Microservices Architecture

Microservices architecture, without the operational overhead

We design and deliver microservices architecture for organisations moving away from large, slow-to-change monoliths. Our approach starts with domain-driven design — modelling services around business capabilities, not technical layers. Just as importantly, it starts with restraint: we don’t reach for heavyweight infrastructure by default, because in our experience, that’s usually where microservices programmes go wrong.

Not every monolith should be broken apart, and over-eager migrations create more complexity than they solve. We work out which parts of your system benefit from decomposition, draw boundaries that will still make sense in two years, and choose infrastructure sized to what your platform team can actually run well — not the most powerful tool on the market.

Domain boundaries before architecture diagrams

Software engineering team collaborating around code review and AI-supported delivery workflows in a modern office

The most common cause of failed microservices migrations is drawing service boundaries around technical layers instead of business domains — which leaves services that all have to change together, the exact problem microservices are meant to solve. We identify bounded contexts that map to real business capabilities first. Only then do we choose the communication and orchestration approach — and that choice depends on what your team can genuinely support, not what looks impressive on an architecture diagram.

The power tools trap

Implementing core platform capabilities with heavyweight tools — the kind that promise to solve every messaging, orchestration, and service-mesh problem at once — is one of the biggest pitfalls we see in enterprise organisations. These tools are genuinely powerful, and genuinely expensive to run well: unless your platform team can operate them to a high standard, they create a spiral of rising costs and operational headaches rather than the agility microservices are supposed to deliver. Our approach is to start with lighter, managed alternatives wherever they meet the need, and only take on platform engineering pitfalls like self-managed messaging and orchestration infrastructure when the complexity of your services genuinely demands it — and your team is resourced to run it.

Understanding the monolith, fast

You can’t safely decompose a system you don’t understand. For one financial services client, we used generative AI to extract more architectural understanding from a 15-million-line .NET monolith in 2.5 days than a previous four-week manual effort achieved — then rebuilt roughly half the system’s functionality in 3.5 days. That speed changes the economics of a monolith to microservices migration: real domain-driven design decisions, including right-sized architecture choices, become possible within days, not months.

What we bring

Domain-driven design workshops that identify bounded contexts mapping to real business capabilities. Right-sized infrastructure decisions — choosing the simplest tool that solves the problem, and being explicit about the operational cost of more powerful alternatives before you commit to them. Paved roads — reusable patterns for new microservices and data pipelines, built around what your platform team can actually maintain. GenAI-accelerated legacy analysis to understand large monoliths in days.

Where we've delivered

Front of a John Lewis store showing people walking in front of and into the store.

John Lewis Digital Platform — in 2017, JLP recognised its monolithic johnlewis.com backend, released monthly from on-premises data centres, needed to change. We worked alongside Partners to build the platform capability behind that shift to independently-deployable services.
A financial services organisation — used generative AI to analyse a 15-million-line .NET monolith, extracting more understanding in 2.5 days than a prior four-week effort, and rebuilding roughly 50% of functionality in 3.5 days.
A national Australian retailer — used a strangler fig pattern to decompose a restrictive SAP Hybris monolith into event-driven microservices one capability at a time, fixing fortnightly full-site outages and cutting the risk of team burnout from constant late-night deployments.

Frequently asked questions

What is microservices architecture?

A collection of small, independently deployable services, each owning its own data and communicating through APIs or events, rather than one large, tightly-coupled application.

Why does domain-driven design matter for microservices?

Domain-driven design decides where service boundaries sit — around business capabilities, not technical layers. Getting this wrong creates more coordination overhead than it removes.

Do we need Kafka and Kubernetes to build microservices?

Not necessarily. These are powerful tools, but they’re also two of the biggest sources of unplanned cost and operational complexity we see in enterprise platform engineering. We recommend starting with the lightest infrastructure that meets your actual need, and only taking on heavier, self-managed tooling when your platform team is resourced to run it well.

How do you decide whether to break up a monolith?

We assess which parts change frequently, need independent scaling, or are owned by different teams. Parts that change together and are owned by one team often don’t benefit from splitting.

How long does a migration take?

It depends on the system’s size and documentation. We’ve used generative AI to compress discovery from four weeks to 2.5 days on a 15-million-line monolith, which shortens the time to real decomposition decisions.

Why Equal Experts?

We bring judgement, not just infrastructure, to microservices

Breaking apart a monolith isn’t a technology decision first — it’s a domain and organisational one. At Equal Experts, we start with where you are, working incrementally to reduce risk rather than proposing a wholesale rewrite.

Our consultants draw on real-world experience decomposing complex, legacy systems for organisations across financial services, retail and government — including using generative AI to understand a 15-million-line monolith in days, not months. We work shoulder to shoulder with your teams, choosing infrastructure sized to what you can run well, not what looks impressive on a diagram.

With a focus on domain-driven design, right-sized architecture and platform thinking, we help you build services that scale with your organisation, not against it.