Microservices Development

Platform engineering is the discipline of building Internal Developer Platforms (IDPs) that abstract Kubernetes and cloud infrastructure complexity from product teams — providing self-service golden paths for creating, deploying, and operating microservices. In 2025, 90% of engineering organizations use an IDP (DORA 2025). Microservices and platform engineering are complementary: microservices require the operational infrastructure that platform engineering provides. Without an IDP, microservices operational overhead frequently overwhelms the team autonomy benefits.

A distributed monolith occurs when services are technically separate but logically coupled — requiring coordinated deployments, sharing databases, or making synchronous chains of calls that create the same brittleness as a monolith with added network latency. We prevent this using Domain-Driven Design: event storming workshops to identify business aggregates, bounded context mapping to align service ownership with business domains, and consumer-driven contract testing (Pact) to validate service interfaces. If two services must always be deployed together, they should probably be one service.

The strangler fig pattern is an incremental monolith migration strategy: new functionality is built as microservices while the monolith continues operating, with an API gateway routing traffic to new services as they become ready. The monolith 'strangles' as services are extracted from it one bounded context at a time. Use it when you need to modernize without a complete rewrite — which destroys business logic accumulated over years. We've delivered strangler fig migrations extracting 8–15 microservices from Java EE and Rails monoliths with zero production downtime.

It depends on your existing cluster setup and operational complexity tolerance. Istio Ambient Mesh (sidecarless architecture, GA in 2025) provides full L7 features — mTLS, traffic management, observability — with significantly lower overhead than sidecar-based Istio. Cilium eBPF provides L4 mTLS and network policy enforcement directly in the Linux kernel without any sidecar or waypoint proxy — lower complexity, appropriate for teams that don't need L7 features. Linkerd remains the simplest option for teams prioritizing operational simplicity over advanced traffic management. We recommend Cilium for most greenfield deployments in 2026 and Istio Ambient Mesh when L7 traffic policies are required.

Distributed transactions across microservices are one of the hardest problems in distributed systems, and two-phase commit is almost never the right answer at scale. We use the saga pattern: each service publishes domain events that trigger compensating actions if a step fails. For most business workflows, eventual consistency is acceptable — a payment that takes 200ms to confirm is fine; a payment that fails silently is not. We design saga orchestrators (using state machines in LangGraph or Temporal) for complex multi-step workflows where saga coordination logic needs to be observable and recoverable.

A modular monolith — a single deployable application with well-defined internal modules, separate database schemas, and clean interfaces between modules — is often the better choice for teams under 10 engineers, early-stage products before product-market fit, or applications where all components scale together anyway. The 2025 CNCF survey found 42% of organizations have consolidated some microservices back into larger deployable units to reduce operational overhead. We assess your situation honestly: microservices deliver their promised benefits only when team topology, scaling requirements, and operational maturity support them.

Yes — and Kubernetes is the natural operating layer for AI inference alongside traditional microservices. In 2026, 66% of GenAI inference workloads run on Kubernetes (CNCF 2026). We design AI-embedded microservices platforms with dedicated GPU node pools for inference services, separate from CPU-bound business logic services, with independent autoscaling policies. LLM inference services sit behind an AI gateway microservice that handles rate limiting, model routing, cost tracking, and fallback — decoupled from the business logic that calls them.

We standardize on OpenTelemetry for instrumentation — vendor-neutral trace, metric, and log collection that works with any backend. For storage and visualization: Grafana Tempo (traces), Prometheus (metrics), Grafana Loki (logs), and Grafana dashboards for unified visibility. We configure RED metrics (Rate, Errors, Duration) per service, SLO-based alerting, and distributed trace correlation that follows a single request through all services in one waterfall view. Observability is configured before launch — not after the first production incident.

Timeline depends heavily on scope. A greenfield platform with 5–8 well-defined services typically takes 3–5 months from domain analysis through production deployment. A strangler fig migration extracting the first 3–5 services from an existing monolith takes 2–4 months per extraction wave, depending on existing test coverage and codebase clarity. Platform engineering setup (IDP, GitOps pipelines, observability stack) adds 4–6 weeks to any engagement. We provide detailed timelines after the domain discovery phase, when service boundaries and complexity are known.

A full Code24x7 microservices engagement includes: domain discovery and event storming workshops, service boundary design with DDD bounded contexts, OpenAPI and AsyncAPI service contracts, hexagonal architecture service development, Kubernetes manifests (Helm/Kustomize), service mesh configuration (Cilium or Istio Ambient), OpenTelemetry observability stack, GitOps pipelines (Argo CD or Flux), chaos engineering validation, and a production readiness review. All source code, Helm charts, Kubernetes manifests, and runbooks are delivered to your team. We provide 90 days post-launch support and can transition to ongoing platform engineering retainer for continued service additions.