Your AI Infrastructure Is Not Ready for Scale. Neither Is Mine.
The GPU shortage is real, rate limits are a production constraint, and your AI demo is going to collapse under real traffic. Some annoyed thoughts on infrastructure realism.
Distributed Systems
Definition
Distributed Systems coverage in this archive spans 14 posts from Mar 2017 to Mar 2026 and centers on data correctness and operability under real production constraints. The strongest adjacent threads are architecture, observability, and monitoring. Recurring title motifs include distributed, systems, patterns, and observability.
Working claims
- The common theme is that schema, ownership, and query shape drive most downstream outcomes.
- Early posts lean on monitoring and enough, while newer posts lean on distributed and patterns as constraints shifted.
- This topic repeatedly intersects with architecture, observability, and monitoring, so design choices here rarely stand alone.
How to apply this
- Define freshness, correctness, and latency targets before choosing storage or pipeline patterns.
- Start with the newest post to calibrate current constraints, then backtrack to older entries for first principles.
- When boundary questions appear, cross-read architecture and observability before committing implementation details.
Where teams get burned
- Scaling pipelines before locking down source-of-truth and reconciliation behavior.
- Optimizing single queries while ignoring data model drift and access patterns.
- Applying guidance from 2017 to 2026 without revisiting assumptions as context changed.
Suggested reading path
- Start here (current state): De-Risking the Black Swan: Red-Teaming Distributed Databases Before Production
- Then read (operating middle): You Probably Don’t Need Multi-Region
- Finish with (foundational context): Monitoring Is Not Enough
Related posts
- De-Risking the Black Swan: Red-Teaming Distributed Databases Before Production
- Your AI Infrastructure Is Not Ready for Scale. Neither Is Mine.
- Distributed Systems Patterns I Keep Reaching For
- Observability for Small Distributed Teams (What Actually Works)
- Event-Driven Architecture: What I Got Wrong and What Survived
- Database Replication Patterns That Actually Matter
- Most Edge Computing Projects Are Premature Optimization
- You Probably Don’t Need Multi-Region
References
Distributed Systems Patterns I Keep Reaching For
The patterns that actually survive production across failure handling, consistency, messaging, coordination, and scaling.
Observability for Small Distributed Teams (What Actually Works)
Most observability advice is written for 500-engineer orgs. Here's what actually matters when you're a small distributed team trying not to drown in dashboards.
Event-Driven Architecture: What I Got Wrong and What Survived
Lessons from building event-driven systems at the fintech startup and Decloud. What actually works, what silently corrupts your data, and Go patterns for handling events without losing your mind.
Database Replication Patterns That Actually Matter
A practical breakdown of replication modes, topologies, and the tradeoffs between consistency, availability, and not losing your users' data at 3am.
Most Edge Computing Projects Are Premature Optimization
Edge computing is real, but most teams adopting it don't have an edge problem. They have an architecture problem they're solving with geography.
You Probably Don't Need Multi-Region
Multi-region architecture is a strategic decision most teams make too early. Here's when it actually pays off, the patterns that work, and why data is the part that will ruin your week.
Design for Failure or It Will Design Your Weekend
Failure is not an edge case. It is the default state you temporarily hold off with good engineering. A few hard-won rules for building systems that bend instead of shatter.
What Building Distributed Systems at a Fintech Startup Taught Me About Failure
Hard-won lessons from designing distributed systems that survive real-world failures -- timeouts, retries, bulkheads, and the operational habits that actually keep things running.
Why Monitoring Wasn't Enough and How We Built Observability at a Fintech Startup
After a mystery outage that our dashboards couldn't explain, I rebuilt the fintech startup's telemetry stack around metrics, logs, and traces. Here's what I learned.
Event Sourcing in Practice: What I Got Right and Wrong
Lessons from building event-sourced systems at the fintech startup -- the patterns that held up, the modeling mistakes that bit us, and the operational realities nobody warns you about.
Multi-Region Architecture: What I Wish Someone Had Told Me
We serve financial data to users across Europe at the fintech startup. Here's what I've learned about going multi-region -- the patterns that work, the ones that burn you, and when you should even bother.
Monitoring Is Not Enough
Your dashboards look green. Your users say the site is broken. That gap is the whole problem.