What are the risks of over-optimizing cloud costs?

I think the main thing you should be careful about when it comes to optimizing your cloud spend is to not let it come at the expense of your availability, reliability and customer experience. These goals should always trump being frugal with your cloud costs.

When it comes to solve any optimization problem, time and space are the two main factors that take the highest priority. Cost optimization is a by-product of it. I think in cloud computing case, risk of optimization can potentially be a costly affair since design is to provide flexibility to meet most widely followed use cases. One should consider current use cases and future need to scale those uses cases while going for optimization. Doing optimization beyond this is overkill from R&D and performance outcome perspective. So main risks of over-optimization of cloud costs would be, 1) Cost of possible service discontinuity 2) Landing in higher cost over span of service deployment 3) Increase in governance complexity 4) Suboptimal R&D

Good cost optimisation considers concerns across cost, security, business need, regulatory compliance, team skill set, maintainability, application and infrastructure performance and reliability needs. Considering costs in isolation can reduce business and IT teams’ ability to operate optimally.

Cloud cost optimization is brilliant but over-optimization of this said cost is a delicate trigger. It could be beneficial depending on the prevailing infrastructure but more often than not, it is damaging as it leads the way for significant infra limitations that don't scale with the processes.

Over optimization comes at the cost of decreased flexibility and potential service outages. Over optimizing costs might result in compromising on service availability in one way or the other which may turn out to be more expensive in the long run

More than a risk, it is a reality for many companies today, which to save a little USD change the characteristics of their infrastructure for one of lower value without taking into account performance, concurrency, latency, availability, reliability, auto-scaling , etc.

Instance size, bandwidth, etc. form one angle of cost optimization, which can in fact be handled if there is scope for further code level optimisation. The challenge comes when availability is compromised over cost. If an application has to be highly available, the compute and storage have to be made multi-region. This brings a fairly large bill along with it.

This is a real risk, and that can be avoided by activly monitoring the performance and experience of the users. Sticking to numeric KPIs and fast-mitigating them with a sense-of-urge will minimize the mal effect of over optimization.

User experience is critical and drives company revenue in most companies. Establishing the right level of performance and availability that meets customer needs is needed to strike the right balance. A typical path to do this is to map out the customer journey and set Service Level Objectives (SLOs) at p95 and p99 levels for latency and availability/error at key points in the journey. This helps determine the right level of cloud resources needed to operate the service. Some organizations also track Failed Customer Interactions *(FCIs) which are a more customer-centric way of approaching availability than pure error rates.

I know such problems from my daily work and can only advise carrying out the performance optimization step by step in order to observe the indications in the meantime.

It's also important to watch for diminishing returns. At some point, the ROI becomes more questionable, as the effort to reduce costs may overshadow the actual reduction. For example, if it takes 6 person-months to save 50K annually, the optimization will pay for itself only after several years. There is also the factor of the migration costs (if relevant), and lost opportunity, as people working on cost reduction could have worked elsewhere. In short, it's important to have ROI in mind, and know when to stop.

En lo posible, es una buena estrategia tener una reserva para invertir en innovación ya que no sólo te diferencia de la competencia, puede llegar a incrementar de manera exponencial los ingresos si aumenta la demanda como tendencia y aumenta la posibilidad de darnos autoridad si logra un impacto evolutivo.

A third risk of over-optimizing cloud costs is the potential to overlook opportunities and hinder innovation that could enhance your software design and value proposition. Avoiding newer, advanced cloud services, neglecting user and stakeholder feedback, or settling for cheaper but outdated solutions may trim expenses in the short term. However, this approach can constrain your software's potential, impede differentiation, and compromise competitiveness in the long run.

Innovations are particularly important in economically / politically difficult times like these. This should always be kept in mind when optimizing in the cloud context.

Suppose costs limit your ability to experiment or experiment safely. In that case, you could miss out on some real innovation that could drive your project forward in new ways that could gain more customers or revenue.

Over optimization is kind of rare case. I would rather say over engineering. If to give simple analogy: customer wants a home, but receives Windsor castle. Then suddenly customer may get something, he is not ready for. As outcome, one or two rooms of castle are used, while other parts of the castle decay.

Start with a good understanding of where your unit costs need to be and share this with the product and engineering team. The unit should be related to the value produced by the system. Say you build a supply chain optimization system. The unit of measure might be pieces shipped per day. You can always throw more compute at the problem to optimize a supply chain network. But if you can set a target unit cost, the engineering teams can optimize towards it (and stop to avoid over-optimization). While not simple, I found this approach doable, as product teams can often come up with a revenue per unit they consider meaningful.

This provides a comprehensive and strategic approach to navigating the complex landscape of cloud cost optimization. The emphasis on finding a balance between cost optimization goals and software design objectives is particularly insightful. Moreover, the article encourages a forward-thinking mindset by suggesting the exploration of new or alternative cloud services, tools, or frameworks. The article strikes a chord by reinforcing that effective cloud cost management goes beyond immediate gains; it's about building resilience and agility into the software architecture. Overall, a well-articulated (initial) guide for organizations seeking a nuanced approach to cloud cost optimization.

"Premature optimization is the root of all evil" is a popular saying among Software Developers. I would imagine this question has similar premise. In my experience, I have seen "central" teams proactively control the deployment pipeline in order to optimize cloud costs, and restrict which services developers can use. This can lead to stalled innovation and frustration among developers. Instead, few suggestions are: - Have a good understanding of the workload in terms of business value (example: Will throttling an API lead to bad end user experience/revenue impact?) - Estimate appropriate budget guardrails - Leverage Cost Optimization tools - Empower your developers by providing cloud cost training and give them ownership

Leverage cloud cost optimization best practices such as choosing the appropriate service level, utilizing reserved or spot instances, and implementing autoscaling and load balancing. Apply tags and policies to streamline resource management. Regularly review cost optimization outcomes, comparing them to software design criteria and metrics, identifying any gaps or issues resulting from cost decisions. Lastly, foster innovation by experimenting with new cloud services, tools, or frameworks. Assess their potential to enhance software design and value proposition, carefully weighing costs and benefits against existing solutions. This holistic approach ensures cost efficiency without compromising the integrity and innovation of your software.

Another aspect that may come into play is that your solution may become too complex to maintain due to these optimizations. All kind of unforeseen side-effects can happen during this process, which can destabilize the solution.

Cloud optimisations shud not be done in specific to cloud. For ex: Taking the VM type which is only available in AWS to reduce the cost might impact tomorrow if you want to scale to multi clouds. Also cloud optimisations shud not impact availability and resilience of the system as you need to draw an equation of minimalist configuration required to have HA(High Availability) and then play beyond. Storage being less cost than compute, it's very common to focus more on compute optimisations but storage generally shall have more scopre of optimisations which will yield ROI in long term unlike compute which will be immediate.

Another perspective is that you might foster a company culture with a cost reduction mindset rather than a value adding one and in doing that totally missing out on the creative power of the personnel and consequently, by doing that, failing in fulfilling the vision and potential of the product.

Optimization often comes at a cost of increased complexity and worse maintainability. It's important to remember that the hours a team has to spend on keeping the system alive are hours they are not putting into creating new features. Even if you save a decent chunk of money on your monthly bill, you might be paying for having a few employees dealing with keeping the system running because you decided to save on monitoring tools or chose an architecture that adds potential failure points. Onboarding time and costs will also increase in a more complex system, leading to growth pains when you want to take your organization to the next level.

Depending on the complexity of the software solution, one may need to consider tool such as load testing ones so that we can gauge better with a multiplier Of x years. At the same time, it really depends on the software target audience, their appetite on risk and downtime etc. if there is a certain appetite or risk allowed, optimisation more may not be too bad either as the cost in cloud is really getting too high. I even know people who ended up going back to self host. An issue that our cloud companies better reflect more.