{"id":288953,"date":"2025-06-11T23:07:21","date_gmt":"2025-06-11T23:07:21","guid":{"rendered":"https:\/\/peraltafinancing.com\/uncategorized\/overengineering-microservices-how-we-cut-cloud-costs-by-82\/"},"modified":"2025-06-11T23:07:21","modified_gmt":"2025-06-11T23:07:21","slug":"overengineering-microservices-how-we-cut-cloud-costs-by-82","status":"publish","type":"post","link":"https:\/\/fivemor.com\/?p=288953","title":{"rendered":"Overengineering Microservices: How We Cut Cloud Costs by 82%"},"content":{"rendered":"


\n<\/p>\n

\n

When microservices are overused, complexity and costs skyrocket. Here\u2019s how we consolidated 25 services into 5 \u2013 simplifying architecture and slashing cloud spend without sacrificing stability.<\/strong><\/p>\n

It\u2019s hard to predict exactly how microservice architecture will evolve, what pros and cons will surface, and what long-term impact it will have. Microservices can offer significant benefits \u2014 like scalability, independent deployments, and improved fault isolation \u2014 but they also introduce hidden challenges, such as increased complexity, communication overhead, and maintenance costs.<\/p>\n

While this architectural approach brings flexibility in managing systems, prioritizing critical components, and streamlining release and testing processes, it won\u2019t magically fix everything \u2014 architecture still needs to make sense. Applying the wrong architecture can create more problems than it solves. Poorly designed microservices may lead to inefficiencies, tight coupling in unexpected places, and operational overhead that outweighs their advantages.<\/p>\n

Entry point: reclaiming architectural simplicity<\/h2>\n

The project we took on was an example of microservice architecture applied without tailoring it to the actual shape and needs of the system. Relatively small and simple applications were over-decoupled. Not only were different modules and domains split into separate services, but even individual layers \u2014 such as REST API, services containing business logic, and database repositories \u2014 were extracted into separate microservices. This is a classic case of solving a simple problem with a complex tool, without adapting to the context.<\/p>\n

Our mission was to refactor the system<\/a> \u2014 not just at the code level, but at the architectural level \u2014 with a primary focus on reducing the long-term maintenance costs. To achieve this, we\u2019ve decided to retain the microservice approach, but with a more pragmatic level of granularity. Instead of 25 microservices, we consolidated the system into just 5 thoughtfully grouped services, reduced cache instances from 3 to 1 and migrated 10 databases into 5.<\/p>\n

Consulting the system<\/h2>\n

Before making any decisions, we conducted a thorough audit of the system\u2019s architecture, application performance, efficiency, and overall cost. Looking at the raw architectural diagram alone is rarely enough \u2014 we wanted to observe the system in action and pay close attention to key metrics. This live analysis provided critical insights into configuring the new applications to better meet the system\u2019s original requirements while reducing operational costs.<\/p>\n

Cloud Provider access<\/h3>\n

To truly understand a system\u2019s architecture, it\u2019s essential to have access to the cloud provider\u2019s environment \u2014 with a wide set of permissions. This level of visibility pays off significantly. The more detailed your understanding at this stage, the more opportunities you uncover for optimization and cost savings during consolidation.<\/p>\n

Monitoring tools access<\/h3>\n

Most systems include monitoring tools to track their health and performance. These insights help identify which metrics are most critical for the system. Depending on the use case, the key factor might be computing power, memory usage, instance count, or concurrency. In our case, we discovered that some microservices were being unnecessarily autoscaled. CPU usage was rising \u2014 not due to a lack of resources, but because of accumulating requests in the next microservices in the chain that performed heavy calculations and interacted with external APIs. Understanding these patterns enabled us to make informed decisions about application container configurations and auto scaling strategies.<\/p>\n

Refactoring, consolidating, and optimizing cloud architecture<\/h2>\n

We successfully consolidated 25 microservices into 5 independent, self-sufficient applications, each backed by one of 5 standardized databases \u2014 down from a previously fragmented set of 10 and a single cache instance instead of 3. Throughout this transformation, we stick to a core refactoring principle: system inputs and outputs must remain unchanged. Internally, however, architecture and data flow were redesigned to improve efficiency and maintainability.<\/p>\n

We carefully defined domain boundaries to determine which services could be merged. In most cases, previously separated layers \u2014 REST proxies, service logic, and repositories \u2014 were brought together in an unified application within a single domain. Some applications required database migrations, resulting in consolidated databases structured into multiple schemas to preserve legacy boundaries.<\/p>\n

Although we estimated resource requirements for the new services, production behavior can be unpredictable \u2014 especially when pre-launch workload testing isn\u2019t possible. To stay safe, we provisioned a performance buffer to handle unexpected spikes.<\/p>\n

While cost reduction was our main goal, we knew we were dealing with customer-facing apps where stability and user experience come first. That\u2019s why we took a safe and thoughtful approach \u2014 focusing on smart consolidation and optimization without risking reliability. Our goal wasn\u2019t just to cut costs, but to do it in a way that also improved the system without impacting end-users.<\/p>\n

Challenges and risks of architecture refactoring<\/h2>\n

Limited business domain knowledge<\/h3>\n

It\u2019s a tough challenge when you\u2019re working with applications and domains without deep insight into the business logic. On one hand, it wasn\u2019t strictly required since we were operating on a higher architectural level. But every time we needed to test and fix issues after consolidation, we had to investigate from scratch \u2014 often without clear guidance or domain expertise.<\/p>\n

Lack of testing opportunities\u200b<\/h3>\n

In maintenance-phase projects, it\u2019s common that dedicated QA support or testers with deep system knowledge aren\u2019t available \u2014 which is totally understandable. At this point, we often rely on the work done by previous developers: verifying what types of tests exist, how well they cover the code and business logic, and how effective they are at catching real issues.<\/p>\n

Parallel consolidation limitations<\/h3>\n

The original system\u2019s granularity made it difficult for more than one developer to work on consolidating a single microservice simultaneously. Typically, each domain was handled by one developer, but in some cases, having multiple people working together could have helped prevent issues during such a complex process.<\/p>\n

Backward compatibility\u200b<\/h3>\n

Every consolidated application had to be 100% backward-compatible with the pre-consolidation microservices to allow for rollbacks if needed. That meant we couldn\u2019t introduce any breaking changes during the transition \u2014 adding extra pressure to get things right the first time.<\/p>\n

Distributed configuration\u200b<\/h3>\n

The old system\u2019s over-granular design scattered configuration across multiple services and a config server. Rebuilding that into a unified configuration required careful investigation to locate, align, and centralize everything in one application.<\/p>\n

End-user impact\u200b<\/h3>\n

Since the system was customer-facing, any bug or functionality gap after consolidation could directly affect users. This raised the stakes for every change and reinforced the need for a cautious, thoughtful rollout.<\/p>\n

Architectural refactoring comes with risks and understanding them upfront is key to delivering both system reliability and cost efficiency.<\/p>\n

What we gained: lower costs, higher reliability, and a sustainable system<\/h2>\n

Cloud cost reduction<\/h3>\n

After consolidation, overall cloud infrastructure costs were reduced by 82%<\/strong>. This was a direct result of architectural refactoring, microservices reduction, and more efficient resource usage.<\/p>\n

Monitoring tool efficiency<\/h3>\n

The new architecture also lowered the load on external monitoring tools, leading up to 70% drop in related costs<\/strong>.<\/p>\n

Indirect cost savings<\/h3>\n

While we didn\u2019t have full access to some billing metrics, we know that many tools charge based on factors like request volume, microservice count and internal traffic. Simplifying the core of the system brought savings across these areas too.<\/p>\n

Simplified maintenance<\/h3>\n

Shrinking from 25 microservices to 5<\/strong> dramatically reduced the effort required for feature development, domain-specific releases, and CI\/CD pipeline management. Once we removed the facade of complexity, it became clear the system wasn\u2019t as complicated as it seemed. Onboarding new developers is now much faster and easier \u2014 which also opens the door to rethinking how many engineers are truly needed for ongoing support.<\/p>\n

Zero downtime deployment<\/h3>\n

Since we were working with a customer-facing system, minimizing downtime for each release was critical. By consolidating functionality into 5 clearly defined, domain scoped applications, we made it possible to achieve zero downtime deployments in production.<\/p>\n

Reduced complexity<\/h3>\n

Consolidation clarified how the system works and gave developers a wider view of its components. With cohesive domains and logic housed in fewer applications, it\u2019s now easier to follow business flows, implement efficient solutions, debug issues, and write effective tests.<\/p>\n

\u2014<\/p>\n

Every decision made at a given moment usually feels like the right one \u2014 and often it is. But if something remains important over time, it\u2019s worth revisiting that decision in light of new context and evolving circumstances. As our case clearly shows, taking the time to reevaluate can truly pay off \u2014 both literally and figuratively.<\/p>\n<\/p><\/div>\n\n","protected":false},"excerpt":{"rendered":"

When microservices are overused, complexity and costs skyrocket. Here\u2019s how we consolidated 25 services into 5 \u2013 simplifying architecture and slashing cloud spend without sacrificing stability. It\u2019s hard to predict exactly how microservice architecture will evolve, what pros and cons will surface, and what long-term impact it will have. Microservices can offer significant benefits \u2014 […]<\/p>\n","protected":false},"author":1,"featured_media":288954,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[119902],"tags":[12564,13594,2070,119904,119903],"dealstore":[],"offerexpiration":[],"class_list":["post-288953","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-app-modernization","tag-cloud","tag-costs","tag-cut","tag-microservices","tag-overengineering"],"yoast_head":"\nOverengineering Microservices: How We Cut Cloud Costs by 82% - Som2ny Network<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/fivemor.com\/?p=288953\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Overengineering Microservices: How We Cut Cloud Costs by 82% - Som2ny Network\" \/>\n<meta property=\"og:description\" content=\"When microservices are overused, complexity and costs skyrocket. 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