Microservices architecture: A Systems Perspective on Technical Benefits and Product Development

- Gennadii Turutin

1. Introduction

A solid microservice architecture can offer substantial advantages to most organizations in terms of business metrics, reliability, and scalability. No architecture is without trade-offs: the process of redesigning a monolithic application requires significant investment, coordination, and discipline, and there is no guarantee of success. Business context should dictate architecture — not the other way around. Engineers must be especially cautious to avoid premature optimization, a classic pitfall famously described by Donald Knuth as "the root of all evil."

Startups are often focused on finding a niche and delivering a business value fast, which means that clean and scalable implementations quite often get deprioritized for the sake of agility and feature development. In these environments, release cycles tend to involve broad, sweeping changes. The codebase is still evolving rapidly, and foundational business logic and design patterns are not yet fully established. Teams are typically small, and the developers responsible for writing code are often the same ones maintaining it. Workloads are more predictable, scalability concerns are minimal, and cognitive overhead from application complexity is low. In such settings, a monolithic architecture provides clarity, cohesion, and velocity.

However, as a company begins to grow—adding more clients, features, team members, and regulatory or performance requirements—the limitations of a tightly coupled system become more pronounced. At this point, a different architectural approach often becomes necessary to support the increasing demands of collaboration, quality assurance, reliability, and time-to-market.

2. Scalability

Imagine a service that performs two tasks:
(1) providing the current temperature every 5 minutes with a latency target of 1 second, and
(2) providing the average daily temperature every 24 hours with a latency of up to 60 seconds.

This means that once a day, the service must handle both tasks concurrently. The problem arises when the Service Level Agreement (SLA) for the current temperature specifies a maximum delay of 30 seconds: users should never see a temperature reading delayed by more than that window. If the daily aggregation job happens to be triggered earlier or consumes significant resources, the SLA for the current temperature may be violated.

While increasing CPU or memory resources could mitigate the issue, it does not fully eliminate the risk — and it introduces a new inefficiency: the service would be overprovisioned for most of the day, just to accommodate the peak requirements of the daily job. As the number of clients grows, the problem worsens: multiple daily jobs may be triggered close together, exacerbating resource contention before the 5-minute updates are processed.

A scalable and straightforward solution would be to decouple these two workflows entirely:

• A scheduler would dispatch messages to two separate queues — one for 5-minute updates, one for 24-hour updates — each with the appropriate frequency, resources, and parameters.
• Each queue would then be polled by dedicated instances: one set of microservices for current temperature reporting, and another set for daily aggregation.

To maintain high throughput and low latency more lightweight consumers with minimal CPU/memory allocations are preferable to fewer heavyweight consumers. The number of instances for each service should be managed dynamically based on schedule or queue depth. Major cloud providers, such as AWS, offer tools to scale services automatically based on metrics. In AWS, this can be achieved by creating CloudWatch alarms on SQS queue metrics and linking them to scaling actions in services like ECS Fargate.

3. Reliability

Because the two services described above operate with independent cloud infrastructure — separate queues, separate deployment clusters, and no direct dependency on each other — an incident in one service should not affect the other. With smaller, well-defined services, acceptance criteria become more atomic, making it easier for QA engineers to design targeted tests and conduct focused workload validation for each individual feature.

A critical advantage of microservice design is increased fault tolerance. This is not limited to simply isolating failures; it also includes redundancy strategies: by scheduling more message-processing runs in a scalable architecture, systems can increase the probability of successful execution. To support such patterns, services must be idempotent — meaning that multiple successful executions should produce the same end result as a single success — and they must be fine-tuned to handle higher loads gracefully.

Another important aspect of reliability is maintaining full operational visibility into each microservice's behavior. One of the biggest advantages of microservice architectures is the ability to monitor CPU and memory usage at the service level. Memory leaks and resource saturation issues are among the hardest problems to troubleshoot in large modules, because they often persist across multiple calls and do not immediately reveal their source.

4. Release Management

In a monolithic architecture, developers often block each other during testing and deployment phases, and releases tend to be large and infrequent. A failure in just one part of the monolith can force a full rollback of all updates — with no easy way to separate stable changes from problematic ones. This slows down product development cycles, worsens business metrics, and negatively impacts overall productivity.

Microservices significantly reduce the blast radius of changes. Rollbacks can be performed at the level of a single service, requiring much less coordination across teams. This leads to faster development cycles, more reliable deployments, and often higher job satisfaction among engineering teams.

Beyond code-level issues, monolithic applications tend to accumulate a larger number of package dependencies, creating additional challenges when different modules require different library versions within the same runtime environment. Microservices, by contrast, can maintain isolated dependency trees, enabling each service to upgrade libraries independently based on its needs.

5. Conclusion

As a company matures — with a stable product, well-defined business domains, and established engineering practices — transitioning to a microservice-based architecture often becomes a strategic necessity. While this approach introduces certain trade-offs, the continued investment by many successful, large-scale organizations serves as compelling evidence of its long-term value and effectiveness.