The promise: Microservices make your system scalable, maintainable, and fast.  The reality: For most... Tagged with microservices, architecture, performance, backend.

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Microservices introduce significant performance overhead compared to monolithic architectures. Network calls add 1,000-5,000x latency versus in-process function calls, resulting in 50-150% higher latency, 300% more resource usage, and 2-3x infrastructure costs. Benchmarks show microservices suffer from cascading failures (5x more downtime), database connection exhaustion, and serialization overhead. The article argues microservices solve organizational problems (team autonomy, independent deployment) rather than technical ones, and recommends modular monoliths for most applications. Microservices only make sense for organizations with 50+ engineers, independent scaling requirements, technology diversity needs, or compliance isolation.

Microservices Are Killing Your Performance (And Here's the Math)

Imagine system (i will not tell the company) where every component on the page has own backend - (BFF architecture) - this backend is communicating with another backend service translating queries to another queries needed with another even bigger and legacy service, then this service is doing mysterious data transformations during database query. All that with multiple cache layers, because of course it works terribly slow. Half of these services written by people no longer working, and even smaller change takes ages, because noone know what happen under the hood. On every page we have at least 10 components doing own requests. Sometimes I think we are in hell and this is our punishment for our previous sins (i pirated few games as a kid). 🙃
Fortunately the’re paying my bills.

While microservices do introduce overhead, framing them as mainly a liability is an oversimplification and ignores how modern systems are actually built and operated today.
First, the “1,000–5,000x latency” comparison is misleading. Comparing an in-process function call to a network call is technically true but practically irrelevant. Real systems are dominated by I/O (databases, caches, external APIs). In most production workloads, network latency between services (especially within the same cluster/VPC) is measured in sub-millisecond to low-millisecond ranges, which is negligible compared to database queries, disk I/O, and third-party integrations. With gRPC, HTTP/2, service meshes, and proper caching, the overhead is well controlled.
Second, modern microservice architectures are designed specifically to handle reliability and cascading failures. Patterns like circuit breakers, retries with backoff, bulkheads, timeouts, and fallback mechanisms significantly reduce failure propagation. Platforms like Kubernetes, Istio, and managed cloud services provide built-in health checks, auto-healing, and traffic management that monoliths do not get for free. A poorly designed microservice system will fail, but so will a poorly designed monolith—just in different ways.
Third, the claim of “300% more resource usage” and “2–3x infrastructure cost” is not universally true. In many real-world systems, microservices reduce cost by allowing fine-grained scaling. Instead of scaling an entire monolith for one hot feature, only the bottleneck service is scaled. This leads to better CPU and memory utilization. Serverless and container-based deployments further optimize this by scaling to zero or near-zero when idle.
Fourth, database and serialization overhead are architectural choices, not inherent flaws. Database connection exhaustion is a design and configuration problem, solvable with pooling, proxies, and proper limits. Serialization overhead is minimized with efficient formats (Protobuf, Avro) and binary protocols. High-performance distributed systems (Netflix, Google, Amazon, Uber) successfully operate at massive scale using microservices, proving these issues are manageable in practice.
Fifth, microservices do not only solve “organizational problems.” They provide real technical benefits:
•	Independent scaling of workloads
•	Fault isolation
•	Technology flexibility
•	Clear ownership boundaries
•	Faster experimentation
•	Better system evolvability over time
These are technical advantages that directly affect system stability and longevity.
Sixth, the idea that microservices only make sense for “50+ engineers” is outdated. Cloud-native tooling, managed platforms, and DevOps automation have dramatically reduced operational complexity. Even small teams can run microservices efficiently today using managed Kubernetes, PaaS, or serverless platforms.
That said, modular monoliths are absolutely a good starting point for many projects, and jumping into microservices too early is a mistake. But this does not mean microservices are inferior—it means architecture should evolve with product maturity, traffic patterns, and business needs.
In practice, many successful systems follow this path:
Start with a modular monolith → Extract high-impact services → Gradually evolve into microservices where it makes sense.
This hybrid and evolutionary approach is what most mature companies actually use.
So the real conclusion is not “microservices are bad,” but:
Microservices are powerful when applied for the right reasons, with proper engineering maturity and tooling. When used correctly, they improve scalability, resilience, and long-term maintainability. When used poorly, they amplify existing problems.
Architecture is about trade-offs, not absolutes.