Migrating from Monolith Architecture to Microservices: 5 Key Challenges

In 1998, service-oriented architecture (SOA) gained popularity and branched into multiple options that included microservices. Today, microservices are perceived as a modern interpretation of SOA and leveraged to develop distributed services that can be reused depending on the context. After the introduction of DevOps in 2014, microservices have become even more popular, providing multiple advantages: 

• Scalability 
• Flexibility 
• Autonomous development 
• Independent deployment 
• Flexibility to select the most suitable tool and language for each challenge 
• Fault detection and isolation 

Microservices are not only a new approach to software development but a representation of innovation in the industry.  

In a narrower sense, especially for businesses, microservices may be defined as a free-standing part of business functionality. This type of architecture resembles stand-alone software products, making up an extensive system together.  

Businesses worldwide are actively adopting the microservices approach to software development. Giants like Amazon, Netflix, and Uber are shifting from monolith architecture as they face the challenges of limited scaling and maintaining monolithic applications. 

For example, the migration to microservices allowed Amazon to organize its processes clearly where small teams are responsible for a certain service and understand how their work influences a bigger system. Amazon automated its operational processes, which allowed them to scale when it is needed.  

Along with the advantages, moving from monolith to microservices has certain challenges associated with these projects. Let’s take a look at the key problems businesses face and find the ways to overcome those.  

Five Challenges of Migrating from Monolith Architecture to Microservices 

Transitioning from monolith to microservices is a complex process, causing several challenges associated with service decomposition, data management, infrastructure complexity, communication overhead, and debugging complexity. Let’s focus on each of these challenges to determine which actions can help overcome them.  

 Challenge 1 – Decomposing services 

Today, many projects are built based on the microservices architecture, and many businesses are trying to migrate from outdated monolithic systems to gain a competitive advantage. So, decisions on how the services will be decomposed and what boundaries they will have impact the development of the whole system and its ability to meet stakeholders’ expectations and needs.  

Solution: Choose the right approach to decomposing 

It is important to choose the approach to decomposing that suits your project needs most. Among the most popular methods it is worth mentioning the one that decomposes services according to business capabilities and subdomains.  

The method of decomposing according to business capabilities is all about creating services about business logic, without focusing on their realization. For example, payments can be viewed as a separate service despite the payment methods or providers will be used as part of this service.  

Decomposing according to subdomains is more complicated as it is usually difficult to determine the boundaries of a particular service. So, some experts recommend a hybrid approach.  

Another method of decomposing is based on user stories, finding interdependencies across user stories, determining context, and monitoring metrics.  

Some teams use a four-stage decomposing: analysis of business requirements and creation of specifications, creation of workflow diagrams, identification of interdependencies between workflows, and identification of microservices candidates through clusters. 

These are only a few ways of decomposing services, and you should choose the one that best suits your project.  

Challenge 2 – Managing data 

Microservices architecture is good for resilience and scalability. However, managing data may be challenging. Each service has its own database. When the system works, it requires synchronizing of all databases to gain data consistency. Ensuring consistency across multiple services becomes difficult, especially for distributed transactions. 

Solution: Choose a relevant pattern to gain data consistency 

Achieving data consistency is possible through several strategies: asynchronous architecture, saga pattern (choreography and orchestration), distributed transactions, command query responsibility segregation, idempotency, and retries. Here is a table comparing these strategies.  

Technique	Consistency model	Use case	Challenges
Event-Driven Architecture	Eventual	Highly decoupled services	Handling out-of-order and duplicate events
Saga Pattern	Eventual	Distributed workflows (e.g., orders)	Complex rollback logic
Two-Phase Commit (2PC)	Strong	Critical operations (e.g., banking)	Latency and scalability issues
CQRS	Eventual	Performance-sensitive systems	Syncing read and write models
Idempotency and Retries	Strong/Best Effort	Handling transient failures	Requires unique tracking for retries

Remember that data consistency is a shared responsibility of developers, architects, orchestrators, data engineers, DevOps, and product owners. So, collaboration is required across all these roles. 

Challenge 3 – Infrastructure complexity 

Migration to microservices or development from scratch requires well-organized CI/CD processes, automated deployments, and container orchestration. Without the right processes, deploying services may be too chaotic, which leads to downtime and various failures.  

Solution: Plan, automate, and use the right tools 

To manage multiple microservices, it is recommended to use containers like Docker and orchestrators like Kubernetes. These platforms help standardize deployments across development, testing, and production.  

Another important thing that can help manage infrastructure efficiently is adopting CI/CD practices and DevOps. Tools like Jenkins, GitLab CI, or GitHub Actions will help create effective CI/CD pipelines.

Another best practices of managing infrastructure include the usage of API gateways, automating scaling and recovery, and centralized monitoring.  

Overcoming infrastructure complexity can be gained through collaboration of the platform engineering team, DevOps specialists, cloud/solution architects, and external consultants.  

Challenge 4 – Communication overhead 

Being one of the biggest challenges in microservices projects, communication overhead means increased network communication between services, causing postponements and failures.  

Solution: Follow best practices in microservices communication 

It is recommended to follow several best practices for efficient communication across services: 

1. Embrace asynchronous communication to increase resilience 
2. Leverage API gateways for outside communication to decrease the number of requests processed by each service 
3. Collect data in the backend when you need several services to respond 
4. Implement caching to avoid frequent calls between microservices 
5. Use versioning to decrease the number of errors and downtime 

Handling communication overhead is a shared responsibility. Solution architects usually develop interactions between services, whereas platform engineers set up API gateways. Product managers prioritize scalability and performance in business requirements; SREs ensure service reliability; backend engineers implement APIs, while quality assurance engineers test communication failures. 

Challenge 5 – Debugging complexity 

Due to the distributed nature of systems in the microservices architecture, it can be pretty difficult to detect and debug errors. The latter may arise in multiple services, so collaboration of various teams can be required.  

Solution: Use special tools and platforms 

Centralized logging is recommended to get a unified picture of all logs. So, you will be able to discover root causes and detect bugs quickly. For this process, there are many tools available: ELK Stack, Graylog, Grafana Loki, Datalog, and more. Choose the one that best suits your project needs.   

Local containers like Docker Compose, Minikube, or Telepresence can help you debug without deployments to production and simulate failures when testing. 

Besides, there are open-source debuggers that work specifically for microservices: Telepresence mentioned above and Squash.  

Identifying bugs in microservices may be tough, so use centralized logging and distributed tracing to reduce this complexity. 

Conclusion 

Monolith-to-microservices migration projects are quite complicated. You can follow the tips we mentioned throughout the article to overcome the challenges. However, if you don’t feel like your team has enough expertise, skills, and time to manage all these issues independently, you’d better look for a professional vendor who specializes in cloud computing, data migration, and microservices architecture. Such providers usually offer a discovery phase comprising recommendations on the most suitable tech stack, team composition, project timeline, etc.  

Although such projects take much time, effort, and cost, they are worth considering, as outdated systems limit your business in scaling and flexibility. Migration to smaller services will allow your organization to maintain a higher number of customers, enhance customer experience, reduce operational costs in the long run, deliver new features faster, and gain a competitive edge.  

Moreover, once you need to update your software, you can do it by replacing a microservice without the need to modernize the entire system. So, you will be able to introduce new functionalities faster as compared to those who prefer staying with legacy software. So, stay competitive in the rapidly changing technology world by shifting to microservices.