How Cloud Services Help Startups Optimize Costs

A modernization journey to Cloud Native has #cost benefits. #Cloud-native container environments are typically more cost-effective than VM-based environments due to better resource utilization, scalability, and automation features. Resource Utilization: #Containers: Containers generally use fewer resources than VMs because they share the host OS, resulting in less overhead. This allows running more applications on the same hardware, reducing overall costs. VMs: Each VM requires a full OS installation, leading to higher overhead and resource consumption. This results in fewer applications per host and potentially higher costs. #Pricing Models: AWS and Azure both offer pay-as-you-go models, but containers can be run on services like AWS ECS or EKS and Azure AKS, where resources scale dynamically based on demand, leading to cost savings. VMs are generally priced by size (vCPU, memory) and duration of use, leading to more predictable but often higher costs due to unused, idle capacity. #Scalability and Elasticity: Containers: Both #AWS Fargate and #Azure Kubernetes Service (AKS) support autoscaling, allowing containers to scale in real-time, optimizing cost efficiency by only using resources when needed. VMs: While VMs can be manually scaled or automatically through certain cloud services, they are slower to scale and often over-provisioned, leading to increased costs. #Maintenance Costs: Containers: Offer a serverless container option (e.g., AWS Fargate, Azure Container Instances) that offloads infrastructure management, potentially lowering operational costs. VMs: Require more effort in management, patching, and monitoring, increasing operational overhead and costs. #Cost Comparison (AWS and Azure): AWS: For example, running a t3.medium EC2 instance costs approximately $0.0416 per hour, whereas running a container using AWS Fargate can start as low as $0.0126 per hour (for compute and memory). Azure: Similarly, a D2_v3 VM instance costs around $0.096 per hour, while Azure Container Instances might cost $0.000012 per GB and $0.000012 per vCPU per second, offering more granular billing and potential savings. Actionable Steps & Risks: #Analyze Workloads: For optimal cost efficiency, assess whether your workloads can benefit from containerized environments, especially for microservices or stateless applications. #Use Autoscaling: Implement autoscaling strategies for containers to dynamically adjust resource consumption based on real-time demand. #Monitor Hidden Costs: While containers reduce resource consumption, factor in networking, storage, and data transfer costs, which can vary depending on the cloud provider and setup. #Risk Mitigation: For mission-critical applications, ensure that the container management platform has robust monitoring, security, and backup strategies to avoid potential downtime or security breaches.

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?

We helped ClickUp reduce its cloud cost by 20%. As CEO of Yotascale, here’s my take on how we did it and what it meant to the ClickUp team: The first step? Visibility. As the engineering manager said, “Yotascale gave us laser focus on regional and platform service cost optimization.” Without visibility, you can’t measure costs; without measurement, optimization is impossible. We helped simplify what seemed complex by breaking down their cloud spending across teams and services. Yotascale helped draw lines of accountability and ownership for ClickUp. But visibility alone isn’t enough. Ultimately, what made our collaboration work was ClickUp’s culture of accountability. See, culture isn’t something we inject. Our product can only empower companies that are cloud cost-conscious, empower teams to take ownership of their cloud costs, and see cloud cost as a KPI. In addition, ClickUp was an early adopter of Yotascale's Yota Copilot, our GenAI-powered tool designed specifically for FinOps. The same engineering manager shared how it reduced the time he spent on cloud cost management from 10 hours a week to just 20 minutes. That’s 9 hours and 40 minutes freed up for high-value tasks. Nobody else in the market is doing that today. Finally, it’s about scaling with confidence. Their team described Yotascale as “the only solution tough enough to handle our sprawling enterprise cloud.” We helped ClickUp when they were growing fast—new teams, services, applications and more. By giving them X-ray vision into every corner of their tech stack, we helped them shift from being *reactive* to *proactive*. No more knee-jerk reactions like, “You’re spending too much, cut costs in half.” Instead, we enabled them to understand why high cloud spend happens and leave room to address it thoughtfully. A culture of collaboration replaced friction. ClickUp is now a shining example of strategic FinOps. They’ve moved past reactive decisions, adopted self-service tools, and built a collaborative approach to cloud cost management that saves money and time. The result? 20% savings on cloud costs.

What'd happen with 2.1 B unnecessary API calls? It will be a STRUGGLE to manage it. Cloud costs will break the roof. Even then, Duolingo solved it. Here's what they did 👇 Background: Duolingo was wasting millions in unnecessary API calls. It was happening as features like stories, adventures, and DuoRadio scaled. Instead of patching up the problem, they reimagined their cost management strategy entirely, turning a challenge into an opportunity for efficiency. But the journey wasn’t without hurdles; they faced significant challenges: 1️⃣ Legacy systems waste resources. 2️⃣ Overprovisioning caused by poor defaults. 3️⃣ Staging environments costlier than production. So, what did Duolingo do? 🔹 Decommissioned unused resources: They eliminated outdated clusters, unused databases, and redundant microservices from deprecated features, reducing waste and reallocating resources to active workloads. 🔹 Enabled full cost visibility with CloudZero: Duolingo broke down cloud costs into queryable components, uncovering inefficiencies like staging environments costing more than production and identifying critical optimization opportunities. 🔹 Right-sized and optimized performance: They fine-tuned configurations for 90-95% memory utilization, migrated databases to a cloud-native, serverless data platform, and leveraged on-demand resources to maximize efficiency. The results? ✅ Service-to-service traffic dropped by 60% ✅ 20% reduction in cloud costs within months. ✅ Hundreds of thousands saved annually from optimizing a single service. The bottom line: When optimizing cloud infrastructure, focus on building visibility, cleaning up tech debt, and right-sizing resources. #AI #Duolingo #CaseStudy #Simform #GenAI P.S. 💡 In yesterday’s newsletter, I covered how Duolingo reimagined its cost management strategy. Subscribers get access to: - Product engineering insights. - Proven development strategies. - Latest Azure & Gen AI trends. Check it out! Link in comments ⬇️