Well-known public cloud platforms provide tools for monitoring usage and raising alerts if thresholds are crossed. These should be used to monitor cloud costs. There are many other ways to balance cost and performance effectively. For example, spot instances can offer up to 90% cost savings for non-critical workloads. Reserved instances should also be considered when long-term planning as they are known to provide up to 50-60% cost savings without compromising performance. One should also consider creating smaller dev/test environments on their own laptops, desktops, and low-powered local servers. They can use a mix of VMs and containers, resulting in a lot of cost savings while having a readily available dev/test environment.

Identify resources that are not utilised and optimise Review invoices to find unused or unattached resources. be it virtual machines or storage or snapshots Avoid unnecessary data transfers, as moving data to and from a public cloud can be expensive.  Use cloud cost optimization dashboards Visualize cost trends, usage patterns, and performance metrics to make informed decisions. Automate cloud spend optimization Use automation to identify, review, and monitor cost-optimization opportunities.  Use Reserved Instances and Savings Plans These long-term commitments from cloud providers can result in lower prices Monitor cost anomalies Analyze cloud expenditure trends to spot any unusual changes in cloud costs.  Use appropriate storage options

Understanding your business and customer needs will play a crucial role once you understand the tech stack and architecture to make an informed decision between trade offs you are willing to take in order to optimize cost and/or performance. Right sizing applications has to the basic practice and making use of autoscaling polices to scale out and scale in applications. Use AI/ML for predictive scaling instead of reacting to sudden spikes, this will decrease the performance trade off of and will help keeping the costs in check. Re-architect where possible. See if you can use server-less architecture for functions or applications which need not be running 24/7. Along with edge processing capabilities.

In my experience, balancing cloud costs and performance often requires a strategic approach combining foresight and adaptability. First, adopt a FinOps mindset—collaboration between IT, finance, and business units is key to aligning cost management with performance goals. Second, embrace reserved or spot instances for predictable workloads, while leveraging auto-scaling for flexibility. Third, evaluate multi-cloud or hybrid-cloud setups to avoid vendor lock-in and optimize pricing. Proactively implementing cloud governance policies ensures accountability and prevents waste. Regularly revisit workload requirements to ensure ongoing alignment. The call to action? Transform cost control into a competitive advantage through cloud transparency.

Cloud cost control is all about strategy and continuous optimization. Start by aligning your infrastructure with actual business needs - right-size resources, automate scaling, and eliminate unused services. Regular monitoring with tools like Azure Advisor or AWS Cost Explorer can pinpoint inefficiencies. Prioritize workload optimization, ensuring high performance for critical apps while leveraging spot instances or reserved capacity for cost savings. Finally, embrace cost management tools to forecast, track, and refine expenses. It's a dynamic process that balances efficiency with innovation. Cloud efficiency isn’t just about saving money; it’s about creating value without compromise.

1. Planning and Testing Ahead: Analyze historical data and predict future usage to avoid over-provisioning or under-provisioning resources; Conduct performance tests to understand how your applications behave under different loads. This helps in identifying the optimal resource allocation. 2. Use Scalable Resources: Implement auto-scaling to adjust resources based on real-time demand. This ensures you only pay for what you use; Continuously monitor and adjust the size of your instances to match the workload requirements. 3. Cost Management Tools: Utilize tools like AWS Cost Explorer, Azure Cost Management, etc. to predict and monitor expenses; Set up alerts to notify you when spending exceeds predefined thresholds. (Write with Copilot)

To balance cloud cost and performance, focus on optimizing resource usage. Start by rightsizing instances, auto-scaling based on demand, and using spot instances or reserved instances where feasible. Use time based scaling if your application has an identifiable pattern. Implement cost monitoring tools to track usage patterns and eliminate underutilized resources. Use managed services like RDS or Lambda to offload infrastructure management. Compress data, leverage content delivery networks (CDNs), and cache where possible. Finally, review your cloud architecture regularly to ensure it aligns with both cost-efficiency and performance goals.

Right-Size Resources: Allocate only what you need and scale up/down dynamically. Budgeting Tools: Set spending limits and track real-time costs. Auto-Scaling: Adjust resources during peak times automatically. Reserved Instances: Use discounts for predictable workloads. Optimize Storage: Use high-performance storage for critical data and cheaper options for archival. Monitor & Automate: Track usage and automate performance tuning. Leverage Expert Support: Get tailored recommendations to minimize costs without sacrificing performance. Jotelulu simplifies optimization for SMBs, helping you manage costs while ensuring high performance.

Cloud cost is one of the most common topics when we're dealing with Cloud Migracions or Cloud context. To balance cost and performance, initially we need to have full understanding of the non-functional requisites of the project, followed by a detailed tracking of the cloud costs. Then, depending the project contexto, you should search for a trade-off between velocity/capatity and costs, or search for strategies of cost optimization, specialy when dealing with cloud storage.