What we learn running AI infrastructure, written for engineers who run it too.
Input/output pricing asymmetry, cache-friendly prompt structure, cheap-model cascades, and hard budget enforcement — a practical walkthrough of where LLM spend actually goes and the controls that keep it bounded.
Read post →A stage-by-stage walkthrough of production AI data pipelines: ingestion and parsing tradeoffs, three tiers of deduplication, LLM-powered extraction with schema validation, and PII masking that fails closed.
Read post →Hard errors are the easy part — the failures that hurt are silent degradation and retry storms. A field guide to retry budgets, per-model circuit breakers, and degradation tiers that beat an error page.
Read post →A no-fluff evaluation guide for picking an LLM gateway: streaming and tool-call fidelity, mid-stream failover behavior, per-key limits, token-level cost attribution, and the managed-vs-self-hosted trade.
Read post →The hard part of AI is everything after the demo: reliability, cost visibility, and data quality. We are launching Runix to build that layer — an OpenAI-compatible gateway, managed data pipelines, and ready-to-deploy solutions.
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