Architect for scale. Build for production.
Cloud-native architecture is not about picking containers or serverless. It is about designing systems where every component can be deployed, scaled, and recovered independently — without waking anyone up at 3 AM. We design production-grade architectures grounded in microservices, event-driven patterns, and Kubernetes-native operations.
Four patterns for production-grade systems
There is no single cloud-native pattern that fits every workload. We select and combine patterns based on your domain requirements, traffic profiles, team structure, and operational maturity.
Microservices Architecture
Decompose monolithic applications into independently deployable services with clear domain boundaries. Each service owns its data, scales independently, and can be deployed without coordinating with other teams. We define service boundaries using domain-driven design, not arbitrary technical layers.
- Independent deployments
- Team autonomy
- Targeted scaling
Event-Driven Architecture
Decouple services through asynchronous event streams using Kafka, NATS, or cloud-native brokers. Event sourcing and CQRS patterns give you auditability, replay capabilities, and the ability to build new read models without touching existing services.
- Loose coupling
- Audit trail built in
- Horizontal scalability
Serverless-First Functions
Deploy compute-on-demand for event handlers, API endpoints, and scheduled tasks. We use serverless where it fits — bursty workloads, low-traffic endpoints, and glue logic — and containers where it doesn't. No ideology, just the right tool for the workload.
- Zero idle cost
- Auto-scaling to zero
- Sub-second cold starts
Hybrid Cloud-Native
Not every workload belongs in a single cloud. We architect systems that run across AWS, Azure, GCP, and on-premises infrastructure using consistent Kubernetes primitives, service mesh, and federated identity. One control plane, multiple execution environments.
- Vendor flexibility
- Data sovereignty
- Consistent operations
The case for decomposition
Microservices are not inherently better than monoliths. They trade development complexity for operational flexibility. Here is when that trade-off pays off — and when it does not.
Independent deployment cycles
Each service ships on its own schedule. A payment service update does not require redeploying the entire application. Teams move at their own velocity without cross-team release coordination.
Targeted fault isolation
When a recommendation engine fails, the checkout flow keeps processing orders. Circuit breakers, bulkheads, and retry policies contain failures to the affected service boundary.
Technology heterogeneity
Use Go for performance-critical services, Python for ML inference, and Node.js for real-time APIs. Each team picks the runtime that fits their problem domain — not a company-wide mandate.
Granular scaling
Scale the search service to 50 replicas during a flash sale while the admin dashboard runs on 2. Resource allocation matches actual demand, not worst-case estimates for the entire monolith.
Organizational alignment
Service boundaries mirror team boundaries. Conway's Law works in your favor when architecture reflects organizational structure. Two-pizza teams own end-to-end delivery.
Incremental modernization
Strangle the monolith one capability at a time. Extract the highest-value domains first, prove the architecture, then continue. No big-bang rewrite required.
When NOT to decompose
Microservices carry real operational cost. Distributed tracing, network latency, data consistency, and deployment orchestration all add complexity your team must absorb. We will tell you honestly if your monolith is the right architecture for your stage.
- ✗ Your team has fewer than 15 engineers and a single deployment pipeline works fine
- ✗ The application has tightly coupled data with transaction boundaries that span multiple domains
- ✗ You lack observability tooling to trace requests across service boundaries
- ✗ The organization doesn't have the operational maturity to manage distributed systems
- ✗ Latency-sensitive workloads where inter-service network hops add unacceptable overhead
12-Factor Apps — adapted for Kubernetes
The twelve-factor methodology predates Kubernetes, but every principle maps directly to cloud-native primitives. Here is how we implement each factor in production Kubernetes environments.
I. Codebase
One repo per microservice, deployed via Helm chart or Kustomize overlay
GitOps with ArgoCD or Flux syncing from a single source of truth
II. Dependencies
Container images pin all dependencies at build time — no runtime package installs
Multi-stage Docker builds with vulnerability scanning in CI
III. Config
ConfigMaps and Secrets, never baked into images
External Secrets Operator syncing from Vault or AWS Secrets Manager
IV. Backing Services
Databases, caches, and message brokers accessed via Kubernetes Service DNS
Connection strings injected as environment variables, swappable without redeploy
V. Build, Release, Run
Immutable container images tagged with Git SHA, promoted across environments
CI builds image once, ArgoCD promotes the same artifact from staging to production
VI. Processes
Stateless pods with shared-nothing architecture, state offloaded to managed services
Horizontal Pod Autoscaler scales stateless replicas based on custom metrics
VII. Port Binding
Services expose HTTP/gRPC endpoints on container ports, routed through Ingress
Kubernetes Service + Ingress Controller (NGINX, Traefik, or Istio Gateway)
VIII. Concurrency
Scale by adding pods, not by threading within a single process
HPA with CPU, memory, or custom metrics (queue depth, request latency)
IX. Disposability
Fast startup, graceful shutdown with preStop hooks and SIGTERM handling
Readiness and liveness probes, pod disruption budgets for zero-downtime deploys
X. Dev/Prod Parity
Same container image, same Kubernetes manifests — environment differences are config only
Kustomize overlays or Helm values files per environment, no manual drift
XI. Logs
Write to stdout/stderr, collected by a DaemonSet log shipper
Fluent Bit or Vector shipping to Elasticsearch, Loki, or Datadog
XII. Admin Processes
One-off tasks run as Kubernetes Jobs or CronJobs, not SSH sessions
Database migrations as init containers or pre-deploy Jobs in the GitOps pipeline
SaaS platform decomposes monolith into 40 microservices
B2B SaaS
The Challenge
A mid-market SaaS company ran a 500K-line Python monolith deployed as a single container. Deployments took 4 hours, required full-team coordination, and failed roughly once per month. A single memory leak in the reporting module brought down the entire platform for 45 minutes during peak hours.
Our Approach
We mapped domain boundaries using event storming workshops, identified 40 bounded contexts, and prioritized extraction by business value and coupling analysis. Over 9 months, we extracted services incrementally using the strangler fig pattern — starting with the billing and notification domains that had the cleanest boundaries. Each service got its own database, CI/CD pipeline, and on-call rotation.
Results
99.99%
Uptime (from 99.5%)
12 min
Deploy time (from 4 hrs)
40
Independent services
8x
Deploy frequency
Frequently asked questions
Technology Partners
Ready to make AI operational?
Whether you're planning GPU infrastructure, stabilizing Kubernetes, or moving AI workloads into production — we'll assess where you are and what it takes to get there.
US-based team · All US citizens · Continental United States only