Cloud, API & Platform Engineering · Microservices
Microservices with clear boundaries, not a tangle
We design and build service-based architectures — drawing boundaries around business domains, choosing how services communicate, and putting the tracing, deployment and data patterns in place that keep a distributed system manageable.
- Domain-driven boundaries
- Sync and async messaging
- Cross-service consistency
Overview
The real trade-offs of going distributed
Microservices are an organizational choice as much as a technical one. They pay off when several teams need to ship parts of a system independently, or when one component has very different scaling or reliability needs from the rest. They cost you network calls that can fail, data spread across stores, and a platform of builds, deployments and monitoring that someone has to own. Size of team and pace of change usually decide the answer more than traffic does.
Inside a service architecture, the biggest decisions are about boundaries and coupling. Too many small services create chatty calls and coordinated releases, a distributed monolith with none of the benefits. Too few leave teams queuing on one codebase. Synchronous calls are easy to reason about but chain failures together; events decouple services but make flows harder to trace. We help you choose per interaction, not as a blanket rule.
A healthy service architecture lets one team deploy without asking others, contains failures to the service that caused them, and gives anyone a trace of a request across every hop. Just as important, the architecture is documented well enough that a new engineer can see which team owns a capability, where its data lives and how a request travels through it.
Who it’s for
Built for teams like yours
- 01
Scaling engineering teams
Companies growing from one product team to several, where everyone shipping through one codebase and one release train has become the main bottleneck. Clear ownership lets each team move at its own pace.
- 02
Monoliths under strain
Businesses whose single application has parts with very different load or reliability needs, such as checkout versus reporting, that would benefit from separate scaling. Only the parts that need it are separated.
- 03
Teams with tangled services
Organizations that already split into services but now face coordinated releases, shared databases and unclear ownership, and need the architecture straightened out. We fix the worst coupling first, then work outward.
Why it matters
Split for a reason, not for fashion
Microservices let teams deploy independently and scale the busy parts of a system on their own, but they add network failures, distributed data and far more moving parts. We start by asking whether a well-structured modular monolith would serve you better. When services are the right call, we draw boundaries around business capabilities, not technical layers.
Each service owns its data, exposes a clear contract and can be deployed, monitored and rolled back on its own — so the architecture speeds your teams up instead of slowing every release down.
Every engagement includes
- Architecture assessmentan honest review of whether microservices, a modular monolith or a mix fits your goals.
- Service mapdocumented boundaries, ownership, data stores and communication paths for every service.
- Incremental extractiona strangler-pattern plan for pulling services out of an existing monolith step by step.
- Platform foundationscontainer builds, service discovery, an API gateway and shared observability.
- Contract testingautomated checks that services stay compatible as each one changes independently.
- Operations handoverrunbooks, dashboards and architecture decision records your team can maintain.
Features
A distributed system that holds together
- 01
Domain-driven boundaries
Bounded contexts mapped with your team so each service owns one business capability and its own data.
- 02
Sync and async messaging
REST or gRPC where callers need answers, and Kafka, RabbitMQ or SQS where events should flow asynchronously.
- 03
Cross-service consistency
Saga and outbox patterns that keep data consistent across services without fragile cross-database locks.
- 04
Resilience patterns
Timeouts, retries, circuit breakers and bulkheads so one slow service doesn’t take the whole platform down.
- 05
End-to-end tracing
OpenTelemetry tracing, centralized logs and service dashboards that show exactly where a request slowed or failed.
- 06
Container orchestration
Docker images deployed to Kubernetes, ECS or Cloud Run with health checks, autoscaling and safe rollouts.
In practice
Typical service architecture work
Carving out a hot spot
One busy or fragile capability, such as search, payments or notifications, extracted from a monolith so it can scale, deploy and fail on its own. The rest of the monolith stays in place and keeps shipping as normal.
Event-driven order flows
Order, inventory, billing and shipping services coordinated through events and sagas, so each step completes or compensates reliably without long cross-service transactions. Each event is logged, so any order’s journey can be traced end to end.
Platform for multiple teams
Shared templates, pipelines and observability that let each team create a new service with logging, tracing and deployment already wired in from the start. Teams spend their time on features instead of rebuilding deployment plumbing each time.
Untangling a distributed monolith
Services that share a database or must release together reworked with clear data ownership, contracts and versioning, so they become independently deployable again. Release coordination meetings shrink as each service gains its own pipeline.
Process
How we work
- 1
Domain mapping
Through event storming workshops with your team, we map business processes, the events they produce and where natural boundaries fall between capabilities and teams. Business experts take part, not just engineers.
- 2
Boundary decisions
We decide which capabilities become services, which stay together, which data each owns and whether each interaction should be a synchronous call or an event. Each decision is recorded with its reasoning.
- 3
Platform foundations
Before extracting services, we set up shared service templates, container builds, deployment pipelines, a message broker and tracing, so every new service starts consistent. This prevents each team inventing its own conventions.
- 4
Incremental extraction
Services are pulled out one at a time behind routing that can shift traffic back, with data migrated and contract tests guarding each new boundary. Each extraction is complete before the next one starts.
- 5
Ownership handover
Each service gets a named owning team, dashboards, alerts and runbooks, plus architecture decision records explaining why boundaries were drawn where they were. On-call responsibilities are agreed for every service before we step back.
Deliverables
What you receive
- Event storming outputs and domain map
- Service boundary and data-ownership document
- Service template with logging and tracing built in
- Message schemas and event catalog
- Traffic-shifting plan for each extraction
- Per-service dashboards, alerts and ownership records
- Architecture decision records for key choices
Tools & methods
Runtime & orchestration
- Docker
- Kubernetes
- Amazon ECS
- Google Cloud Run
- Helm
- Istio
Messaging & data
- Apache Kafka
- RabbitMQ
- Amazon SQS
- NATS
- PostgreSQL
- Debezium
Patterns & observability
- Event storming
- Sagas
- Transactional outbox
- OpenTelemetry
- Pact
FAQ
Frequently asked questions
Anything else about Microservices? Ask us directly.
Often not yet. If you have one small team and a product still finding its shape, a modular monolith is usually faster to build and cheaper to run. Microservices pay off when separate teams need to release independently, or when parts of the system have very different scaling needs. We’ll give you a straight recommendation.
Let’s work together
Have a project in mind?
Book a strategy call and we’ll show you exactly how to turn your goals into a system that generates consistent results.