By ByteBot
Twilio Segment just published a case study that should make every CTO uncomfortable. They moved 140+ microservices back to a single monolith. Amazon Prime Video did the same and cut costs by 90%. These aren’t startups making rookie mistakes—they’re major tech companies with DevOps expertise.
The microservices revolution promised infinite scalability and developer velocity. A decade later, the industry is quietly admitting it sold you premature optimization disguised as best practice.
This Is Cargo Cult Programming at Scale
Amazon and Netflix run microservices successfully, so the industry decided everyone should. But Amazon and Netflix have hundreds of engineers and proven scale needs. Your seven-person startup doesn’t.
That’s textbook cargo cult programming. You’re copying rituals without understanding why they work. You’re building for Google-scale traffic you’ll never see. Eighty-nine percent of organizations adopted microservices, but adoption doesn’t equal necessity.
Remember when everyone needed Hadoop clusters? When NoSQL would kill relational databases? Microservices hit the same trajectory: necessary for the top 1% of scale, cargo cult for everyone else.
The Math Doesn’t Work for Small Teams
Industry data is clear: microservices only benefit teams larger than 10 developers. Below that threshold, monoliths perform better. Measurably, provably better.
The reason is physics. Network calls are 10,000 times slower than in-memory operations. You’re replacing function calls with HTTP requests, adding latency and failure modes that didn’t exist before. For small teams, coordination costs exceed productivity gains.
Ninety percent of microservices teams batch deploy anyway, defeating the architectural benefit. If you deploy every service for a library update, you don’t have microservices. You have a distributed monolith: network latency plus deployment bottlenecks. The worst of both worlds.
Twilio Segment learned this the hard way. Their team was “mired in exploding complexity.” Test suites took an hour. Three full-time engineers spent most of their time keeping the system alive. That’s not scalability—that’s technical debt disguised as architecture.
Real Companies Proved It Wrong
Twilio Segment’s case study is remarkable for its honesty. After consolidating 140+ services into a monolith, test time dropped from an hour to milliseconds. Library improvements increased from 32 to 46 per year. One engineer could ship changes in minutes instead of coordinating a hundred repositories.
The trade-off? Less fault isolation. Twilio accepted that risk because productivity gains were worth it. Sometimes the “best practice” is the one that lets you ship faster.
Amazon Prime Video’s migration was even more dramatic. Their video quality analysis pipeline ran on Lambda and Step Functions. It worked, but costs were brutal. They rewrote it as a monolith. Data transfer moved in-memory instead of over the network. Costs dropped 90%.
These are official case studies from companies with resources to do microservices “right.” If they’re moving back, what does that tell you?
The Pragmatic Path Forward
The industry is shifting from dogma to pragmatism. The new consensus isn’t monolith good, microservices bad. It’s context matters.
Modular monoliths emerged as the pragmatic middle ground. Single deployment units with well-defined internal boundaries. You get structure without network overhead and operational complexity. Modern frameworks like Spring Modulith enforce module boundaries at compile time.
The evolution path is clear: start with a modular monolith, validate your product, extract services later if business needs justify operational complexity. Ninety percent of projects should follow that path.
If your team has fewer than 10 developers and you’re building microservices, you’re wasting engineering time. Start simple. Solve actual problems, not hypothetical ones. You can always add complexity when real constraints demand it.
The microservices hype is dying because reality caught up with the sales pitch. Smart teams are choosing simple solutions that let them ship faster, debug easier, and sleep better.
