// Distributed systems on AWS
Microservices Architecture
Service boundaries, communication patterns, and the AWS platform underneath. Design through rollout, by an engineer who has run this in production.
One certified senior engineer on the work. No sales layer, no juniors.
// Services
Our Microservices Services
Architecture Design
Service boundaries drawn along the business domain, with the data ownership question answered before any code moves.
Service Mesh
Traffic policy, mTLS, and tracing once the service count justifies a mesh. Often it does not, and we will say so.
Migration Strategy
Strangler Fig, one slice at a time, with the monolith serving production traffic the whole way through.
// From a recent engagement
A platform of 26 services handling thousands of requests per minute (NDA) had no single alerting path and no consistent incident process. One alerting path plus runbooks for the recurring failures cut production incidents by more than 50%.
// In detail
Microservices Solutions in Detail
Microservices Architecture Design
Design Principles:
- Domain-driven design (DDD) applied to find real boundaries
- Service ownership: one team, one deploy, one datastore
- Data consistency and transaction patterns decided up front
- Event-driven flows where synchronous calls would couple services
Architecture Patterns:
- Amazon API Gateway or ALB in front, backend for frontend where it earns its keep
- Circuit breakers, bulkheads, timeouts, and retries with backoff
- Saga pattern for transactions that cross service lines
- Outbox pattern so a database write and its event cannot drift apart
Service Communication & Data
What We Set Up:
- REST and gRPC contracts with versioning that does not break existing clients
- Asynchronous messaging with SQS, SNS, and EventBridge
- Kafka or Amazon MSK when ordering and replay actually matter
- A database per service, plus a plan for the shared tables you have today
Runtime and Storage:
- Amazon ECS Fargate or EKS, depending on how much control you need
- Amazon RDS, DynamoDB, and ElastiCache behind the services that own them
- Amazon MSK, SQS, SNS, and EventBridge for the message paths
- Your application code stays yours: we design the boundaries and build the platform under them
Service Mesh Implementation
Service Mesh Features:
- Traffic management: canaries, weighted routing, retries at the mesh layer
- mTLS between services and policy on who may call whom
- Distributed tracing without instrumenting every service by hand
- Fault injection to prove the resilience story before an outage does
Service Mesh Solutions:
- Istio when you need policy and routing at the mesh level
- Linkerd when you want less to operate
- An ALB with sane timeouts and retries when a mesh would be overkill
- A mesh adds a control plane to run. We only recommend one when the traffic justifies it
Monolith to Microservices Migration
Migration Strategies:
- Strangler Fig: route slices of traffic to the new service, retire the old path last
- Database decomposition, including the joins that quietly hold the monolith together
- Incremental cutover with a rollback path at every step
- Integration with the legacy system that is going to stay longer than planned
Migration Tooling:
- Dependency mapping before anything moves
- API gateway routing old and new side by side during cutover
- Event streaming to keep data in sync while both paths are live
- A CI pipeline per service, with a rollback that has been tested
Microservices Observability
Observability Stack:
- Distributed tracing across service hops, with request IDs that survive the queue
- Metrics per service: latency percentiles, error rate, saturation
- Centralized structured logs, correlated by trace ID
- Health checks and service discovery that match how traffic is routed
Monitoring Tools:
- OpenTelemetry and Jaeger for traces
- Prometheus and Grafana, or Amazon CloudWatch and New Relic
- Amazon OpenSearch for log search
- PagerDuty as the one alerting path, so nothing routes to an inbox nobody reads
// Stack
Microservices Technologies We Use
Kubernetes
EKS where services need scheduling control. CKA, CKAD, CKS certified.
ECS Fargate
Less to operate when Kubernetes is more than you need.
EventBridge & SQS
Asynchronous messaging without running brokers yourself.
Service Mesh
Istio or Linkerd, once the service count justifies it.
AWS is the platform we work on. We are not going to sell you a multi-cloud design we would not run ourselves.
// What changes
Benefits of Microservices Architecture
Scalability
Scale the two services that are actually hot instead of the whole application.
Resilience
Fault isolation and circuit breakers so one slow dependency does not take the platform down.
Independent Deploys
Teams ship on their own schedule, without a release train that waits for the slowest change.
Clear Ownership
Each service has one owner, one datastore, and one pager. Nobody debates whose problem it is.
Microservices trade code complexity for operational complexity. If your team is small and the monolith is holding up, the honest answer is to leave it alone and fix the deployment pipeline instead.
// Process
Our Microservices Implementation Process
Domain Analysis
We map the business domain and find the boundaries that already exist in how your team works, before drawing any on an architecture diagram.
Architecture Design
Services, contracts, message paths, and data ownership written down, with the failure modes named.
Platform Build
Runtime, networking, pipelines, and IAM built as Terraform, so a new service is a pull request rather than a project.
Incremental Migration
Slices move one at a time behind a routing layer. The monolith keeps serving until the new path is proven.
Observability & Handover
Tracing, metrics, alerting, and runbooks in place, then your engineers get walked through all of it.
// Next step
Ready to Build Scalable Microservices?
The easiest way to start is the AWS Quick Wins Audit, a fixed scope review of your account and the architecture running in it.