Monolith to microservices migration roadmap: modernizing enterprise apps

Michael Labutin

Apr 5, 2025 12 min

If you’re here, chances are your monolithic system is becoming more of a burden than an asset. Slow release cycles, scaling headaches, and a rigid architecture make it harder to keep up. The bigger your app gets, the more frustrating it becomes. New tech doesn’t integrate smoothly, agility takes a hit, and reliability starts slipping.

Microservices can turn things around by making your system modular, speeding up deployments, and letting you scale exactly what you need when you need it. But here’s the catch: migrating isn’t just about splitting code. If you don’t plan it right, you could end up with more complexity, integration nightmares, and unexpected costs.

In this article, I’ll walk you through a real-world roadmap for moving from monolith to microservices. No fluff — just practical steps, hard-earned lessons from our solution architects, and strategies that actually work. Let’s dive in.

Steps for migrating from monolithic to microservices architecture

Step 1: Planning a monolith-to-microservices migration

I’ve seen many companies think microservices are the magic fix, only to end up with more complexity, broken workflows, and skyrocketing costs. And if there’s one thing I’ve learned, it’s that jumping straight into the tech side without a solid plan is a fast track to chaos.

It’s tempting to start ripping apart a monolith and spinning up services, but before we even touch the code, we work with clients to map out the why, when, and how of migration. That way, every step forward actually delivers value.

Setting the right goals: why are you migrating

Whenever clients come to us about switching from monolith to microservices, I ask what’s driving their decision to move. The answers vary, but more often than not, I hear, because their competitors are doing so. And honestly, that’s not a great reason. Jumping into microservices without a clear goal usually just leads to more headaches, not actual progress.

So before taking the plunge, ask yourself:

What are you hoping to achieve?
Have you considered alternatives to using microservices?
How will you know if the transition is working?

If you’re not 100% sure — no worries. We’ll help you define the key metrics and business outcomes upfront, so every tech decision moves the needle.

Microservices: a fit for everyone? Not always

Microservices bring modularity, independent scaling, and faster innovation. But they’re not a silver bullet. Some businesses do just fine with a monolith, especially if their app is simple, stable, and not changing much.

Imagine a small employee portal or an inventory system that only a handful of people use. If it’s working fine and doesn’t need constant updates, breaking it into microservices could just add a bunch of complexity for no real gain.

That’s why we don’t push microservices just for the sake of it. Instead, we look at what you specifically need and whether microservices will bring real rewards. If they will, great — we go for it. If not, we find a better way forward.

Assessing the monolith: know what you’re dealing with

Once we’ve decided microservices are the right move, we like to give your system a full health check to see how everything’s connected. We look for slow spots, potential dependency issues, and where all that critical data lives.

Skipping this step is risky. If you don’t know what’s under the hood, you could accidentally knock over the whole system like dominos. By mapping out what’s working, what’s lagging, and what could break, we create a smart migration plan that tackles the most critical areas first, minimizing risks, avoiding downtime, and making the transition as smooth as possible.

Picking the right migration strategy

By now, you’ve probably guessed, I’m not a fan of tearing down an entire monolith overnight. It’s too risky, too disruptive, and usually not worth the stress. Instead, I opt for a step-by-step approach that gives you quick wins while keeping your operations stable.

One of my favorite strategies is the Strangler Fig pattern, which lets your old system and new microservices coexist until you’re ready for the full handover.

Branch by Abstraction is handy when you have to make changes inside the monolith itself: we add a layer, move components one by one, and retire the old stuff without blowing things up.

If reliability is mission-critical, Parallel Run keeps both systems going, comparing outputs before you fully commit.

And if you can’t mess with the monolith, Change Data Capture lets us track database changes to keep microservices in sync.

There’s no single best method — it all depends on your setup. Our team also picks which parts to migrate first, focusing on the ones that’ll have the biggest impact. Take an e-commerce checkout system handling thousands of daily orders or a data analytics engine that constantly updates — those should go early. That way, you see real benefits fast and keep your operations sane.

Aligning teams and processes

Incorporation of microservices also means shaking up how your teams work. Instead of one huge team handling a monolith, I suggest moving to smaller, cross-functional teams where each one owns a specific microservice. This way, decisions get made faster, and everyone knows exactly what they’re responsible for.

Additionally, our experts bring in DevOps principles and automation from day one so that rolling out new features is smooth and hassle-free.

“Switching from monolith to microservices isn’t just a tech tweak — it hits your development speed, system stability, and your ability to scale. Without a best-laid plan, costs can skyrocket and integrations can turn into a real headache. At Innowise, we make the transition smooth and efficient, so you can keep things agile and focus on growing your business.”

Dmitry Nazarevich

CTO

Step 2: Identifying and defining microservices

Once we’ve mapped out the migration strategy, the next big question is figuring out how to break monolith into microservices without making a mess. I’ve seen companies either try to decouple everything at once or just pick random modules to split out. Either way, it leads to wasted time, broken dependencies, and months of frustrating rework.

My rule of thumb: keep it business-focused. That means each microservice should map to a real business function, not just some random chunk of code.

Finding natural service boundaries

One of the common pitfalls we see is splitting a monolith into technical layers. I mean separating the frontend, backend, and database into different services. That’s a surefire way to end up with tightly coupled, overly chatty microservices that don’t scale well. Instead, we go with Domain-Driven Design (DDD) and bounded contexts to break things down in a way that actually makes sense.

Take an e-commerce platform. Rather than splitting it into a generic front-end service and back-end service, we separate it into real business functions like order processing, inventory management, payments, and user management. Each service owns its own logic and data, keeping them loosely coupled so they can scale independently and evolve without breaking everything else.

Prioritizing services for migration

I’m not a fan of the “big bang” approach. Trying to migrate everything at once is just asking for trouble. Instead, we focus on what to break off first by looking at:

Minimal dependencies. Less tangled modules are easier to detach without breaking everything.
Business impact. Anything tied to revenue or customer experience usually jumps to the front of the line.
Frequent changes. Services that get updated all the time benefit most from microservices’ flexibility.

This approach helps us score quick wins and show early value, making it easier to get buy-in from the team. For example, in an enterprise HR system, payroll processing might make a great microservice since it handles complex, region-specific calculations. But a static company directory? Probably isn’t worth the extra overhead and can stay in the monolith for a while.

Avoiding the distributed monolith trap

The last thing we want is to convert monolith to microservices and still end up with a bunch of services that depend too much on each other. To dodge this, we:

Define clear APIs (REST, gRPC, or event-driven) so services communicate smoothly without unnecessary back-and-forth.
Make sure each service owns its data — no shared databases that create bottlenecks.
Keep dependencies to a minimum so services can be updated without breaking everything else.

By keeping services loosely coupled, we can upgrade or modify them without worrying about breaking everything else.

Getting teams on board

As I said before, microservices really shine when each team owns their service from start to finish. You get quicker feedback, more accountability, and way less back-and-forth between teams. At Innowise, we help companies set up their teams so devs, ops, QA, and everyone else can work together smoothly.

Break down your monolith into microservices and breeze through traffic spikes.

Step 3: Managing data in microservices

After we’ve split your monolith into microservices, the first question is usually what do we do with the data? In a monolithic setup, everything’s tied to one big database, which works until it doesn’t. In a microservices setup, that shared database quickly becomes a bottleneck, slowing everything down and making it impossible to scale services independently.

That’s why I push for a decentralized data model, where each microservice owns its own data. Done right, it lets each service grow, adapt, and scale without constantly tripping over each other.

Ditching the monolithic database

A massive, all-in-one database might seem like the easy way to go, but in a microservices setup, it quickly turns into a bottleneck. Every service has different needs, and cramming everything into a single database just creates roadblocks. Scaling gets tricky, dependencies pile up, and even small changes can cause system-wide issues.

That’s why we split up into smaller, service-specific ones, so each microservice:

Controls its own data. No more conflicts or accidental changes from other teams.
Scales on its own. Services don’t have to fight over database resources.
Can change freely. Updating one service won’t risk breaking the entire system.

This way, everything is more flexible, keeps teams from stepping on each other’s toes, and avoids the database bottleneck that slows everyone down.

Migrering av data

Moving data out of a monolith isn’t a flip-the-switch moment. A rip-the-bandage-off migration is risky, so I prefer an incremental approach, breaking it down step by step.

Usually, that means spinning up new tables or databases for each microservice and keeping them in sync with the old system using Change Data Capture (CDC) or dual writes. That way, each service gradually takes ownership of its data — no downtime, no unpleasant surprises.

Keeping data consistent

In a monolith, you’ve got one big shared database and ACID transactions that make sure everything updates (or fails) together. But with microservices, every service manages its own data, so updates don’t happen instantly across the system.

Instead of direct updates, services talk through asynchronous messaging. Say an order is placed, the Order Service fires off an event, and the Inventory Service listens in to adjust stock. This setup keeps things running smoothly, even if a service temporarily goes down.

Of course, that means handling consistency the smart way. At Innowise, we use idempotent operations to prevent duplicates, retry mechanisms to handle hiccups, and dead-letter queues to catch failures. This way, your data stays accurate, even when things don’t go as planned.

Step 4: Implementation & integration

Alright, now that we’ve set clear service boundaries and a solid data migration plan, it’s time to roll up our sleeves and turn strategy into action. Let’s dive into how we make that happen.

Building scalable, resilient microservices

Our dev team builds microservices using modern tools like Spring Boot and Node.js, making sure they’re built to scale and handle real-world challenges. To keep things running smoothly, we use smart design patterns like circuit breakers to manage traffic spikes and graceful degradation to prevent cascading failures. That way, even if one service hits a snag, the rest of your system keeps running without a hitch.

Keeping the legacy alive (for now)

Shutting down your monolith overnight? Not happening. Instead, we set up integration layers using RESTful APIs and message brokers like RabbitMQ or Apache Kafka to keep your new microservices and existing systems in sync. These act as bridges that let everything communicate smoothly without breaking workflows.

And when it makes sense, we also bring in API gateways to boost and secure interactions, guaranteeing a smooth transition with zero downtime.

Adopting containerization

We containerize your microservices with Docker so they’re fast, flexible, and easy to manage. With Kubernetes handling the orchestration, scaling up during busy times or rolling out updates across different environments is a snap. This setup keeps everything consistent, predictable, and cost-effective, so your IT ops never feel like a crapshoot.

Automating with CI/CD pipelines

Our team set up CI/CD pipelines with tools like Jenkins, GitLab CI, or CircleCI to handle testing, building, and deployments automatically. No more manual updates or last-minute fire drills. Bugs get caught early, releases go out faster, and the system stays rock solid.

Let us build a fault-tolerant microservices ecosystem for your business.

Step 5: Testing, deployment & monitoring

Without the right safeguards, even the best-designed system can hit bottlenecks, unexpected failures, or just flat-out crash at the worst time. That’s why our team takes a no-shortcuts approach, automating everything and catching issues before they cause real problems.

Automatisert testing

Testing isn’t just the final step, it’s part of the entire process. Our AQA team uses multi-layered automated test suites to catch failures early, so nothing slips through the cracks.

Unit testing. Each microservice gets its own checkup with JUnit, Mocha, and PyTest. If something’s off, we catch it right away.
Integration testing. APIs, dependencies, and data flow all need to sync up. Our experts use Postman, REST-assured, and WireMock to make sure they do.
Contract testing. Microservices have to play by the rules. With Pact, we keep them in check, preventing broken connections between services.
End-to-end testing. We run through real-world scenarios — from the user interface all the way to the backend — using Selenium, Cypress, and Playwright. This way, the whole system works as expected.
Load & stress testing. Our team pushes the system to its limits with JMeter, Gatling, and Locust to see how it holds up under heavy traffic.

Risk-free deployments

Nobody wants a bad release bringing down their system, frustrating users, or tanking revenue. That’s why our team keeps deployments safe, controlled, and rollback-ready with battle-tested strategies.

Canary releases. Instead of flipping the switch for everyone at once, we start small, rolling out updates to a tiny percentage of users first. If all goes well, we keep going. If something’s off, our experts fix it before anyone else even notices.

Let’s say a retail company wants to launch a points-based loyalty program, but their order system is too complex to modify safely. A retail company wants to launch a points-based loyalty program, but its order system is too complex to modify safely. To play it safe, we test it with a small group first. If all goes well, we roll it out wider.

Blue/green deployments. Our team runs two live environments at the same time:
- Blue (current version): the stable version that’s live;
- Green (updated version): the new release, tested and ready to go.

Once we’re confident the green version is solid, we flip traffic over instantly. If anything goes sideways, we switch back to blue. Zero downtime, no stress.

For instance, a travel platform wants to add real-time pricing, but messing with its old system could wreck booking. Instead of going all in, our team goes blue-green, sending a small group of users to the new setup first. If all’s good, we switch everyone over. If things go sideways, we roll back instantly.

Feature flags & A/B testing. Sometimes, rolling out to 100% of users isn’t the right move. Feature flags let us enable or disable features dynamically, so we can test in production without impacting everyone. We also use A/B testing to compare multiple feature versions in real-world conditions before committing to a full release.

Imagine an e-commerce company rolling out an AI-powered recommendation engine. Instead of flipping the switch for everyone, we use Feature Flags to enable it for returning customers first. If engagement and sales go up, we expand; if not, we turn it off instantly.

At the same time, our team runs A/B tests, comparing the old system to the new one and tracking key metrics like cart value and conversion rates. This data helps us fine-tune the AI before a full-scale launch.

Proactive monitoring & real-time logging

Microservices churn out tons of data, so keeping an eye on system health in real time is a must. Our team sets up multi-layered monitoring with tools like Prometheus, Grafana, and New Relic to track speed, memory usage, and errors. This way, we can spot problems before they become a headache. Using ELK Stack, Fluentd, and others, we also gather all the logs (basically, the digital trail of your apps) in one place, so nothing slips by. And if something does go wrong, automated alerts get our engineers on it ASAP.

Data backup & recovery

Let’s be real, no system is 100% fail-proof. Hardware dies, software crashes and cyber threats never stop evolving. That’s why data protection is a must. So our team sets up automated backup strategies so your critical data stays safe and easy to recover.

Automatic snapshots. We use AWS RDS, Google Cloud SQL, and Azure Backup to take continuous snapshots of your database. If something breaks, you can roll back instantly to a stable version with minimal downtime.
Geo-redundant storage. One backup isn’t enough. Our experts spread copies across different data centers, so even if one crashes or gets hit by a cyberattack, your data is still safe and accessible.
Incremental backups & point-in-time recovery. Rather than doing one huge backup that takes ages, we use smart backups that only capture recent changes. And with point-in-time recovery, we can rewind your database to any moment before an issue, saving you from accidental deletes or data corruption.
Disaster recovery. A strong disaster recovery plan keeps small issues from turning into full-blown crises. If something fails, automatic failover systems switch you to a backup, so your business keeps running without a hitch.

Step 6: Iterative optimization & scaling

Migrating monolith to microservices isn’t just a one-time upgrade, they need ongoing care to perform at their best. We’re here for the long run, guaranteeing your setup stays agile, scales smoothly, and handles even the toughest loads.

Innstilling av ytelse

Our team keeps an eye on each microservice, tweaking code, optimizing database queries, and smoothing out how services communicate to keep everything running fast.

Smart scaling

By analyzing real-time traffic and load patterns, our specialists dynamically adjust resources, making sure high-demand services get the boost they need without overspending.

Kontinuerlig forbedring

Your system needs to grow with your business. Our team tracks performance in real time, listens to feedback, and makes smart tweaks to keep your architecture secure, efficient, and bulletproof.

Krystallklar dokumentasjon

As we refine and expand your microservices, we keep everything well-documented. That way, future updates and migrations are smooth, and your team knows exactly what’s going on.

Future-proof your enterprise app with a smart microservices migration.

Modernize your enterprise applications with Innowise’s solutions

Monolith to microservices migration is a strategic move for better agility, scalability, and resilience. But jump in without a sensible plan, and you’re looking at downtime, broken workflows, and skyrocketing costs. A smart migration requires nailing service boundaries, handling data right, and following best practices for security and deployment.

At Innowise, we help companies make this shift with confidence. With 18+ years in software modernization and development, we handle everything from assessing your setup and designing a solid migration strategy to building scalable microservices and revamping performance. Our solution architects, DevOps engineers, and developers use tried-and-tested methods to reduce risk and maximize impact, guaranteeing your app’s systems scale and evolve with your business.

Michael Labutin Head of Java, ERP Solutions

Dato: Apr 5, 2025

Michael Labutin Head of Java, ERP Solutions

Dato: Apr 5, 2025

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