IP instead of Network. In most cases you require DDoS protection on certain external services but not everything. Using IP based protection is a lot cheaper compared to Network based protection. ✅ What kind of storage do you need? Azure has different NFS/SMB based storage options providing much of the same capabilities such as Azure Files and Azure NetApp files, but there is a high cost difference between them. ✅ Do you need Private Endpoints? (Cost per PE, Cost bandwidth cost, Cost VNET Peering) or can services (storage) be locked down using Service Endpoint with or without policies? ✅ Is LicenseType = "Windows_Client" set on AVD machines? It does it automatically using the portal, but not via Terraform/Bicep. This ensures that you do not pay for Windows license for AVD workloads. ✅ Standalone or centralized services? I see so often redundant instances APIM, WAF, NAT, Backup vaults, try and avoid redundant services

HubSpot saved millions in AWS S3 storage costs because of this simple shift by their backend performance team. Here’s exactly how they did it.  1. Identifying the Cost Problem - The Backend Performance Team at HubSpot focused on optimizing costs by analyzing cloud spending, specifically in AWS S3 storage.   - They discovered that S3 storage accounted for 45-50% of daily cloud costs.   - Two primary cost drivers:     1. Raw JSON logs (~31 petabytes of request logs).   2. Compaction lag: Only 30% of logs were being converted to ORC format due to bottlenecks.  2. Hypothesis for Savings - Compressing all logs to Optimized Row Columnar (ORC) format could reduce storage size by 95%.   - ORC was chosen because it provided better compression and was already supported by their existing infrastructure.   - They also identified TTL (time-to-live) discrepancies: Raw logs were stored for 730 days vs. ORC logs for 460 days, leaving room for optimization.  3. Redesigning the Logging Process - They reworked their pipeline to convert raw logs to ORC immediately during the staging phase to avoid JSON bloating. - Streaming conversion was implemented to process logs in real-time, ensuring better performance and reducing backlog.   - 140 workers were deployed to backfill existing 34.7 PB of JSON logs, converting them to 1.47 PB of ORC logs—achieving a 4.24% final storage size.  4. Execution & Results - The backfill process took 8 days, and 34.7 PB of logs were converted to ORC, reducing costs by seven figures (over $1 million).   - Monthly JSON log costs decreased by 55.7%, while ORC bucket costs increased by only 6.4% of the original JSON costs.   - Net Savings:     - One-time savings from the TTL reduction: 6 figures.     - Total yearly savings: 7 figures.   5. User Experience Impact - Engineers reported that query times dropped from 30 minutes to 36 seconds for high-throughput services due to ORC’s improved performance.   6. Key Takeaways - Cost-saving projects require revisiting assumptions and configurations (like TTL settings).   - HubSpot reduced storage costs and improved query performance, ensuring long-term scalability.   - Cost optimization isn’t just technical—regular audits of cloud usage can reveal hidden savings. This project shows how simple changes in data management like switching to ORC compression can yield massive financial and operational benefits.

𝗡𝗼𝗯𝗼𝗱𝘆 𝘁𝗮𝗹𝗸𝘀 𝗮𝗯𝗼𝘂𝘁 𝗰𝗼𝘀𝘁 𝘂𝗻𝘁𝗶𝗹 𝘁𝗵𝗲 𝗳𝗶𝗿𝘀𝘁 𝗰𝗹𝗼𝘂𝗱 𝗯𝗶𝗹𝗹 𝗮𝗿𝗿𝗶𝘃𝗲𝘀. In most data platforms, cost is treated as a finance problem. The architecture team designs the pipeline. The finance team reviews the bill 30 days later. By then, the decisions that drive 80% of the spend are already baked into production. Cost is not a billing category. It is a design constraint. 𝗪𝗵𝗲𝗿𝗲 𝗰𝗹𝗼𝘂𝗱 𝗰𝗼𝘀𝘁𝘀 𝗵𝗶𝗱𝗲: → Compute sizing. An always-on XL warehouse running queries that need a Medium. Nobody downsizes because nobody measures. → Storage sprawl. Snapshots, staging tables, and temp files that were never cleaned up. Data accumulates silently. → Over-scheduling. Pipelines running hourly when daily would meet the SLA (Ep 44). Every unnecessary run is compute you pay for and data nobody uses. → Scan waste. Full table scans on unpartitioned data. The query touches 500GB to return 5MB. Partitioning (Ep 22) and file format choices (Ep 21) directly reduce this. → Zombie resources. Dev clusters left running. Test environments that outlived their purpose. Resources nobody owns and nobody shuts down. 𝗪𝗵𝗮𝘁 𝗰𝗼𝘀𝘁-𝗮𝘄𝗮𝗿𝗲 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 𝗹𝗼𝗼𝗸𝘀 𝗹𝗶𝗸𝗲: → Right-size compute. Match warehouse size to workload. Auto-suspend when idle. → Tier your storage. Hot, warm, cold. Not everything needs fast access. → Align scheduling to SLAs. If the SLA is daily, run daily. Tighter schedules cost more and deliver marginal value. → Partition and compress. Reduce scan surface before optimizing queries. → Tag and own resources. If nobody owns it, nobody cleans it up. The cheapest compute is the compute you never run. If your architecture review doesn't include cost, your bill review will. Where is your biggest cloud cost hiding right now? #DataEngineering #FinOps #DataArchitecture

If I were Head of FinOps of a SaaS company, here’s my 4-step playbook to cut up to 20% off our cloud costs, avoid expensive vendor lock-in, and align my entire company on cloud spending: This playbook is simple, but you’d be surprised how much the basics can help transform your bottom line. Here’s my playbook: 1. Understand your workloads You need to know what workloads you’re running and whether they’re predictable or dynamic. - Predictable If you have workloads that don’t change a lot – as in, you can forecast cloud costs accurately — lock in volume discounts like reserved instances or savings plans. - Dynamic If you have no idea what the resource profile of certain workloads will look like,  say you’re innovating, stick with on-demand capacity. You don’t want to risk overcommitting to enterprise discount pricing (EDP). For instance, if your actual spend is $70M but you commit to $250M, that’s a painful conversation with the CFO waiting to happen. 2. Stop running your engine overnight Instances running 24/7 without being used are a hidden cost killer. Implementing automated scheduling systems to power down these instances during periods of inactivity can significantly reduce costs. It’s like turning off your electric car overnight so you can drive it the next day without recharging. This may be straightforward. But at scale, this simple change can free up a significant budget. 3. Attached storage waste Storage utilization is often overlooked. One of our customers had a petabyte-sized S3 bucket costing $10k per month – yet no one knew what it was for. Right size your instances and audit storage usage regularly. Otherwise, you’re wasting resources like using a tank to kill a rat. 4. Make cost management a KPI Cloud cost visibility must be a company-wide priority – a top-level KPI so everyone knows they’re accountable. Focusing on this can lead to up to20% savings as people start paying attention to what’s being spent and why. Final thoughts: Cloud cost management is like fitness: every day counts. You won’t see the results immediately, but your expenses will balloon without consistent effort. Start today, focus on the basics, and watch your costs shrink over time. Pay now or pay later – the choice is yours.

Imagine you’re filling a bucket from what seems like a free-flowing stream, only to discover that the water is metered and every drop comes with a price tag. That’s how unmanaged cloud spending can feel. Scaling operations is exciting, but it often comes with a hidden challenge of increased cloud costs. Without a solid approach, these expenses can spiral out of control. Here are important strategies to manage your cloud spending: ✅ Implement Resource Tagging → Resource tagging, or labeling, is important to organize and manage cloud costs. → Tags help identify which teams, projects, or features are driving expenses, simplify audits, and enable faster troubleshooting. → Adopt a tagging strategy from day 1, categorizing resources based on usage and accountability. ✅ Control Autoscaling → Autoscaling can optimize performance, but if unmanaged, it may generate excessive costs. For instance, unexpected traffic spikes or bugs can trigger excessive resource allocation, leading to huge bills. → Set hard limits on autoscaling to prevent runaway resource usage. ✅ Leverage Discount Programs (reserved, spot, preemptible) → For predictable workloads, reserve resources upfront. For less critical processes, explore spot or preemptible Instances. ✅ Terminate Idle Resources → Unused resources, such as inactive development and test environments or abandoned virtual machines (VMs), are a common source of unnecessary spending. → Schedule automatic shutdowns for non-essential systems during off-hours. ✅ Monitor Spending Regularly → Track your expenses daily with cloud monitoring tools. → Set up alerts for unusual spending patterns, such as sudden usage spikes or exceeding your budgets. ✅ Optimize Architecture for Cost Efficiency → Every architectural decision impacts your costs. → Prioritize services that offer the best balance between performance and cost, and avoid over-engineering. Cloud cost management isn’t just about cutting back, it’s about optimizing your spending to align with your goals. Start with small, actionable steps, like implementing resource tagging and shutting down idle resources, and gradually develop a comprehensive, automated cost-control strategy. How do you manage your cloud expenses?

How I Cut Cloud Costs by $300K+ Annually: 3 Real FinOps Wins When leadership asked me to “figure out why our cloud bill keeps growing Here’s how I turned cost chaos into controlled savings: Case #1: The $45K Monthly Reality Check The Problem: Inherited a runaway AWS environment - $45K/month with zero oversight My Approach: ✅ 30-day CloudWatch deep dive revealed 40% of instances at <20% utilization ✅ Right-sized over-provisioned resources ✅ Implemented auto-scaling for variable workloads ✅ Strategic Reserved Instance purchases for predictable loads ✅ Automated dev/test environment scheduling (nights/weekends off) Impact: 35% cost reduction = $16K monthly savings Case #2: Multi-Cloud Mayhem The Problem: AWS + Azure teams spending independently = duplicate everything My Strategy: ✅ Unified cost allocation tagging across both platforms ✅ Centralized dashboards showing spend by department/project ✅ Monthly stakeholder cost reviews ✅ Eliminated duplicate services (why run 2 databases for 1 app?) ✅ Negotiated enterprise discounts through consolidated commitments Impact: 28% overall reduction while improving DR capabilities Case 3: Storage Spiral Control The Problem: 20% quarterly storage growth, 60% of data untouched for 90+ days in expensive hot storage My Solution: 1, Comprehensive data lifecycle analysis 2, Automated tiering policies (hot → warm → cold → archive) 3, Business-aligned data retention policies 4, CloudFront optimization for frequent access 5, Geographic workload repositioning 6, Monthly department storage reporting for accountability Impact: $8K monthly storage savings + 45% bandwidth cost reduction ----- The Meta-Lesson: Total Annual Savings: $300K+ The real win wasn’t just the money - it was building a cost-conscious culture** where: - Teams understand their cloud spend impact - Automated policies prevent cost drift - Business stakeholders make informed decisions - Performance actually improved through better resource allocation My Go-To FinOps Stack: - Monitoring: CloudWatch, Azure Monitor - Optimization: AWS Cost Explorer, Trusted Advisor - Automation: Lambda functions for policy enforcement - Reporting: Custom dashboards + monthly business reviews - Culture: Showback reports that make costs visible The biggest insight? Most “cloud cost problems” are actually visibility and accountability problems in disguise. What’s your biggest cloud cost challenge right now? Drop it in the comments - happy to share specific strategies! 👇 FinOps #CloudCosts #AWS #Azure #CostOptimization #DevOps #CloudEngineering P.S. : If your monthly cloud bill makes you nervous, you’re not alone. These strategies work at any scale.

Most teams assume reducing cloud costs means sacrificing performance. This case proves otherwise. A growing SaaS company was struggling with rising infrastructure costs, touching nearly $18K/month. Alongside this, their Kubernetes clusters were over-provisioned, and CI/CD pipelines were inefficient—causing unnecessary compute usage and slower deployments. The approach was simple but strategic. First, infrastructure was optimized by right-sizing resources, enabling autoscaling, and leveraging spot instances. Next, CI/CD pipelines were enhanced using caching and parallel execution, significantly reducing build times. Finally, cost visibility was introduced through monitoring dashboards and alerting systems. The impact was immediate and measurable. Cloud costs dropped by 38%, bringing expenses down to around $11K/month. Deployment speeds doubled, and teams gained real-time visibility into their infrastructure spend. The biggest takeaway? Cloud waste isn’t just a technical issue—it’s a visibility and ownership problem. When teams understand where resources are being used, optimization becomes natural. If your cloud bill is scaling faster than your product, it’s time to rethink your architecture—not your budget. #CloudComputing #DevOps #AWS #Kubernetes #CostOptimization #SRE #Infrastructure #TechLeadership #CI_CD #StartupTech

⚡ Smart tier is now GA: What if Azure Storage could optimize its own cost automatically? One of the most frustrating parts of storage management is trying to predict what data should stay hot, what should move to cool, and what can safely go colder over time. That’s why this Smart tier announcement is worth a look. Microsoft has now made Smart tier generally available for Azure Blob Storage and Azure Data Lake Storage. It is a fully managed capability that continuously evaluates object access patterns and automatically moves data across the hot, cool, and cold tiers to keep storage costs aligned with actual usage. ✅ Less manual lifecycle tuning As data estates grow, lifecycle rules get harder to maintain. Smart tier is designed to reduce that operational effort by handling tiering automatically. ✅ Built around real access patterns Frequently accessed data stays hot. Inactive data moves to cool after 30 days, then to cold after another 60 days. If the object is accessed again, it is promoted back to hot and the cycle restarts. ✅ Useful for both Blob and Data Lake scenarios This is not just for one storage use case. Microsoft says Smart tier supports both Azure Blob and Data Lake Storage, and is available in nearly all zonal public cloud regions. One detail I found especially interesting: since the feature’s preview launch, Microsoft says more than 50% of Smart-tier-managed capacity has already shifted to cooler tiers automatically based on actual access patterns. That gives a good sense of how much storage estates can drift away from their ideal cost profile over time. For architects, platform teams, and operations teams, this is a very practical improvement: less guessing, less policy maintenance, and better cost alignment without constant manual intervention. 👉 Worth saving if you manage large Azure storage estates or data platforms. https://lnkd.in/gjprGiSk #Azure #AzureTipOfTheDay #AzureMissionCritical #MSAdvocate #AzureStorage #BlobStorage #DataLake #FinOps #CloudArchitecture #CloudOperations

𝗘𝘅𝗰𝗶𝘁𝗶𝗻𝗴 𝗻𝗲𝘄𝘀 𝗳𝗼𝗿 𝗔𝘇𝘂𝗿𝗲 𝘀𝘁𝗼𝗿𝗮𝗴𝗲 𝗽𝗿𝗼𝘀: 𝗦𝗺𝗮𝗿𝘁 𝗧𝗶𝗲𝗿 𝗶𝘀 𝗻𝗼𝘄 𝗴𝗲𝗻𝗲𝗿𝗮𝗹𝗹𝘆 𝗮𝘃𝗮𝗶𝗹𝗮𝗯𝗹𝗲 𝗳𝗼𝗿 𝗔𝘇𝘂𝗿𝗲 𝗕𝗹𝗼𝗯 𝗦𝘁𝗼𝗿𝗮𝗴𝗲 𝗮𝗻𝗱 𝗗𝗮𝘁𝗮 𝗟𝗮𝗸𝗲 𝗦𝘁𝗼𝗿𝗮𝗴𝗲, 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗻𝗴 𝗰𝗼𝘀𝘁 𝗼𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝗯𝘆 𝗰𝗼𝗻𝘁𝗶𝗻𝘂𝗼𝘂𝘀𝗹𝘆 𝗲𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗻𝗴 𝗮𝗰𝗰𝗲𝘀𝘀 𝗽𝗮𝘁𝘁𝗲𝗿𝗻𝘀 𝗮𝗻𝗱 𝘀𝗵𝗶𝗳𝘁𝗶𝗻𝗴 𝗱𝗮𝘁𝗮 𝘁𝗼 𝘁𝗵𝗲 𝗿𝗶𝗴𝗵𝘁 𝘁𝗶𝗲𝗿 (𝗵𝗼𝘁, 𝗰𝗼𝗼𝗹, 𝗰𝗼𝗹𝗱) 𝘄𝗶𝘁𝗵𝗼𝘂𝘁 𝗺𝗮𝗻𝘂𝗮𝗹 𝗹𝗶𝗳𝗲𝗰𝘆𝗰𝗹𝗲 𝗿𝘂𝗹𝗲𝘀. Since its preview launch at Ignite 2025, over 50% of managed capacity has auto-moved to cooler tiers, slashing costs for large data estates while preserving performance for hot data. 𝗞𝗲𝘆 𝗕𝗲𝗻𝗲𝗳𝗶𝘁𝘀  • Zero operational overhead—no rules to design or maintain.  • Simplified billing: No transition/retrieval fees; just a small monthly monitoring fee per object.  • Easy setup: Enable as default tier on new/existing zonal-redundant accounts via Portal or API. How to Get Started   1. Create a storage account and select "Smart Tier" as default access tier.  2. For existing accounts, switch blob service default to Smart Tier.  3. Let Azure handle the rest—continuous optimization kicks in automatically. Perfect for evolving workloads. Who's enabling it? Check the full blog: https://lnkd.in/gkKmpniX #AzureStorage #CloudCostOptimization #AzureBlob #SmartTier