▸ Category · Architecture
Notes on Architecture.
12 posts in this category.
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ArchitectureScaling with RabbitMQ: why message brokers matter
Synchronous controllers are how monoliths die. RabbitMQ basics, exchanges and queues, the strangler pattern for going async, idempotent workers, and the Laravel queue setup I use to absorb 100k-row spikes without breaking the login page.
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ArchitectureCaching for speed: Redis and semantic layers in RAG
Stop paying for the same LLM call twice. Two-tier caching — exact-match Redis keys plus semantic vector lookups via RedisVL — that cuts RAG latency from seconds to milliseconds and slashes API spend by up to 80%. With tenant isolation, TTL tiers, and the precision metrics that keep it honest.
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ArchitectureScaling on demand: smart auto-scaling for modern AI apps
CPU autoscaling is a lie for GPU workloads. Why queue depth, KV-cache pressure, and TTFT beat CPU as scaling triggers — KEDA-driven patterns, ARIMA forecasting, and composite metrics that scale your AI SaaS before users hit the spinner.
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ArchitectureGPU-aware load balancing: managing AI compute like a pro
Round-robin is a relic when LLM requests span 50 tokens to 50,000. Prefill vs decode disaggregation, KV-cache-aware routing, prefix matching, and the four metrics that matter — how to route AI traffic so your P99 stops bleeding.
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ArchitectureCircuit breakers: preventing cascading failures in your vector DB
A slow vector DB kills SaaS faster than a dead one. The circuit-breaker pattern for AI infrastructure — closed/open/half-open states, fallback tiers, semantic caches, LLM-only mode, and Laravel-friendly wiring to keep production from melting under one bad dependency.
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ArchitectureMessage queues: handling the heavy lifting of document processing
Stop running embeddings inside the request-response cycle. A production-grade document ingestion pipeline — staged workers, exponential backoff, dead-letter quarantines, batched embeddings, and queue-depth autoscaling that keeps your AI app from melting under a 500-page PDF.
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ArchitectureRate limiting: protecting your AI wallet
One runaway agent loop = $5,000 OpenAI bill. Why request-per-second limits lie for LLM apps, how to architect hierarchical token-bucket limits across global / tenant / user layers, and adaptive throttling patterns that protect margins without breaking UX.
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ArchitectureAPI Gateway: the front door of your AI stack
Stop exposing LLM providers directly to the frontend. The gateway pattern for AI apps — JWT-scoped tenant isolation, model aliases, denial-of-wallet rate limiting, streaming-safe timeouts, and the wallet-saving guardrails every senior engineer needs.
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ArchitectureMastering event-driven architecture with Google Pub/Sub
Decouple your services or drown in latency. Topics, fan-out, push vs pull, dead-letter queues, idempotent consumers, and the Laravel integration I run on Google Cloud — a practical EDA blueprint from a senior engineer.
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ArchitectureVibe coding and the architectural shift to agentic workflows
MCP, agentic loops, and intent-based engineering. How vibe coding becomes a real architecture pattern when AI stops being a chat sidebar and starts owning stateful loops against your tools. The practical Laravel + MCP stack I run today.
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ArchitectureFrom monolith to micro-services: a senior dev's guide to pragmatic scaling
Skip the big-bang rewrite. The strangler fig pattern, anti-corruption layers, Docker-first migration, and GKE/Coolify operations — how I peel services off a Laravel monolith one endpoint at a time without breaking revenue.
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ArchitectureAI integration vs traditional development: which is better for your business in 2026?
Speed, control, or a hybrid path? When AI-assisted development pays off, when traditional engineering is non-negotiable, and the hybrid workflow I recommend most often to founders and tech leads.
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