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Map bounded contexts
We read the code, data model and call graph, then draw candidate boundaries with your domain experts and rank them by value and tangle.
Monolith to microservices
Monolith to microservices migration, one bounded context at a time
We map your domain, extract one service behind a routing layer, run it beside the monolith and repeat. Some systems are better left whole, and we will tell you which.
- Domain mapping before any extraction
- Old and new paths run side by side
- Honest advice on keeping the monolith
How do you move from a monolith to microservices?
You do it in small slices, not in one rewrite. A monolith to microservices migration starts by finding the seams in your domain: the places where one business capability can be separated from the rest behind a narrow, well-defined interface. You extract that capability into its own service, send a share of traffic to it, compare its behavior with the old code, and retire the old path only when the results match. Then you pick the next seam.
This is the strangler fig pattern, and it is the usual way to reduce migration risk. It reduces risk. It does not remove it, because every extraction still touches data, transactions and the people who run the system. Our article on the strangler fig pattern explains the mechanics in more detail.
First we run an assessment such as a legacy code audit. For some monoliths the right answer is a modular monolith.
Signs the monolith is the real constraint
Microservices are a means, not a goal. These are the problems that usually justify the effort.
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Releases are coupled
A small change in billing forces a full regression run and a joint release with unrelated teams.
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One hot path drives scaling
A single module needs ten times the capacity of the rest, so the whole application is scaled to match it.
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Tables are shared by everyone
Many modules write to the same tables, so nobody can change a schema without a committee.
How an extraction runs
The same loop repeats for each service. The first pass is slower because it builds the groundwork.
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Put a routing layer in front
An API gateway or proxy lets you send chosen requests to new code and everything else to the monolith, and switch back quickly.
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Build the first service beside the monolith
The new service gets its own tests, deployment pipeline and observability. We write characterization tests against the old behavior first.
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Separate the data
The service takes ownership of its tables. Until then it reads through an interface, and replication or events keep both sides in sync.
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Shadow, cut over, retire
We compare responses from both paths, move traffic in stages with a rollback point, and delete old code after a stable period.
Extract services or keep a modular monolith?
| Extract services | Modular monolith | |
|---|---|---|
| Team shape | Several teams blocked by releases | One or a few teams |
| Scaling profile | Parts scale very differently | Load is fairly even |
| Operations skill | Tracing, CI and on-call exist | One deploy, simpler to run |
| Data ownership | Clear owner per context | One shared database is fine |
Risks and limits of splitting a monolith
Services trade one kind of complexity for another. A function call becomes a network call that can be slow or fail. A database transaction becomes a sequence of steps that must handle partial failure, which usually means eventual consistency and compensating actions. If the new services still share a database or must deploy together, you have built a distributed monolith, which is harder to run than the original.
- Operating many services needs monitoring, tracing and a team that can be on call for them.
- Splitting a shared database is the hardest part and often takes longer than the code.
- Cost and duration depend on the system. We do not give timelines or savings before an assessment, and we cannot promise outcomes.
Questions from architects and engineering leads
How long does a monolith to microservices migration take?
It depends on how entangled the code and data are, how many contexts you extract and how much test coverage exists. We do not quote a duration without reviewing the system. The assessment produces a phased plan, and the first extracted service is the real measure, because it shows how long the loop takes in your environment.
Do we need Kubernetes to run microservices?
No. Containers and an orchestrator are common, but a few services can run on a managed container platform or even virtual machines. The need grows with the number of services. We choose the platform with your operations team based on what they can run reliably, not on what is fashionable.
How do you split a database that every module uses?
Gradually. We first find which module writes each table, then move reads behind an interface, then move ownership of the tables to the new service. During the transition, replication or events keep both sides consistent. Each step has a rollback point, and each step is tested against production-like data.
Can you work with our existing team?
Yes. A modernization pod works alongside your engineers, with regular demos and written status. Your team keeps the domain knowledge, and ours brings migration practice and extra hands. We agree up front who owns which service and who reviews each change. See legacy application modernization for the wider approach.
Talk through your monolith
Describe the system and the problem that is pushing you to change it. Message us on WhatsApp at +92 326 5200333 or use the request form. A mutual NDA can be signed before you share any code or data.
What to send
- What the application does and who uses it
- Languages, framework and database
- Rough size and number of teams
- The main pain: releases, scaling or support
Chat on WhatsApp
Request a code audit
Please do not send secrets or source code in the first message.
Not sure where your monolith should split?
Send the request form or message us on WhatsApp with a short description of the system and its biggest pain point.