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DECISION FRAMEWORKS

Honest comparisonsfor high-stakes AI choices.

Build vs buy. RAG vs fine-tuning. OpenAI vs Anthropic vs open-source. LangChain vs LlamaIndex. Real analysis from a team that ships production AI systems.

RAG vs Fine-Tuning

Choose RAG when your knowledge changes frequently, when you need source citations, or when you have role-based access controls, which describes the majority of enterprise AI use...

Build vs Buy AI: Enterprise Decision Framework for 2026

Buy when the AI capability is commoditized and not strategic to your differentiation (general-purpose chatbots, off-the-shelf transcription, generic copilots). Build when AI is ...

LangChain vs LangGraph

Use LangGraph for production agent systems where you need explicit state management, human-in-the-loop checkpoints, and reliable multi-step workflows, which describes most produ...

OpenAI vs Anthropic

Both OpenAI (GPT-4o, GPT-5, o-series reasoning models) and Anthropic (Claude 3.5/4 Sonnet, Opus, Haiku) are frontier-class options viable for nearly any production AI workload. ...

Pinecone vs Qdrant

Use Pinecone if you want a managed vector database with zero operational burden, accept vendor lock-in, and operate at small-to-medium scale (under 30M vectors). Use Qdrant if y...

LoRA vs Full Fine-Tuning

Use LoRA (or QLoRA) for 95%+ of production fine-tuning: much better cost-quality trade-off, much smaller infrastructure requirements, easier to manage multiple adapters. Use ful...

Self-Hosted vs Managed LLM

Use managed LLMs (Anthropic API, OpenAI, AWS Bedrock, Vertex AI) for the first 6-18 months of any AI initiative: the operational simplicity is dramatic. Switch to self-hosted (o...

DPO vs RLHF

Use DPO (or its variants ORPO, KTO, SimPO) for 90%+ of preference-tuning use cases: much simpler, much cheaper, comparable results on most tasks. Use full RLHF only when (a) you...

LangGraph vs CrewAI vs AutoGen

Use LangGraph for production agent systems requiring explicit state management, human-in-the-loop checkpoints, and reliable debugging: our default for production work. Use CrewA...

Snowflake vs Databricks

Use Snowflake when your primary use case is analytical SQL workloads with some AI / ML on top, you want operational simplicity, and you're not committed to PySpark. Use Databric...

Fine-Tuning vs Prompt Engineering

Start with prompt engineering for almost every AI use case: it's faster, cheaper, and reaches surprising quality. Reach for fine-tuning when prompt engineering can't reach your ...

Multi-Agent vs Single-Agent Systems

Default to single-agent design for most production AI: simpler to build, debug, and operate. Reach for multi-agent when the problem genuinely requires multiple specialized agent...

Azure OpenAI vs AWS Bedrock

Use Azure OpenAI when you're committed to the Microsoft / Azure stack, want OpenAI models with enterprise BAA / compliance, and have predominantly Microsoft-stack engineering. U...

Open-Source vs Closed-Source LLMs

Use closed-source frontier models (GPT-5, Claude Sonnet / Opus, Gemini 2.5) when you want best-in-class quality without operating infrastructure, accept vendor lock-in, and oper...

Promptfoo vs Braintrust vs LangSmith: Choosing an LLM Eval Tool

Use Promptfoo for prompt-level CI integration with simple eval needs (open-source, free, easy to integrate). Use Braintrust for production trace analysis and dataset curation at...

LangChain vs LlamaIndex

Use LlamaIndex for document-heavy RAG where ingestion / indexing / retrieval depth matters: our default for production RAG over diverse document types. Use LangChain for broader...

AI Agents vs RPA

Use RPA (UiPath, Automation Anywhere, Blue Prism) for high-volume rule-based automation of repetitive structured workflows where the process is well-defined and rarely changes. ...

MLflow vs Weights & Biases

Use MLflow for production model registry, deployment, and lifecycle management: open-source, enterprise-friendly, integrates with Databricks and standard MLOps stacks. Use Weigh...

Semantic vs Hybrid Search

Use hybrid search (semantic + keyword) for almost every production RAG and search use case: combines the meaning understanding of semantic search with the exact-match precision ...

OpenAI vs Cohere vs Voyage

Use OpenAI text-embedding-3 (large or small) for general-purpose production retrieval: strong quality, well-supported, reasonable cost, the default choice for most BearPlex enga...