AiGanakPractical notes on running AI in production.
Guides and comparisons from our audits and builds: cutting LLM costs, choosing models, building agents and keeping them safe. Written by our engineers, with sources.
AiGanak Insights publishes practical guides and comparisons on running AI in production: reducing LLM API costs, choosing between small and large language models, building agent workflows and defending against prompt injection.
How to reduce LLM API costs: nine levers, in the order to apply them
LLM API bills are usually dominated by input tokens. Trim system prompts, retrieve fewer documents, shorten answers, cap agent steps, use structured outputs, cache repeat answers, route simple work to smaller models, turn on provider prompt caching and batch work that can wait, applying each change on top of the last so savings are not counted twice.
Read the article →Comparison · 8 min readSmall vs large language models: how to choose for a business task
Small language models of roughly 0.5 to 7 billion parameters suit narrow, high-volume tasks and private deployment on your own hardware, while large API models remain stronger at open-ended reasoning. Most teams end up routing simple requests to a small model and hard ones to a large model.
Read the article →Comparison · 7 min readn8n vs Make for AI agent workflows: a practical comparison
n8n can be self-hosted, is licensed under its fair-code Sustainable Use License and bills per workflow execution; Make is cloud-only, has over 3,000 integrations and bills credits per action. n8n suits private data and long agent workflows; Make suits fast, broad SaaS automation.
Read the article →Security · 8 min readPrompt injection: how to defend production LLM applications
Prompt injection, LLM01 in the OWASP Top 10 for LLM Applications 2025, lets text in user input or retrieved content override a model's instructions. Defend in layers: least privilege, separating untrusted content, validating outputs, human approval for risky actions, keeping secrets out of prompts, and monitoring, with injection tests on every change.
Read the article →AI glossary
Plain definitions of the terms we use: small language models, prompt caching, model routing, RAG, quantization and more.