Simor

Data Infrastructure for Production AI

Practical writing on AI data engineering, feature stores, and the infrastructure choices that determine whether AI systems work in production.

Vector database showdown: Pinecone, Weaviate, Qdrant, Milvus
Vector database showdown: Pinecone, Weaviate, Qdrant, Milvus
06 May, 2026 | 05 Mins read

Every team building retrieval-augmented generation or semantic search eventually needs a vector database. The market has consolidated around four serious options: Pinecone, Weaviate, Qdrant, and Milvu

From 3-hour dashboards to 3-minute insights: a BI modernization story
From 3-hour dashboards to 3-minute insights: a BI modernization story
05 May, 2026 | 05 Mins read

A manufacturing company with facilities in twelve countries ran its operational reporting on a traditional BI stack: a data warehouse, an ETL pipeline, and a dashboard tool that had been deployed six

What we can learn from the DevOps revolution applied to AI
What we can learn from the DevOps revolution applied to AI
04 May, 2026 | 04 Mins read

In 2009, deploying software to production was an event. It involved a change request, a maintenance window, a runbook, and a prayer. Developers wrote code, then threw it over the wall to operations, w

Build vs buy: a decision tree for AI infrastructure
Build vs buy: a decision tree for AI infrastructure
03 May, 2026 | 06 Mins read

Every AI infrastructure team eventually faces the same argument. One faction wants to build a custom solution because the commercial options do not handle their specific requirements. The other factio

The open-source LLM landscape just shifted — again
The open-source LLM landscape just shifted — again
02 May, 2026 | 03 Mins read

Three releases in the last six weeks have redrawn the open-source LLM map. Meta shipped Llama 4 with a mixture-of-experts architecture that narrows the gap with proprietary frontier models. Mistral re

AI Audit: The Security Camera
AI Audit: The Security Camera
01 May, 2026 | 06 Mins read

A security camera does not stop crimes. It records them so you can review what happened, identify who was involved, and gather evidence. After the fact, the footage becomes valuable for understanding

AI Observability: Monitoring Hallucinations, Latency, and Cost at Scale
AI Observability: Monitoring Hallucinations, Latency, and Cost at Scale
30 Apr, 2026 | 09 Mins read

Traditional software monitoring tracks CPU utilization, memory consumption, request rates, and error counts. These metrics tell you whether your service is running and whether it is handling load. The

When RAG failed: a knowledge retrieval project post-mortem
When RAG failed: a knowledge retrieval project post-mortem
29 Apr, 2026 | 05 Mins read

A legal technology company had invested six months building a retrieval-augmented generation system to help contract attorneys find relevant precedent clauses across a corpus of 180,000 executed agree

Migrating from batch to streaming: a 6-month journey
Migrating from batch to streaming: a 6-month journey
28 Apr, 2026 | 05 Mins read

A logistics company processing two million shipments per day ran their entire operational reporting stack on nightly batch ETL. Every morning at 6 AM, operations managers reviewed dashboards built on