Microservices don’t fail because they are bad.
They fail because teams adopt them for the wrong reasons, at the wrong time, in the wrong way.
1️⃣ They Start With Microservices (Instead of Evolving)
What Teams Do ❌
- Day-1 microservices
- 10–20 services before first customer
- Each service has its own repo, CI/CD, DB
Why It Fails
- Development slows down
- Debugging becomes distributed hell
- Requirements are still changing
FAANG Reality ✅
- Amazon, Netflix started as monoliths
- Microservices were extracted under pressure, not ideology
Rule
Microservices are a scaling solution, not a starting architecture
2️⃣ Poor Service Boundaries (The #1 Killer)
Common Mistake ❌
Services split by:
- Technical layers (auth, db, api)
- Tables
- Random features
Instead of business domains.
Result
- Constant cross-service calls
- Circular dependencies
- One change touches 5 services
Correct Approach ✅
Split by bounded contexts:
- Order
- Payment
- Inventory
- User
Each owns:
- Its logic
- Its data
- Its lifecycle
If two services deploy together, they are the same service.
3️⃣ Synchronous Communication Everywhere
What Happens ❌
- Service A calls B
- B calls C
- C calls D
Real Production Impact
- Cascading failures
- Timeouts
- Retry storms
- P99 latency explodes
Why Teams Do This
- It feels “simple”
- HTTP is familiar
FAANG Solution ✅
- Sync for user-critical paths
- Async events for everything else
Distributed systems fail by default — assume it.
4️⃣ Distributed Transactions Without Strategy
Problem ❌
- Order → Payment → Inventory
- One fails, others succeed
What Teams Try
- Two-phase commit (2PC)
- Cross-DB transactions
Why It Fails
- Locks
- Deadlocks
- Poor performance
Correct Pattern ✅
- Saga pattern
- Eventual consistency
- Compensating actions
Strong consistency everywhere does not scale.
5️⃣ Operational Complexity Explodes
Microservices Add:
- Multiple deployments
- CI/CD pipelines
- Service discovery
- Secrets management
- Observability
Teams Underestimate:
- On-call burden
- Incident response
- Monitoring effort
Result
- Engineers spend more time fixing infra than building features
Microservices shift complexity from code to operations.
6️⃣ No Observability = Blind System
Common Situation ❌
- Logs per service
- No correlation IDs
- No tracing
During Incident:
“Which service failed?”
“I don’t know.”
FAANG Standard ✅
- Distributed tracing
- Centralized logging
- Metrics-first culture
If you can’t observe it, you can’t operate it.
7️⃣ Teams Aren’t Structured for It
Conway’s Law (Very Important)
System design mirrors team structure.
Problem ❌
- One team owns 10 services
- No clear ownership
- Shared responsibilities
Result
- Nobody knows who broke what
- Slow fixes
FAANG Model ✅
- One team → one or few services
- Clear ownership
- You build it, you run it
8️⃣ Versioning & Contract Hell
What Happens ❌
- API changes break consumers
- Services deployed independently
- No backward compatibility
Result
- Production outages
- Frozen deployments
Correct Approach ✅
- Contract-first design
- Backward compatibility
- Consumer-driven contracts
9️⃣ They Think Microservices = Better Engineering
Reality Check
Microservices:
- ❌ Don’t make bad code good
- ❌ Don’t fix unclear requirements
- ❌ Don’t replace discipline
What They Actually Require
- Strong engineers
- Clear communication
- Solid fundamentals
Microservices amplify both good and bad engineering.
🔟 The Brutal Truth (FAANG-Level Insight)
Most companies don’t have:
- Netflix traffic
- Amazon scale
- Google SRE culture
But they copy:
- Netflix architecture
- Amazon blogs
- Google talks
And skip:
- Engineering maturity
- Operational readiness
- Clear domain modeling
📊 When Microservices Actually Work
✅ Product-market fit
✅ Clear domain boundaries
✅ Experienced engineers
✅ Strong DevOps & SRE
✅ Real scaling pain
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