Born in the cloud. Built for what’s next.

Cloud‑native apps can scale specific services independently, so you add capacity only where demand spikes instead of scaling an entire monolith. They isolate failures, meaning one broken service is less likely to take down the whole system. Release velocity increases because teams deploy smaller, self-contained changes without mega coordinated releases. You also get better cost efficiency by combining auto‑scaling, containers, and serverless with pay‑as‑you‑go pricing.

Use microservices when your system has clear, independent business capabilities and you need autonomous teams, frequent releases, and selective scaling. Start with coarse‑grained services aligned to bounded contexts (DDD) instead of splitting by low‑level technical layers. If services must deploy together or constantly coordinate to complete a single business action, you are drifting into distributed monolith territory. Adjust granularity by merging overly chatty services and splitting only where cohesion and autonomy clearly improve.

Begin with “lift‑into‑containers”: wrap the monolith in Docker without changing code, standardize configs, and run it under an orchestrator like Kubernetes. Use this phase to externalize configuration, add observability, and automate deployment, then identify hotspots and logical modules inside the monolith. Incrementally carve out well‑defined capabilities into separate services, routing traffic via API gateway or service mesh while keeping the system functional. Avoid big‑bang rewrites; iterate with strangler‑fig patterns until the legacy core is small or gone.​

Use synchronous REST/gRPC for request/response paths that require immediate feedback (e.g., pricing, authentication). Move cross‑service workflows and non‑critical updates (emails, audits, projections) to async messaging with queues or event buses to reduce coupling and improve resilience. Define clear contracts and idempotent handlers so retries and at‑least‑once delivery do not corrupt state. Standardize on patterns like correlation IDs and timeouts/circuit breakers to keep latency and cascading failures in check.

Split CI (build/test images) from CD (environment‑specific deployment) and keep all desired state—manifests, Helm charts, Kustomize—in Git. Use GitOps operators (Argo CD, Flux) to continuously reconcile clusters to what’s declared in the repo, with PRs as the single change mechanism. Automate tests at multiple levels and use canary/blue‑green releases plus easy rollbacks based on metrics. Standardize pipelines and templates so each service team doesn’t reinvent the wheel.

Anchor on cloud‑agnostic layers first: containers (Kubernetes), open‑source data stores where feasible, and IaC tools like Terraform. Wrap provider‑specific services (managed queues, databases, serverless) behind internal interfaces or sidecar adapters to reduce direct coupling. Avoid deep lock‑in for core value paths (e.g., proprietary databases) unless the ROI is overwhelming; use them more freely for edge or non‑critical capabilities. Regularly test running key workloads on at least two providers so portability remains real, not theoretical.

Common traps include splitting purely by technical layers, ending up with tightly coupled “distributed monoliths” that still require synchronized releases. Teams often underestimate observability, making debugging across dozens of services much harder than in the monolith. Platform complexity (Kubernetes, service mesh, CI/CD) can grow faster than team skills, slowing delivery instead of speeding it up. Mitigate by starting with a few high‑value services, investing early in logging/metrics/tracing, and aligning service boundaries to business capabilities and team ownership.