Microservices Architecture
Microservices solve an organisational problem, not a technical one. When several teams need to deploy independently, they are transformative. When one team runs one product, they are a tax — and we will say so.
Independent
Deploy one service, not everything
Event-driven
Services decoupled by messaging
Incremental
One endpoint at a time, reversible
Often: no
Our usual honest recommendation
The site has separate pages for the pieces — Kong, RabbitMQ, Kubernetes — but the architecture they form together is the thing enterprises actually ask about. It looks like this:
Frontend → API gateway → independent services → message bus → per-service databases → separate reporting services. Each service owns its own data, communicates asynchronously through events rather than by calling its neighbours directly, and can be deployed, scaled and failed independently of the rest.
We have built and run this shape — Django services communicating over RabbitMQ with asynchronous consumers, their own databases, dedicated reporting services, and containerised deployment on Kubernetes. We are also clear that most systems should not be built this way, which is the more useful half of the conversation.
How Microservices Go Wrong
Every one of these produces something harder to operate than the monolith it replaced.
The distributed monolith
Services split apart but still calling each other synchronously in a chain, so one slow service makes every request slow and nothing can be deployed alone. All the operational cost of microservices, none of the independence.
One shared database behind everything
Services separated in code but all writing to the same schema. Any change to a table risks breaking three services, so nobody dares change anything — the exact coupling the split was meant to remove.
No correlation IDs
A request touches five services and nobody can trace it end to end. Debugging becomes reading five log files with timestamps and guessing which entries belong together.
Split before the domain was understood
Service boundaries drawn along technical lines rather than business ones, so almost every feature requires changing several services at once — and coordinated releases return, which is what everyone was trying to escape.
Architecture Capabilities
Designing it, migrating to it incrementally, and operating it once it exists.
Architecture Assessment
An honest read on whether your system needs splitting at all — what the real bottleneck is, and whether it is technical, organisational, or a missing database index.
Service Boundary Design
Boundaries drawn along business capabilities rather than technical layers, so a typical feature lives inside one service instead of requiring a coordinated release across four.
API Gateway Layer
Kong at the edge handling authentication, rate limiting, routing and request logging once, so services receive already-authenticated requests and policy is enforced consistently.
Event-Driven Messaging
RabbitMQ or Azure Service Bus carrying domain events between services, with idempotent consumers, retry policy and monitored dead-letter queues so nothing is lost silently.
Per-Service Data Ownership
Each service owning its own database, with data shared through events rather than cross-service queries — the decision that actually delivers independent deployment.
Reporting Services
Dedicated reporting services consuming events into a read-optimised store, so cross-service reporting does not require querying five databases or slowing down transactional work.
Strangler-Pattern Migration
Moving endpoints from a monolith one at a time behind unchanged public URLs, with percentage traffic shifting and instant rollback — no big-bang cutover weekend.
Containerised Deployment
Services packaged as containers and orchestrated on Kubernetes with health-checked rolling updates, so deploying one service does not touch any of the others.
Observability
Correlation IDs propagated from the gateway through every service and message, with centralised logging and per-service metrics, so a request can be traced end to end.
Getting the Architecture Right
Data ownership is the decision that matters
Everything else is negotiable; this is not. If several services read and write the same tables, you do not have microservices — you have a monolith with network calls in the middle, and you have taken on distributed-systems complexity without buying any independence. A schema change still coordinates across teams, and a release still has to be coordinated.
So each service owns its data outright, and other services learn about changes through published events rather than by querying across a boundary. This forces you to confront eventual consistency honestly, which is uncomfortable and correct: it is a real property of any distributed system, and pretending otherwise with cross-service joins simply hides it until it causes an incident.
- Each service owns its own database, no exceptions
- Data shared through published events, not cross-service queries
- Eventual consistency designed for, not discovered
- Schema changes stop being a coordination problem
Asynchronous by default, synchronous only where required
Services calling each other synchronously produce chains: A waits on B, which waits on C. Latency accumulates, and one slow service degrades everything upstream. Worse, C being down takes A down with it — the failure mode people adopted microservices to escape.
So the default is a published event that other services consume when they can. The originating request completes as soon as its own work is durably recorded. Where a synchronous call is genuinely unavoidable, it carries a timeout and a fallback so a slow dependency degrades one feature rather than the platform. That is the same discipline described on our RabbitMQ page, applied at the architecture level.
- Events by default; synchronous calls the exception
- Every synchronous call has a timeout and a fallback
- Idempotent consumers — messages will be delivered twice
- Monitored dead-letter queues so failures are visible, not silent
Migrate one endpoint at a time, never in one go
A rewrite that replaces a working monolith with a service architecture in one release is the highest-risk way to do this, and it is the version that most often ends with the business running two half-finished systems. The alternative is the strangler pattern, and it needs a gateway to work at all.
The gateway holds the stable public URL while routing decides whether each path is served by the monolith or by a new service. So you extract one endpoint, send a small percentage of traffic to it, compare error rates and latency, increase gradually, and roll back instantly by changing a route rather than redeploying. Each step is small, reversible and independently valuable — and the business keeps running throughout.
- Stable public URLs while the implementation moves behind them
- Percentage traffic shifting with error-rate and latency comparison
- Rollback by changing a route, in seconds
- Every step independently valuable — no half-migrated dead end
Reporting is where naive splits fall apart
The question that breaks most microservice designs is the first cross-cutting report: revenue by customer by region this quarter, where the data lives in four services. Query across the boundaries and you have reintroduced coupling and made every report as slow as the slowest service.
The answer is a dedicated reporting service that consumes the same domain events into a read-optimised store shaped for querying rather than for transactions. Reports are fast, they never touch transactional databases, and the reporting store can be rebuilt from the event history if a definition changes. It is more moving parts, and it is the only version that scales — the same separation of analytical from transactional load we apply on single-database systems too.
- A dedicated reporting service consuming domain events
- Read-optimised store, never queried across service boundaries
- Reporting load fully isolated from transactional work
- Rebuildable from event history when a definition changes
Most systems should stay a monolith
We build microservices and we talk the majority of clients who ask for them out of it. Microservices solve an organisational problem — several teams needing to deploy without coordinating. If that is not your problem, you are paying the cost and getting none of the benefit:
- One team, one product? Build a well-structured monolith. It will be faster to develop, far easier to operate, and you can split later if you genuinely need to.
- Pre-launch startup? Almost certainly not. Optimise for changing quickly, not for a scale you do not have. We say the same on our startup page.
- The current system is slow? Find out why first. It is usually missing indexes, N+1 queries or work happening inside a request — none of which a service split fixes, and all of which it makes harder to diagnose.
- A modular monolith is often the right middle ground. Clear internal boundaries and separate schemas, one deployment. Most of the discipline, almost none of the operational cost.
What We Pair It With
Services
Edge
Messaging
Data
Platform
Frequently Asked Questions
Probably not, and we say that as a team that builds them. Microservices solve an organisational problem — several teams needing to deploy independently without coordinating. If you have one team on one product, a well-structured monolith will be faster to develop and far easier to operate. We assess the actual bottleneck first, and it is frequently a missing index or work happening inside a request rather than anything architectural.
Related Expertise
Kong API Gateway
The edge that makes incremental migration possible at all.
RabbitMQ
The event backbone between services.
Docker & Kubernetes
Deploying and scaling services independently.
Microsoft Azure
Managed queues, service bus and container hosting.
Python & Django
What we build the individual services in.
Custom Software Development
How architecture work is scoped alongside features.
Told You Need Microservices?
Before you commit to that, let us look at what is actually slow or hard to change. If the answer is a well-structured monolith or a modular one, we will tell you — and that is a smaller project for us.
Serving startups, factories and enterprises across India, the US, UK, Australia & Europe.