TechForge’s new guide warns that many fledgling microservices projects end up as “distributed monoliths,” delivering the latency of network calls without any scaling benefits. The piece urges engineering teams to start with a solid monolith and only break it apart when clear scaling or ownership needs arise.
Why teams rush into microservices
The appeal of microservices is obvious: independent services, separate deployments, and the promise of scaling each part of an application on its own terms. Start-up culture and recent success stories have turned the pattern into a badge of modern engineering. Yet splitting a monolith too early often creates a new kind of monolith—dozens of networked components. The cost? Higher latency, harder debugging, and more operational overhead, while the original benefits stay out of reach.
The first mistake: starting with a monolith in name only
Teams often label a system “micro-service-based” while keeping a single codebase and a shared database. The result is a series of tightly coupled modules that still talk to each other over HTTP or RPC. The guide calls this a “distributed monolith.” The pain points match those of a traditional monolith—tight coupling and difficulty changing one part without affecting the rest—plus added latency from network hops.
What to do instead: Build a clean monolith first. Define clear module boundaries, keep the data layer unified, and ensure the application can be tested and deployed as a single unit. Extract a module into its own service only when it needs independent scaling or separate team ownership.
Splitting by technical layer versus business capability
Another frequent error is carving services along technical concerns—UI, business logic, or data access. This forces a request to travel through a chain of services for a single operation, inflating response times and creating a fragile dependency graph.
Better approach: Organize services around business capabilities such as “orders,” “payments,” or “inventory.” Let each capability own its data and its own API, eliminating the need for a request to hop across layers.
Data ownership matters
When two services write to the same database table, they are no longer independent. The guide stresses that a service must never query another service’s tables directly; it should always go through that service’s public API. Sharing a database ties the services together, defeats isolation, and makes schema changes a coordination nightmare.
Synchronous HTTP is not a universal solution
Relying on synchronous HTTP for every interaction makes the whole system vulnerable to a single slow service. If Service A waits for Service B to respond before returning to the client, any slowdown in B propagates to A and ultimately to the user.
Alternative patterns: Use asynchronous messaging for tasks that do not need an immediate answer. Message queues or background jobs let services hand off work and continue processing, keeping the overall system more resilient.
Accepting eventual consistency
Traditional relational databases give you ACID transactions—Atomicity, Consistency, Isolation, Durability. Across service boundaries, those guarantees disappear. Trying to force two-phase commits (a protocol that tries to make distributed transactions behave like local ones) leads to complexity and instability.
The guide recommends sagas (a series of compensating actions) or the outbox pattern (where a service writes events to a local table that are later published). These approaches acknowledge that data may be temporarily out of sync and design the business logic to handle those gaps.
Build for failure from day one
A bug in one service should not bring down the entire system. Implement timeouts to avoid waiting forever, retries with back-off to handle transient failures, and circuit breakers that stop calls to a failing service until it recovers. Adding these safeguards after a production outage is too late; they belong in the initial design.
Observability is non-negotiable
Debugging a distributed system with logs scattered across many containers is near impossible. Centralized logging, aggregated metrics, and request-level correlation IDs let engineers trace a single user request as it moves through multiple services. Tracing tools visualize the call graph, making performance bottlenecks and failures easier to locate.
Keep the infrastructure lightweight at the start
Kubernetes, while powerful, brings a steep learning curve and operational overhead. For a handful of services, Docker Compose provides enough orchestration to spin up the entire stack locally. Only when traffic patterns, deployment frequency, or team size demand it should a more complex platform be introduced.
Align services with team ownership
Microservices were partly invented to let small, autonomous teams own the full lifecycle of a service. If a single team is responsible for ten services, coordination costs rise dramatically, eroding the intended benefits. The guide suggests that teams of fewer than ten people may be better served by a monolith, preserving simplicity while still allowing modular development.
The counter-argument: when microservices shine
The guide does not claim that microservices are inherently bad. In environments where different parts of an application have wildly different scaling requirements, or where regulatory constraints demand strict data isolation, the pattern can provide real value. Large organizations with multiple product lines often find that independent services reduce cross-team friction and enable faster release cycles.
The key is intentionality. If a team adopts microservices because they need to handle millions of requests per second for a specific feature, or because a new product line must be owned by a separate business unit, the added complexity is justified. The guide’s warnings target cases where the decision is driven by hype rather than concrete requirements.
What to watch for next
As more companies adopt cloud-native stacks, tooling around service mesh, distributed tracing, and automated canary deployments continues to mature. These advances lower the operational barrier but do not eliminate the fundamental design choices highlighted in the guide. Teams should monitor the evolution of observability platforms and async messaging frameworks, but still start with a clear justification for each service they spin up.
Takeaway
Microservices are a means to an end, not an end in themselves. Begin with a well-structured monolith, give each service true ownership of its data, use asynchronous communication where possible, and embed resilience and observability from the first line of code. When the business case is clear, break out services deliberately; otherwise, keep the architecture as simple as the problem demands.