AI decision guides

Pick the right AI stack

Straight comparisons across the choices enterprise teams actually face — models, techniques and channels — written by the engineers who ship them.

LLM techniques

RAG vs Fine-tuning

How to choose between retrieval-augmented generation and fine-tuning

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Conversational AI

AI chatbot vs Voice agent

Text-based chatbots vs voice AI agents — which channel fits your workflow?

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Foundation models

GPT (OpenAI) vs Claude (Anthropic)

How OpenAI's GPT models compare to Anthropic's Claude for enterprise workloads

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Frontier LLMs

GPT-5 vs Gemini 2.5 Pro

OpenAI's GPT-5 vs Google's Gemini 2.5 Pro for enterprise

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LLM frameworks

LangChain vs LlamaIndex

LangChain vs LlamaIndex for enterprise RAG and agents

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Vector databases

Pinecone vs pgvector

Managed Pinecone vs Postgres pgvector for enterprise RAG

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Deployment

OpenAI API vs Azure OpenAI

OpenAI direct vs Azure OpenAI for enterprise

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Automation

n8n vs Zapier

n8n vs Zapier for AI-native workflow automation

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Speech-to-text

Whisper vs Deepgram

OpenAI Whisper vs Deepgram for production speech-to-text

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LLM deployment

Self-hosted (Llama / Mistral) vs Managed API (OpenAI / Anthropic)

Self-hosted open-source LLM vs managed frontier API

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LLM techniques

Supervised Fine-tuning (SFT) vs Reinforcement Fine-tuning (RLHF / DPO)

SFT vs RLHF/DPO for enterprise LLM customisation

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Workflow orchestration

n8n vs Temporal

n8n vs Temporal for durable AI workflows

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AI integration

REST / OpenAPI vs MCP (Model Context Protocol)

Exposing enterprise systems to AI: REST vs MCP

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