Field reports from the TechKis team.
Engineering notes, architecture deep-dives and honest takes on what we’re learning while shipping AI-first software.
Top highlights
- 11 min read·ArchitectureScalingBackend
Scaling from 10 users to 10 million — the decisions that matter at each stage
Scaling isn't one problem. It's a sequence of different problems that appear at different stages. Here's what actually matters at 10 users, 10,000 users, 100,000 users, and 10 million — and the mistakes that come from solving tomorrow's problem today.
Read post - 10 min read·ArchitectureMicroservicesBackend
Monolith vs Modular Monolith vs Microservices — picking the right shape for your stage
A practical breakdown of when a monolith is the right call, when to modularise it, and when microservices actually earn their operational cost — with the signals that tell you it's time to move.
Read post - 9 min read·LangGraphLangChainOpenAI
LangGraph vs LangChain vs raw OpenAI SDK in 2026 — what we actually pick
An opinionated, code-first comparison of LangGraph, LangChain and the raw OpenAI / Anthropic SDKs for shipping production agents in 2026 — when each one earns its keep, where they bite, and what we default to on new client work.
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22 posts · page 2 of 4
- 10 min read·AI EngineeringArchitectureBackend
How AI changes backend architecture — the parts that are genuinely different
Adding an LLM to your backend breaks assumptions that held for a decade: latency, determinism, cost per request, statefulness. Here's what actually changes and how to design for it.
Read post - 10 min read·AI EngineeringArchitectureEnterprise
The AI gateway pattern for enterprise applications
Letting every service call model providers directly is how enterprises lose control of cost, security and compliance. An AI gateway is the single seam where routing, auth, budgets, logging and guardrails live. Here's how to build one.
Read post - 10 min read·AI EngineeringObservabilityArchitecture
AI observability — logs, traces, cost and hallucinations
You can't run an LLM system on hope. Traditional observability misses the things that actually go wrong with AI: silent quality drift, runaway token cost, and confident wrong answers. Here's the observability stack an AI system needs.
Read post - 9 min read·AI EngineeringCostArchitecture
AI rate limiting and cost control — before the bill surprises you
One buggy loop or abusive user can turn an LLM feature into a five-figure invoice overnight. Rate limiting and cost control for AI aren't the same as for a normal API — here's how to cap spend without breaking the product.
Read post - 11 min read·ArchitectureScalingBackend
Scaling from 10 users to 10 million — the decisions that matter at each stage
Scaling isn't one problem. It's a sequence of different problems that appear at different stages. Here's what actually matters at 10 users, 10,000 users, 100,000 users, and 10 million — and the mistakes that come from solving tomorrow's problem today.
Read post - 8 min read·API DesignArchitectureBackend
API-First Development — why the contract should come before the code
API-First means designing the contract before writing the implementation. Here's why that order matters, what it changes about how teams work, and the practical workflow that makes it work without slowing you down.
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