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Microservices & Cloud-Native

Not a monolith. An architecture that scales.

Instead of one big app — small, independent, clearly-owned services. Containers, Kubernetes, auto-scaling, continuous delivery, message queues, observability — the full modern cloud-native frame. On AWS, GCP, Azure or your own metal.

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k8s-prod-eu-west-1 healthy

Production cluster

nodes

8

pods

42

req / s

2.4k

deployment: api-gateway

14 running2 starting2 pending
auth-service v2.1.4
user-service v3.0.1
order-service v1.8.2
payment-service v1.2.0

When microservices?

Not every app needs them. Does yours?

01

Traffic spikes are wild

10-100x at certain hours or seasons.

02

Modules scale differently

Some CPU-heavy, others IO-heavy.

03

Multiple teams in parallel

5+ teams in the same code = constant conflicts.

04

Mixed technologies

Python (ML), Go (perf), Node (real-time) together.

05

High availability required

If one service falls, the rest must stay up.

06

Fast independent deploys

Every team wants to ship their service daily.

Cloud-native pillars

The 12 pillars of a modern app.

01

Containers

Docker / OCI-based consistent packaging.

02

Orchestration

Kubernetes / ECS / Nomad.

03

Service mesh

Istio / Linkerd: traffic, security, observability.

04

API gateway

Kong / Traefik / AWS API GW.

05

CI/CD

GitHub Actions / GitLab CI / Argo CD.

06

IaC

Terraform / Pulumi / Helm.

07

Message queues

Kafka / RabbitMQ / SQS.

08

Cache & store

Redis / Memcached / DynamoDB.

09

Observability

Prometheus + Grafana + OpenTelemetry.

10

Secret management

Vault / AWS Secrets / GCP SM.

11

Zero-trust security

mTLS, SPIFFE, least privilege.

12

Multi-region & DR

Multi-AZ / multi-region, auto-failover.

Cloud providers

AWS, GCP, Azure or your own metal.

AWS EKS AWS ECS / Fargate GKE (GCP) AKS (Azure) DigitalOcean Kubernetes Hetzner On-prem Kubernetes OpenShift Rancher

Benefits

What changes when you go microservices?

01

Independent scaling

Each service grows/shrinks per its own need.

02

Fault isolation

A failing service doesn't drag others down.

03

Fast deploys

Change in one service = one deploy.

04

Multi-team

Teams ship without waiting on each other.

05

Tech flexibility

Right language/runtime per service.

06

Cloud-portable

Moving AWS → GCP is weeks, not months.

Risk management

Microservices isn't a free lunch. It needs discipline.

01

Right boundaries

Wrong service boundaries = distributed monolith.

02

Data consistency

Eventual consistency + saga pattern.

03

Observability is mandatory

Traces, metrics, logs — without them, debugging is impossible.

04

Contract management

Inter-service contracts are versioned.

05

Network + latency

Service-to-service calls add latency; be careful.

06

Ops overhead

Don't do it without proper tooling and team training.

Process

From monolith to distributed system.

  1. 01

    Domain modelling

    DDD: identify the bounded contexts.

  2. 02

    Service boundaries

    Which module first; the order of split.

  3. 03

    Platform setup

    Kubernetes, CI/CD, observability, secret store.

  4. 04

    First service & contract

    One service split out, API contract written.

  5. 05

    Strangle further

    Modules are progressively split out of the monolith.

  6. 06

    Operate & mature

    SLOs, error budgets, autoscaling, multi-region.

Stories

Systems we scaled with cloud-native.

Fintech 14 services

Java monolith → 14 microservices

Release cadence weekly → 12/day.

E-commerce 80× peak

Black Friday auto-scale

80× traffic, zero downtime, auto-scale.

SaaS multi-region

Multi-region failover

EU region down → US auto-takeover.

IoT 200k msg/s

Device event pipeline

Kafka + Flink, 200k messages/s.

Logistics saga pattern

Microservices + saga

Order + dispatch + return in saga pattern.

Media real-time

Live streaming platform

WebRTC + Redis Streams + Kubernetes.

FAQ

Most asked

No. For small-mid apps a well-written monolith is usually the right call. Microservices need discipline, observability and team size. We decide together after scope analysis.
Yes. For simpler needs AWS Fargate, GCP Cloud Run, or managed Docker Swarm are good options. The right choice depends on the need.
Strangler-fig pattern: split one module, verify, then the next. We avoid big-bang rewrites; production keeps running.
Yes, with FinOps. Budget + cost alarm per service, conservative auto-scale parameters. No end-of-month bill surprises.
Yes. AWS / GCP / Azure / DigitalOcean / Hetzner / on-prem — we decide together. Cloud-agnostic architecture also makes later migration easy.

Move your system to a scalable architecture.

In a 30-minute discovery call we review your current system together.

Book a call Contact us
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